Jean-François Benoist

Keywords: Separation, identification and quantification of isomeric metabolites by differential mobility spectrometry Metabolomics, isomers, quantification, biomarkers, differential mobility spectrometry

The Proteinogenic amino acids and related compounds are metabolites which can act as sensitive biomarkers of metabolic diseases (MDs). Protocols based on liquid chromatography hyphenated to tandem mass spectrometry (LC-MS/MS) are routinely used for diagnosis at the hospital. These LC-MS/MS protocols for biomarker quantification are particularly robust, but there is a need for alternative and/or complementary methods that are more specific and rapid, and that could resolve specific isobaric or isomeric metabolites, and address multiple classes of metabolites simultaneously.

Our objective was to assess the performance of protocols based on differential mobility spectrometry (DMS) hyphenated to MS/MS for targeted metabolomics. A home-built device has been specifically designed for this purpose, which could be mounted on two of our MS/MS instruments. We were also interested in fundamental aspects of separation and identification of isomeric species using DMS. For this purpose, infrared multiple photon dissociation (IRMPD) was used in DMS-MS/MS(IRMPD) sequences in order to identify DMS peaks and even resolved overlapping peaks. In the latter case, addition of polar molecules in the carrier gas leading to modifier-assisted DMS was found as an alternative for resolving DMS peaks, which are characterized by a compensation voltage (CV) applied to one of the two DMS electrodes.

An important added-value of this work was to show that CV value associated with one metabolite is very stable and could thus be used as an identifier. No matrix, nor concentration effects were found upon the analysis of standard solutions and healthy and MD plasma samples. Using a dedicated R-suite of programs, quantitative data analysis and statistics could be rapidly performed. Quantification results for a set of metabolites using DMS-MS were found to be in excellent agreement with those obtained at the hospital using LC-MS/MS. We even showed that faster protocols based on few selected DMS-MS analysis at specific fixed CV values are also reliable, and could be used, for example, for emergency diagnosis of MSUD.

2 Titre : Séparation, identification et quantification de métabolites isomères par spectrométrie de mobilité différentielle Mots clés : Métabolomique, isomères, quantification, biomarqueurs, spectrométrie de mobilité différentielle Résumé : Les acides aminés protéinogènes et les composés apparentés sont des métabolites qui peuvent servir de biomarqueurs sensibles des maladies métaboliques (MD). Des protocoles basés sur la chromatographie liquide couplée à la spectrométrie de masse en tandem (LC-MS/MS) sont couramment utilisés pour le diagnostic à l'hôpital. Ces protocoles LC-MS/MS pour la quantification des biomarqueurs sont particulièrement robustes, mais il existe un besoin pour des méthodes alternatives et/ou complémentaires plus specifiques et rapides, et qui pourraient résoudre certains métabolites isobares ou isomères spécifiques, et traiter simultanément plusieurs classes de métabolites.

Notre objectif était d'évaluer les performances de protocoles basés sur la spectrométrie de mobilité différentielle (DMS) couplée à la MS/MS pour la métabolomique ciblée. Un dispositif DMS a été spécialement conçu à cet effet, qui a pu être monté sur deux de nos instruments MS/MS. Nous nous sommes également intéressés aux aspects fondamentaux de la séparation et de l'identification des espèces isomériques à l'aide du DMS. À cette fin, la méthode dite Infrared Multiple Photon dissociation (IRMPD) a été utilisée dans les séquences DMS-MS/MS(IRMPD) afin d'identifier les pics de DMS et même de résoudre les pics se chevauchant. Dans ce dernier cas, l'ajout de molécules polaires dans le gaz porteur conduisant à la modifier-assisted-DMS s'est révélée être une alternative pour résoudre les pics de DMS, qui sont caractérisés par une tension de compensation (CV) appliquée à l'une des deux électrodes de DMS.

Une importante valeur ajoutée de ce travail a été de montrer que la valeur de CV associée à un métabolite est très stable et peut donc être utilisée comme identifiant. Aucun effet de matrice ou de concentration n'a été constaté lors de l'analyse des solutions standard et des échantillons de plasma sain ou de patients atteints de MD. Grâce à une suite de programmes R dédiés, l'analyse quantitative et statistique des données a pu être faite rapidement. Les résultats de quantification d'un ensemble de métabolites par DMS-MS se sont révélés en excellent accord avec ceux obtenus à l'hôpital par LC-MS/MS. Nous avons même montré que des protocoles plus rapides basés sur quelques analyses DMS-MS sélectionnées à des valeurs CV fixes spécifiques de métabolites cibles sont également fiables, et pourraient être utilisés, par exemple, pour le diagnostic d'urgence du MSUD.

ACKNOWLEDGEMENTS

So glad I finally got here. It's been a long, long journey. The past three years have been a bit unbelievable. I feel lucky to be able to finish my PhD on time after the COVID-19 pandemic.

First of all, I would like to thank Dr. Philippe Maître for his supervision, support and patience these years. I have to admit that I took too much patience from him, especially when I was silent. Thank him for his tolerance and understanding in work. He is a wise elder full of wisdom in my eyes. Thank him for teaching me not only knowledge but also how to deal with people.

I would like to thank my thesis committee Jean-Yves Salpin, thanks for his patient follow and guidance every year when I do inscription. I would like to thank Jean-François Benoist, thank for his advice and help for my work, his clinical experience provided us with many useful information. I would like to sincerely thank Héloïse Dossmann, Sandra Alves, François Fenaille, Didier Thiébaut for accepting my invitation to be my defense juries and reviewing my work. Thank them for keeping time for my defense.

I would like to thank Francis Berthias, he helped me and also taught me a lot when I first came to the lab. He is an excellent and very nice person. Thank him for Chinese movie recommendation, I will remember Chungking Express and remember our friendship. I would like to thank Eskander Alhajji. Thank him for working with me over these years. I will never forget we used to be good partners.

I would like to thank Bernard Rieul, thanks for his kindness and technical support whenever we meet problems at work. I would like to thank Pascal Pernot, thanks for his help for data analysis that saved us a lot of time. I would like to thank all colleagues in Capri group, Estelle Loire, Frédéric Halgand, Guillaume van der Rest. Thanks for their kind help and advice over these years.

Thank my family for their firm support and understanding. They always stand with me so I can pursue whatever I want with no worries even if I leave far away from home. I know life is not easy, but I have enough courage to face it, with their support and love.

I appreciate all my friends I met in ICP, Yining, Chiraz, Rika, Jian, Xiaojiao, Cong, Qian, Jingwei. Thank my friends, Ting, Pierre, Miaobo, Linghua, Bo, Xiaowen. Thanks for their accompaniment and encouragement along the way that makes my PhD life happier and easier. I will never forget all those happy memories together.

I would like to thank China Scholarship Council (CSC) for the funding support of my work and life in France. Thanks to my motherland.

I am grateful for the happiness, success, confusion, sadness, stress I have experienced these years. It was these experiences that made me a more mature, wiser person. I am grateful for everything I have experienced here. They make me have a new view of the world and understand more about life.

I will remember the time, remember love. Les deux principaux obstacles à la découverte de biomarqueurs sont l'identification des La quantification des métabolites isomères, même lorsqu'ils ne sont pas entièrement résolus, s'avère très bonne.

CHAPTER Ⅰ. INTRODUCTION TO THE APPLICATION OF ION MOBILITY SPECTROMETRY IN METABOLOMICS 1.1 METABOLITES IDENTIFICATION AND BIOMARKERS VALIDATION

Metabolomics is the large-scale study of metabolites involved in cell function and metabolic pathways. It contributes to a more comprehensive understanding of the biochemical characterization of metabolites. 1,2 As an important tool for understanding the biological process of disease and the screening of biomarkers, the contribution of metabolomics has been greatly emphasized in recent years. 2 In this work, we were interested in evaluating a new way of screening biomarkers associated with inherited metabolic disorders (MD). Also called inborn errors of metabolism, MDs are caused by genetic defects and can be diagnosed through quantitative measurement of targeted metabolites 3 in biological fluids.

Metabolomics is the endpoint of omics cascade in biological systems as illustrated in Figure 1, where a gene mutation induces an enzyme deficiency, both being upstream pathways. Metabolic pathway diversion is one of the ways cells regulate metabolism.

Dysregulated metabolic pathways can thus lead to diseases. 4 As a substrate or product of enzymes, metabolites are sensitive to changes in related enzyme activity levels. As illustrated in Figure 1, amino acids (AAs) and acylcarnitines are typical metabolites that can be taken as biomarker candidates. Metabolites are small molecules with a molar mass <1500 Da present in biological fluids that are involved in metabolic reactions. The large number of metabolic pathways and corresponding metabolites are at the origin of the analytical challenge. 7 About 2,000 metabolites are currently included in metabolic pathway maps. 8 In order to use metabolites as diagnostic or predictive biomarkers, a prerequisite is to identify their roles in metabolic pathways. These metabolites are chemically diverse, and can be carbohydrates, AAs, acylcarnitines, lipids, nucleotides; …. 9 AAs, acylcarnitines and organic acids are commonly targeted molecules for many diseases. 10 Nowadays, the screening of biomarkers allows for an early diagnosis of many diseases, 11,12 and can also answer the need for emergency diagnosis. 13 Metabolites identification provides biological interpretation for metabolic diseases. 14 Two complementary approaches are used for biomarkers discovery in metabolomics analysis: untargeted and targeted. 7 Untargeted approach involves determining the difference between samples to select biomarker candidates. Metabolic profile and fingerprint approaches provide data for biomarker selection. Then targeted metabolomics is used to identify and quantify the selected metabolites. The biochemical properties of biomarkers are explained and validated based on the knowledge of the metabolic pathways. 10 The two biggest hurdles to biomarker discovery are metabolite identification and biomarker validation. 8 After validation, biomarkers can be used for clinical diagnosis. An ideal biomarker should be specific of a given disease and easily separable from isobaric and isomeric compounds to be quantified.

BLOOD SAMPLES

This work focuses on targeted metabolomics, and blood samples were analysed. Blood is one of the most commonly used bio-fluids in clinical metabolomics since it irrigates all the organs and can be easily sampled and prepared. Blood is a complex matrix where small molecules such as metabolites as well as macromolecular proteins and nucleic acids can be found. 15 Metabolic profiling in blood samples give insight into metabolic pathway and provide pathological evidence for metabolic diseases by biomarkers screening. 10,15 In this work, our targeted analyses focus on two types of metabolites in plasma samples, amino acids and related compounds that carry biological information of aminoacidopathies. 16 For example, phenylketonuria is a rare inborn error of metabolism, for which the concentration of phenylalanine in blood is used as the primary screening parameter. 17 Acylcarinitines were also envisaged, but only preliminary results are briefly discussed. Acylcarnitines are essential for lipids and amino acids metabolism and are associated with a wide range of diseases. These molecules have long been used as biomarkers for disorders of fatty acid oxidation because they are intermediate oxidative metabolites. 18,19 Acylcarnitines profiling in plasma, serum or dried blood spots (DBS) samples are applied for metabolic disorders diagnosis, 5,20 DBS is an interesting alternative bio-fluids for metabolites profiles. 21

COMMON ANALYTICAL TECHNIQUES FOR METABOLOMICS STUDIES

Separation and characterization of metabolites rely on a suite of separation and detection techniques. There are many robust analytical tools available for measuring metabolites, 2, 7, 22, 23 such as nuclear magnetic resonance (NMR) and mass spectrometry (MS). The main advantages of MS are its sensitivity and its high throughput. 23 MS-based metabolomics allow for simultaneous detection of a large number of metabolites in complex biological samples. 24 MS fitted with electron impact (EI) source coupled with gas chromatography (GC-MS) and Orbitrap-based instruments that improves the detection of metabolites between isobaric molecules. 25 Hyphenated MS techniques are routinely used for clinical screening and diagnosis. MS coupled with liquid chromatography (LC-MS), gas chromatography (GC-MS) and capillary electrophoresis (CE-MS), 6 in particular, are the separation methods routinely used for comprehensive metabolomics. Metabolomics profiling based on chromatographic separation coupled with tandem mass spectrometry (MS/MS) is considered to be a method of choice for the identification and quantification of metabolites. 4,10 Fragmentation mass spectra obtained through MS/MS are routinely used for metabolite identification. 22,23 In addition, chromatographic retention time can also be used for characterization. 22 Improvements in MS and chromatography instruments have expanded MS combined with chromatographic separation. 22 Nevertheless, chromatographic-based separation is not without its drawbacks. 4,22,23,26 GC-MS/MS can only be used for volatiles metabolites. As a result, derivatization of most metabolites, which are polar molecules, is required. 26 High resolution can be obtained recent years, ion mobility spectrometry (IMS) could be an alternative for separating and identifying small molecules. 29 The most important benefit of IMS is the possibility to improve the separation of isobaric and isomeric compounds. IMS has been increasingly employed in metabolomics studies, in particular, due to its separation ability and fast screening capabilities. 32 Both time-and space-type IMS separation, 33 such as drift-time IMS (DTIMS) and differential mobility spectrometry (DMS), respectively, have been successfully used for the separation of metabolites. 34 Over the past few years, the popularity of this technique and its application in metabolomics have been boosted by the commercialization of integrated IMS-MS platforms based on DT-IMS, 35 Traveling Wave IMS (TWIMS), 36,37 Trapped IMS (TIMS). 38 DT-IMS coupled to high resolution time-of-flight MS has been employed for the profiling of human blood metabolome, and separation of 300 isomeric metabolites has been achieved. 35 An interesting aspect of IMS is that its short time separation (millisecond) compared to that of LC allows for coupling IMS-MS/MS instruments to chromatographic separation. For example, an LC-IMS-MS/MS protocol has been recently proposed for metabolomics and lipidomics. 39 In this work, ion mobility based on space-separation has been used. DMS approaches have been shown to be more orthogonal to MS/MS than DTIMS or related timeseparation methods, for example, in the case of peptides. 40,41 Coupled to MS/MS, it was shown that the coverage achieved with DMS is comparable to that achieved with chromatographic methods. 42 In addition, DMS has also been proved to allow for a reduction of the number of false-positive assignments. 43 DMS hyphenated to MS/MS has thus been used to analyze complex biological fluids such as urine and plasma, especially for separating co-eluting isomers. [44][45][46] It has been shown

to be effective in cleaning the baseline raising the signal-to-noise ratio in complex matrices, and ultimately facilitating the quantification of analytes of interest. 47,48 The advantages of DMS-MS/MS workflows, especially for the deconvolution of mass spectra from isomeric and isobaric species, have been discussed in the case of lipid analysis. 49 IMS method could be an alternative and/or a complement to chromatography for the separation step of metabolites, 33,50 which favors its application for complex biological samples analysis by adding a separation dimension. 51 A very promising aspect of IMS is that it allows for identification of ionic species. Using time-separation IMS, collision cross section (CCS) of metabolites, which is an intrinsic physical feature, can be determined. 52

THE MAIN PURPOSE OF THIS WORK

Chapter II is dedicated to the presentation of the experimental methods used during my PhD work. The main features of interest of the two tandem mass spectrometers used in the context of this work are also given. Our motivations were twofold. We were interested in using differential mobility spectrometry (DMS) to separate isobaric and isomeric metabolites. Identification of metabolites using DMS was our second goal. For this purpose, we focused on the robustness of DMS-MS protocols. In this context, we discuss the different factors influencing DMS peak position.

After a brief introduction to the core principles of ion mobility spectrometry, the working principle of DMS is introduced. We highlight the simplicity of the introduction into an electrospray ionization (ESI) source, and the possibility of turning it off (transparent mode)

to optimize ESI conditions. The principle of space separation of ions using an asymmetric radiofrequency voltage supply is described. Without going into the physico-chemical details, we insist on the importance of the nature of the carrier gas, which allows for a strong differential mobility between low and high electric fields. Examples of DMS spectra illustrating isobar and isomer separation are given. Mechanical details of DMS are given, especially those allowing for a control of the DMS resolution.

Chapter III is dedicated to the coupling of DMS and Infrared multiple photon dissociation (IRMPD). The motivation was two-fold. First, our group has been developing and applying

IRMPD spectroscopy for about twenty years to identify isomeric ions, and some limits have been reached. The idea was to show that DMS and IRMPD could be combined to identify isomeric ions using two different protocols. Second, this work was also the first one using the coupling of DMS with our high resolution Fourier transform ion cyclotron resonance (FT-ICR) MS/MS instrument.

Separation and identification of three isomers (,  and ) of aminobutyric acids were investigated, and two DMS-MS/MS-IRMPD protocols were used. First, IRMPD spectroscopy of DMS-and mass-selected isomers was performed. For this purpose, the CV value was set in order to transmit a single targeted isomer. In the case of ABA isomers, this was only possible using the so-called modifier-assisted DMS, i.e. by introducing a polar molecule into the N2 carrier gas. This protocol can be designated by DMS(fixed)-MS/MS-IRMPD(scan).

The second protocol can be designated by DMS(scan)-MS/MS-IRMPD(fixed). We show that by selecting the IR wavelength, specific depletion of DMS peaks can be achieved, thereby allowing for DMS peak identification. IR selection is based on calculated absorption spectra, or IRMPD spectroscopy of the three isomers. Specific ion depletion is obtained when the wavelength corresponds to a specific absorption band of the targeted isomer. We show in particular that identifying the two components of an unresolved DMS peak can be achieved.

Identification and separation issues using DMS are illustrated and discussed in chapter IV. Common amino acids (AAs) and related compounds are our targeted compounds. When available, DT-IMS data are compared to DMS ones, with a special focus on isobars and isomers separation. One of the main outputs of this chapter is the high robustness of our DMS-MS/MS instrument. CV values measured over a long period of time are shown to be highly reproducible. This opens the door to using CV value as an additional identifier of metabolites.

Chapter V is devoted to the quantification results obtained for amino acids and related compounds using our DMS-MS platform. Our objective is to show that DMS-MS could be reliable for clinical diagnosis. We thus compare our performance with those obtained with the same plasma samples analyzed by LC-MS/MS at the hospital in the frame of daily metabolic disease (MD) diagnosis, performed by the group of J.-F. Benoist.

Isotopically labeled compounds were used as internal standards, and details on the calibration curve procedures and determination of the limit of quantification are given.

One key point of this chapter was the use of a specifically written R suite of programs by P. Pernot. Using this program, DMS peak areas can be calculated assuming a twodimensional (2D) Gaussian profile, and statistics can be easily carried out. This R program allows for different constrains to be applied. For example, 1D peak integration can be performed by fixing one (CV) or the other (m/z) variable.

An original added-value developed in this chapter is the possibility of quantification based on DMS-MS spectra recorded only at the specific CV values of the targeted metabolites.

This fast DMS(fixed)-MS approach for quantification was found to be as accurate as the one based on DMS(scan)-DMS. We first evaluate the possibility of such fast quantification at fixed CV using numerical simulation. Then, quantification results obtained by performing dedicated fixed-CV experiments are presented.

Chapter VI focuses on the separation, identification and quantitative analysis of isomeric and isobaric metabolites by DMS-MS. We were primarily interested in the MSUD, for which allo-isoleucine (aIle) is a specific biomarker. We show that while aIle cannot be separated from its isomers, it can be easily separated from its isobaric compounds, i.e.

hydroxyproline (OH-Pro). This is an interesting perspective for using DMS coupled with a low resolution MS instrument for MSUD screening. Quantification of isomeric metabolites, even when they are not fully resolved, is found to be very good.

CHAPTER Ⅱ. ION MOBILITY SPECTROMETRY METHODOLOGY 2.1 INTRODUCTION TO ION MOBILITY SPECTROMETRY

Core principles of ion mobility spectrometry (IMS) devices have been discussed in different reviews. 1,2 IMS has been increasingly used for analytical purposes in recent years, and different instrumental platforms based on IMS have been proposed. 3,4 However all these instruments are based on the same physical ground: when an ion moves in the gas phase under the influence of an electric field, its velocity depends on its size, shape, and charge state. As a result, IMS could be used for the separation of analyte ions and also for their identification.

The relationship between ion velocity and ion shape can be exploited for isomeric separation as illustrated in Figure 1. Typically, the IMS instrument is composed of an ion source, an IMS cell, and an ion detector. 2 In Figure 1, isomeric ions separation is illustrated using the most well-known IMS instrument which is a drift tube IMS instrument (DT-IMS). Spectrum on the right side is plotted using ion count as a function of drift time.

Chapter Ⅱ. Ion Mobility Spectrometry Methodology

39

Two isomeric ions with different shapes are represented in blue. These ions are entrained by the electric field which is constant along the drift tube axis. Ions traverse the drift tube which is pressurized with a buffer gas such as nitrogen or a rare gas. Hence, ions experience collisions with the gas. Isomeric separation is based on the fact that the most compact ion experiences less collisions with the gas than the extended one. As a result, the former hits the ion detector before the latter, and ions can thus be separated as illustrated in Figure 1. From the measurement of the drift time, the collision cross section (CCS), which is a physical property of the ion, can be derived as discussed below.

td. The time that it takes for an ion to go across the drift tube is referred to drift time (td).

Since the electric field is constant within the IMS cell, there is a simple relationship between td, the length of the drift tube (L, cm) and the ion velocity (V, cm/s):

𝑉 = 𝐿 𝑡 𝑑 (1)
The relationship between the velocity of the ion and the electric field (E, V/cm) is given in equation 2. In this equation, K(E) (cm 2 /V) is the mobility which depends upon the electric field strength as given in equation 3, where K(0) is a mobility constant parameter under zero field condition and alpha α(E) is the specific coefficient of electric field strength.

𝑉 = 𝐾(𝐸) × 𝐸 (2)
𝐾(𝐸) = 𝐾(0) × (1 + 𝛼(𝐸)) (3)
The mobility coefficient K(E) can be express as a function of L, td and E as K(E) = L/(td×E).

Below an electric field threshold value, 5,6 the mobility coefficient is found to be constant.

In other words, α(E) is negligible and can be assumed to be equal to 0. Under such low electric field conditions, ion mobility coefficient K(E) is independent of the electric field strength. 7 Thus, K(E) reduces to a constant value K(0).

Chapter Ⅱ. Ion Mobility Spectrometry Methodology 40

The electric field threshold is on the order of 245 kV/m. It is in fact more proper to use the townsend (symbol Td) unit, which is the physical unit of the reduced electric field (ratio E/N), where N is the density of gas particles. 1 Td is equal to 10 -21 V.m 2 , and it corresponds to an electric field value of 24.5 kV/m under 1 atmosphere and 298 K conditions. The electric field threshold value below which the mobility coefficient can be considered to be constant is on the order of 10 Td.

Based on the Mason-Schamp equation, 8 the K(0) coefficient is related to the collision cross-section value (CCS or Ω). Ω is the momentum transfer collision integral associated with the collision between the ion and the buffer gas. Ω provides direct information about the conformation of the ion traveling through the drift region. 9 As shown in equation 4, the ion mobility coefficient scales with 1/Ω.

𝐾(0) = 3 16 × ( 2𝜋 𝜇 × 𝑘 𝐵 × 𝑇 ) 1 2 𝑧𝑒 𝑁 0 × 𝛺 (4)
In this formula, e is the elementary electric charge (1.602×10 -19 C), and z is the charge number of the analyte ion. kB is the Boltzman constant that is equal to 1.38×10 -23 J/K. T is the temperature (Kelvin). N is the buffer gas density number (m -3 ). μ is the reduced mass of the ion (M) and colliding gas (m), that can be calculated by equation: μ=mM/(M+m).

Combining equation 1 and 2, CCS values can thus be directly derived from the measured drift time using a drift tube IMS instrument, or derived after calibration from other IMS instruments, such as traveling wave IMS.

IMS provides a powerful analytical technology for analyte ions annotation and separation, attributed to its fast screening capability and high sensitivity. 2 In particular, the potential of IMS for separation of unresolved isobaric and isomeric species has been explored. 10 It has been demonstrated that the application of IMS hyphenated to mass spectrometry (MS) instruments for ions detection can help to increase peak capacity, reduce background, and improve instrument resolution. 11 In particular, IMS-based methods have been successfully used for isomeric metabolites separations and signal filtering for targeted metabolomics in recent years. 12 Furthermore, derived CCS values have been suggested to be used as an additional annotation for metabolites identification. 3 But challenges still remain for routine incorporation of mobility analysis and CCS information into untargeted workflows. 13

WORKING PRINCIPLE OF DIFFERENTIAL MOBILITY SPECTROMETRY (DMS)

Differential mobility spectrometry (DMS) is a type of IMS method based on ion mobility differences under low and high electric field. 14 DMS allows for space separation of the ions, and the working principle can be found, for example, in a textbook 15 and a review. 12 My project is based on our home-built DMS device that has been successfully used for the separation and identification of isomers of alanine 16 and aminobutyric acids. 17 The main part of DMS is composed of two parallel electrodes as shown in Figure 2. The DMS device is inserted between the needle of the electrospray ion source and the glass transfer capillary of mass spectrometry. Ions are dragged by a carrier gas flow between the two electrodes, from atmospheric pressure to the low pressure region of MS. 12 The high voltage required for the ESI process is applied to the ESI needle, and the glass capillary entrance is grounded.

If the two electrodes are both grounded, ions drift axially between two electrodes and are transmitted to the mass spectrometer. This mode of operation is thus called "DMS off" or "transparent" mode. In DMS off mode, the ion transmission is nearly identical to that observed upon standard electrospray conditions. As a result, the DMS device does not need to be removed from the instrument when not in use. The DMS off mode is also very useful for signal optimization prior to DMS operation. Three ions all pass between the two electrodes, then through the glass capillary transfer to the mass spectrometer.

In practice, one DMS electrode is grounded, and the other is connected to an asymmetric radio frequency high voltage generator. As a result, an alternative high and low electric field which is orthogonal to the gas flow stream is produced. This high electric field thus induces a transverse displacement of ions, and this is this transverse mobility which is at play. 14 The trajectories of ions exhibited as a saw-tooth curve that alternate between high and low electric field as shown in Figure 3. During each cycle, ions experience high electric field for a short period of time and are directed toward one electrode. During the longer period of low electric field, ions are displaces toward the other electrode. 18 In Figure 3, it is assumed that positive ions are generated. The DMS device, with similar settings, can be used with negative ions. For switching from positive to negative ESI mode, one only has to change the polarity of the high voltage applied to the ESI needle. SV. The maximum amplitude of the asymmetric waveform is referred to separation voltage (SV), also known as dispersion voltage (DV). 19 As its name indicates, the separation of ions increases with the SV. At high voltage values, however, arcing may occur, leading to the formation of a deposit on the surface of the electrodes. In practice, when using a DMS device with a gap of 0.7 mm between the two electrodes, we found that a DV value of 1.8 kV corresponds to the best trade-off.

RF. The asymmetric radiofrequency (RF) is generated by combining two sinusoidal waveforms, as shown in Figure 4. One sinusoidal waveform has amplitude of 1.8 kV and a frequency of 1.7 MHz, the second sinusoidal waveform at double frequency (3.4 MHz) is phase shifted by approximately 90°, with 50% of the amplitude of the first sinusoidal waveform. 20 The combination of these two harmonic waveforms result an asymmetric waveform with frequency of 1.7 MHz. MHz and intensity of 1.8 kV (RF1), orange curve is for the harmonic sinusoidal waveform with frequency of 3.4 MHz and intensity of 0.9 kV (RF2), gray curve is resulting RF1+RF2 asymmetric waveform at 1.7 MHz.

DMS SPECTRUM

To be transmitted to the MS/MS instrument, ions have to go through the DMS device.

This can be achieved if the two transverse displacements at low and high electric field compensate each other. If it is not the case, and in order to offset the migration of ions, a direct current voltage called compensation voltage (CV) is applied to one electrode.

Scanned CV. By continuously scanning the CV value, all ions are able to successively pass through the DMS device before entering the mass spectrometer. The DMS spectrum can be obtained by providing a CV ramp from negative to positive as illustrated in Figure 5. couples of values are also reported by the operator, and in particular the starting time (t0)

and corresponding CV0 values. A constant increment of 0.1 V is used during the CV ramp.

Note that CV does not necessarily vary linearly with time when the Ion Charge Control (ICC) mode is used for the MS/MS operations.

Fixed CV. DMS system can also be operated in fixed CV mode. Transmission of ions of interest can be achieved by adjusting CV to a specific value corresponding to maximum transmission to the mass spectrometer. The analysis time can be reduced by recording only a single MS/MS spectrum at fixed CV specific of one isomer, for example.

Under a low electric field, typically below a threshold of the order of ~70 Td, ion mobility coefficient is not affected by electric field strength. This have been reported, for instance, by Schneider and coworkers in a study of 36 compounds. 21 At higher electric field values, however, three types of ion behaviors were observed in this assay. These three types of ions, often referred to as A, B, or C ions, were also observed for AAs under the present DMS conditions. 22 Under high electric field, the alpha coefficient (E), calculated based on dispersion plot data sets, were extracted. 12,21,23 These experiments illustrate the fact that the mobility coefficient K(E) varies non linearly with the electric field strength.

A schematic plot of the three types of alpha coefficient versus electric field (E/N, the electric field strength is normalized to the gas density) is given in Figure 6. in Townsends (Td). 12 Type A ion behavior is generally interpreted assuming that inelastic collisions occur between ions and the buffer gas, even leading to strong ion-molecule clustering effect. 21,24 This hypothesis is supported by numerous experiments based on a carrier gas containing polar molecules at increasing concentration in the N2 buffer gas. 5,25,26 The hypothesis is that ion-molecule clusters are formed under the low electric field, and that declustering may occur under high electric field each RF cycle. Indeed, the higher is the electric field, the higher is the collision frequency between ions and the carrier gas atoms or molecules, and the larger is the energy associated with this collision.

This clustering-declustering effect is illustrated in Figure 7. Under low electric field, few polar or even dinitrogen molecules may be bound to the ion. When increasing the electric field strength, however, declustering occurs due to the high frequency and high energy collisions of this cluster with the buffer gas. As a result, the effective collision cross section of the ion is larger under low than under high electric field.

When travelling through the DMS cell, type A ions are involved in clustering and declustering reactions during each RF cycle, i.e. at a timescale of one microsecond. 25,27 For type C ions, the mobility decreases with electric field strength. 12 This behavior is interpreted assuming that these ions undergo elastic collision with buffer gas atoms, thus behaving as hard sphere. 21 In other words, no ion-molecule cluster formation is expected for these ions. 32 As a result, the plot of the mobility as a function of the electric field strength has negative slope. Typically, large (m/z above 300) and flexible ions, such as peptides and protein ions belong to Type C ions. 33,34 Observation of type B ions illustrate the fact that there is a continuum between type A and type C behavior. 35 Relative to type A ions, type B ions are associated to more weakly bound clustering. 24 Above an electric field threshold, the energetics of ion-molecule collisions are high enough so that no ion-molecule cluster can be formed. Hopkins and coworkers suggested that the continuum between types A and B ions can be correlated to ion-molecule binding energy. 34 The three types of ion behavior as a function of the electric field strength have been characterized experimentally. For example, experiments were performed on ketones in air at different moisture levels by Krylova et al. 6,26,31 They compared alpha plots for ketones from acetone to decanone, and both protonated monomers (MH + ) and proton bound dimers (M2H + ) were studied. Protonated monomers were found to have positive alpha functions. The explanation was that protonated monomers are susceptible to the formation of hydrated ions since their positive charge is accessible. Proton bound dimers showed negative alpha functions. That is because the positive charge of the dimers is not easily accessible to the water molecules. 26 Type B behaviors are often observed for large protonated monomers, or small protonated dimers. 26 Improved separation of small isobaric or isomeric molecules is one critical strength of DMS. In my research, DMS is utilized to separate and identify isobars and isomers of small metabolites such as amino acids, acylcarnitines, and related compounds. For example, as illustrated in Figure 8, two protonated isobars, glutamine (Gln) and lysine (Lys), cannot be separated using a low mass resolution instrument such as our 3D quadrupole ion trap mass spectrometer. Gln and Lys can be baseline separated under our standard DMS conditions, i.e. using N2 as a carrier gas and 1.8 kV as dispersion voltage, as can be observed in Figure 9. CV values for protonated Gln and Lys isobars were found to be -2.8 V and -0.3 V, respectively, which corresponds to a peak-to-peak resolution of 1.8. Using a TWIMS instrument, these two isobars have also been separated, and CCS values of protonated Gln and Lys by Paglia et al 36 were reported to be 124 and 127 Å, respectively, assuming that the FWHM of TWIMS

Gln + Lys

Chapter Ⅱ. Ion Mobility Spectrometry Methodology 50 peak is on the order of 1%, the peak-to-peak resolution is on the order of 1.4 that is smaller than obtained by DMS. 9

HOME-BUILT DMS DEVICE

Our home built DMS device is mounted between the electrospray needle and the entrance of the glass transfer capillary of the mass spectrometer. As shown in Figure 10, a specific source chamber was constructed to replace the original door of mass spectrometer. First, a spacer had to be added in order to fit the DMS device. Since ion mobility is very sensitive to the composition of carrier gas, our custom built chamber allows for the adjustment of the position of the ESI nebulizer needle with respect to the entrance of the DMS device.

This optimal position corresponds to the most positive CV value for a set of protonated AAs. When the ESI needle is closer to the DMS cell, negative CV shifts were observed, which were interpreted as being induced by ESI solvent molecules acting as modifier.

Identical positive ESI conditions have been used throughout. The voltage of the ESI emitter was set to 4.5 kV and the entrance glass transfer capillary was grounded. 20 The picture on the right shows the two electrodes.

An NIGen LC-MS 40 -1 (Claind, Tremezzina, Italy) N2 generator was used to produce the N2 gas with a purity up to 99.99%. The desolvation gas N2 of the Bruker instrument is delivered through holes surrounding the glass capillary transfer. It is redirected through the outer housing (insulating white part in the left picture of Figure 11). The carrier gas flow inside the DMS cell is shown with dotted lines in Figure 12. Based on our measurement using a thermal camera, there is a direct correlation between the temperature of the carrier gas and that of the DMS cell. Part of this N2 flow goes through the hole of the spray shield and acts as a desolvating gas. The other part goes through DMS device between the two electrodes under the effect of the MS/MS pumping system.

Ions are thus transported from atmospheric pressure to low pressure (~10 -3 mbar)

Chapter Ⅱ. Ion Mobility Spectrometry Methodology 52 through the glass transfer capillary. This gas flow has been measured to be ~0.9 l.min -1 .

In order to enhance the transmission of ions, the standard glass transfer capillary has been replaced with a custom flared glass transfer capillary. 37,38 In our DMS-MS system, the carrier gas flow was measured as 0.9 l.min -1 . The size of electrodes is 6×20 mm for width, and length, respectively, and the gap between two electrodes was 0.7 mm. Gas velocity in DMS cell can be calculated on the basis of transport gas flow rate in the DMS cell and cross section area of DMS cell, which can be presented in equation below:

𝑁 2 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑖𝑛 𝐷𝑀𝑆 𝑐𝑒𝑙𝑙 (𝑉) = 𝐺𝑎𝑠 𝑓𝑙𝑜𝑤 𝐺𝑎𝑝 × 𝑊𝑖𝑑𝑡ℎ = 1 × (100 𝑚𝑚) 3 60 𝑠 × (0.7 × 6 (𝑚𝑚) 2 ) ≈ 4×10 3 𝑚𝑚/𝑠
Assuming that ion velocity is the one of the gas molecules, the residence time of ions (tR) can be calculated according to the length of electrode length and gas velocity, which turns out to be (20 mm)/(4×10 3 mm/s) =5 ms.

The RF frequency (f) is set to 1.7 MHz, and the cycle period (T) is thus 0.6 ps. The number of oscillations of each ion in the DMS cell is thus on the order of 8.5×10 3 . Assuming that the amplitude of the oscillation is on the order of 0.1 mm, the total drift path length is on the order of 1 meter.

The coupling of DMS to MS/MS analysis has great potential for the analysis of small molecular species, especially when isobars and/or isomers are at play. DMS is interesting because it is more orthogonal to MS than conventional IMS. 39,40 A DMS device can be easily added to any commercial MS/MS instrument without any mechanical or electronic changes. 35 The combination of DMS separation and MS/MS allows for an additional separation step without compromising the speed of MS/MS operation. The DMS device has advantage of being compatible with different mass spectrometers. 35 We have successfully hyphenated DMS to two MS/MS spectrometers, namely a Fourier transform ion cyclotron resonance (FT-ICR) tandem mass spectrometer and a quadrupole ion trap MS/MS spectrometer. FT-ICR mass spectrometer determines the mass-to-charge ratio (m/z) of ions based on their specific cyclotron frequencies which is measured within the 7T magnet. This cyclotron motion ensures for the trapping in the plane perpendicular to the magnetic field axis. Ions are also trapped along the magnet axis between two trapping plates at a voltage on the order of 5-10 V.

The principle of m/z measurement using an FT-ICR is illustrated in Figure 14. By applying a broadband radiofrequency voltage on a pair of excitation electrodes, ions are excited to a larger cyclotron radius in a plane orthogonal to the magnetic field axis. After the excitation field is removed, ions are rotating at their cyclotron frequency in phase, as a "packet" of ions. As the packets of ions pass close to the detector plates, an image current can be recorded. Using a Fourier transformation, current intensity at each frequency can be extracted. In the simplest idealized form, the relationship between cyclotron frequency and m/z is given by the formula f=qB/2πm, where f is the cyclotron frequency, q is the ion charge, B is the magnetic field strength, and m is the ion mass. Bruker Esquire quadrupole ion trap. DMS device was threaded to match the spray shield of an Esquire 3000+ quadrupole ion trap mass spectrometer (Bruker-Daltonics, Bremen, Germany). The spray shield can be removed and the DMS assembly can be screwed in its place. The setup is based on the one developed by Glish and coworkers. 41 It is important to stress that the core of the DMS device, i.e. the two electrodes and their housing, is exactly the same our two DMS devices. Only the external housings are slightly different due to mechanical constrains. A schematic of the DMS hyphenated to quadrupole ion trap mass spectrometer is illustrated in Figure 15.

OPTIMIZATION OF IONS RESOLUTION IN DMS-MS EXPERIMENTS

DMS is a space-type ion separation technique, and the ion resolution can be improved by increasing the residence time of the ions in electric field. 42 As introduced above, this residence time is controlled by the gas flow between the DMS plates. Extending the lengths of the DMS plates could thus be envisaged. An alternative describe in the literature is to introduce a throttle gas between the DMS electrode and the glass capillary transfer in order to reduce the gas flow. Introducing throttle gas. The DMS resolution can be improved by increasing the residence time of the ions between the two DMS electrodes. Since it can be assumed that the ion velocity is that of the buffer gas between the electrodes, the idea is to reduce the gas flow. This can be achieved by introducing a so-called "throttle gas". 42 In practice, a controlled N2 flow was introduced between the DMS electrodes and the glass capillary transfer. For this purpose, as shown in Figure 16, two gas inlets were added in the outer part of the DMS cell. As shown in panel (a), FWHM values are in a range of 0.85-0.99 V for the four AAs using only N2 as carrier gas and without addition of throttle gas. In panel (b), after introducing throttle gas at a flow rate of 0.5 l.min -1 , FWHM values decrease to 0.52-0.60 V. Higher flow rate of throttle gas results in smaller FWHM values. As shown in panel (c), when increasing the flow rate of throttle gas to 0.75 l.min -1 , FWHM values are in the range of 0.36-0.46 V. The CV, FWHM, and Rpp values are listed in Table 1. As can be seen from the table, the CV position of AAs did not shift too much. On the other hand, Rpp values significantly increase.

For instance, for leucine and phenylalanine, the Rpp evolves from 1.28 without throttle gas to 2.1 and 2.98 at flow rate of 0.5 and 0.75 l.min -1 , respectively. DMS peak-to-peak resolution is improved when the dispersion voltage is increased.

Nonetheless, this is at the expense of ion transmission efficiency and sensitivity, and a trade-off has to be found. 43 Electric field strength can be changed either by adjusting the DV value, of by adjusting the gap between the two parallel electrodes. 44 Electrodes with three different gaps at 0.7 mm, 1 mm, and 1. Controlling the nature of the carrier gas is another critical factor. It has been proved that changing gas composition can lead to CV shift. Besides, gas temperature and gas pressure can also influence gas density and thus change ion mobility in DMS cell.

Change carrier gas composition. It has been reported that increasing the percentage of

He from 50% to 75% in the He/N2 mixture gas can increase the resolution by a factor of ~3 using a FAIMS instrument. 33 Introducing organic solvent, often called dopant or modifier, into the carrier gas has become a popular strategy to improve separation. 25,45 Researches have proved that addition of a small volume of polar chemical modifiers into the default N2 carrier gas can greatly improve DMS separation. [46][47][48] And the improved separation ability were found to be related to the strength of the interaction of ions with modifiers. 34,49 It turns out the addition of modifiers can change the mobility related alpha parameter of analyte ions through selective ion-molecule clustering reactions in different ways, 25 thus affecting ions resolution.

Chapter Ⅱ. Ion Mobility Spectrometry Methodology 61

Researchers have found that the ion-modifier clustering is one of the most important factor to the dispersion plot of ions in DMS. 27,34 In recent years, efforts have been made

to understand and characterize the cluster formation between analyte ions and modifiers in gas phase. 5,25,26,28 It has been proved the proton affinity, modifier size, ion-modifier binding energies and clusters geometry can all affect ion-molecule interaction and result in ion mobility change. 34,50 In Figure 19 is an example of introducing methanol (MeOH) as modifier into N2 carrier gas to improve separation ability of two AAs. Tryptophan and Lysine were not separated using only N2 as carrier gas (see panel (a) in Figure 19). As can be seen from the spectra in panel (b), using 0.009% of MeOH as modifier, overlapped peaks of tryptophan and lysine can be well separated with Rpp of 4.57. If the percentage of MeOH is increased to 0.026%, Rpp value thus increased to 6.04. CV values are thus very sensitive to the gas composition, since as illustrated by the effects of these small percentage values of MeOH in the carrier gas. It can be observed that the addition of modifiers spreads the analytical peaks across a wider range of CV values compared with pure N2 while maintaining the peak widths, thereby increasing the overall peak capacity of the DMS experiments.

Gas temperature. Gas temperature may also affect DMS peak resolution. Indeed, CV positions have been observed to shift as a function of gas temperature. When increasing the temperature, it can be assumed that less clustering occurs. 51,52 This thus lead to positive CV shift. The higher the temperature is, the slighter the effect of contamination on the accurate measurement of reduced ion mobility is. It was thus suggested that performing IMS experiments at high temperature to decrease false positives in compounds detection. 51 We performed DMS experiments for 7 AAs (tryptophan, arginine, phenylalanine, methionine, lysine, isoleucine, and proline) at different gas temperatures. The results are The temperature effect on the CV value is ion depend. 34 For example, the observed CV shift for Phe was small. CV shift degree is larger for Pro, for which the CV value positive shifted from -17.3 V to -10.6 V. In order to achieve good separation of AAs, the gas temperature of 150 °C was thus applied in the following experiments by DMS.

IDENTIFICATION OF AMINOBUTYRIC ACID ISOMERS SEPARATED BY DIFFERENTIAL MOBILITY SPECTROMETRY

INTRODUCTION

In this chapter, two chromatography free DMS-MS/MS(IR) workflows are proposed for the separation, identification, and structural characterization of isomers of aminobutyric acid (ABA). This is the first application of the coupling of DMS and isomer-selective infrared induced fragmentation with high resolution MS/MS Fourier transform ion cyclotron resonance (FT-ICR). DMS-and mass-selected ions are subjected to infrared (IR) irradiation of the CLIO Free Electron Laser 1 allowing for infrared multiple photodissociation (IRMPD). Isomer-selective fragmentation of each isomer at a selected wavenumber allows for the structural characterization. In a first step, structural characterization of the modifier-assisted DMS peaks can be achieved through the comparison of IRMPD spectra of DMS-and mass-selected ions with IR absorption spectra predicted by density functional theory. Alternatively, identification of DMS peaks can be achieved using a second protocol where the IR wavenumber is fixed while the DMS parameter is scanned (DMS(scan)-MS/MS (IR fixed)).

Separation of ABA isomers is an important issue for their quantification. 2 For instance, γaminobutyric acid (GABA) is known for being an inhibitor of neurotransmission in the nervous system of animals, 3 whereas β-aminobutyric acid (BABA) can act as a partial agonist of the glycine receptor. 4 α-aminobutyric acid (AABA) has been investigated as biomarker for liver toxicity and alcohol abuse. 5 Separation of these and other ABA isomers has been investigated using GC-6 and LC-MS/MS The challenge therefore is to develop a direct, fast and sensitive method to completely separate and quantify these isomeric compounds in biological samples. Infrared Multiple Photon Dissociation (IRMPD) experiments were carried out using the IR beam delivered by the CLIO infrared free electron laser (IR FEL). 1 The coupling of the IR FEL with the FT-ICR has already been described in details elsewhere. 8 DMS-selected ions can be mass-selected in a quadrupole, and accumulated in a linear hexapole ion trap.

METHODS

Solutions

Typical accumulation time is 500 ms. DMS-and mass-selected ions are then pulseextracted toward the ICR cell, where they are irradiated by IR light for a fixed time period.

The energy of the IR FEL electron accelerator was set to 42 MeV. A fairly stable power was maintained in the 900-1900 cm -1 wavenumber (𝜈 ̃) range. Typical irradiation time for these experiments was 250-500 ms. The FT-ICR was equipped with an Apollo II ESI ion source. A dedicated homebuilt differential mobility spectrometry (DMS) device had been used for the first time. The conception of this DMS device is based on the one developed by G.L. Glish. 9 The coupling of such DMS device and IRMPD activation with our quadrupole ion-trap has already been described. 10,11 DMS spectra are recorded by scanning the CV value by steps of 0.1 V.

The DMS spectrum is derived from extracted ion chromatogram obtained using the commercial Bruker software. The centroid of each peak and its full width at half maximum Here we proposed a combination of two orthogonal techniques to MS/MS for the separation and identification of isomers: separation with DMS and identification through isomer specific IR induced activation. The principle of these ion depletion experiments is illustrated in Figure 1. For a given CV value, DMS-selected ions can be mass-selected in a quadrupole, and accumulated in a hexapole ion trap, before being pulse extracted towards the ICR cell where they can be trapped for a long period of time. DMS-selected ions are submitted to IR irradiation in the ICR cell, and fragmentation can be observed only if the IR wavenumber is on resonance with an absorption band of the DMS-selected ions. If one isomer has a specific IR absorption band, only fragmentation of this isomer will be induced if the laser is tuned on resonance with this absorption band. This is illustrated in Figure 1 where isomer 2 in red is selectively depleted when IR is fixed at wavenumber 𝜈 ̃2.

RESULTS AND DISCUSSION

Separation of three protonated ABA isomers with modifier-assisted DMS is investigated with methanol. The DMS spectra of an equimolar mixture of protonated AABA, BABA and GABA isomers are shown on Figure 2 with N2 only as desolvation gas (Figure 2a) and also with methanol injected at 1.5 μL.min -1 in the N2 desolvation gas (Figure 2b).

Figure 2a displays only two peaks. One centered at -7.0 V, and another, broader, centered at -5.5 V. By adding methanol in the N2 flow, DMS peaks are shifted towards more negative CV values, and the DMS spectrum (Figure 2b) exhibits three baseline separated peaks which are centered at -24.6, -20 and -16.9 V. This allows in particular for the resolution of the broadband observed near -5.5 V using pure N2 as a carrier gas. Yet, assigning DMS peaks and predicting how a broad and unresolved band would be resolved, and how its different components would be more or less shifted towards negative CV values is difficult. Assignment can be achieved by characterizing the CV value of each isomer through DMS spectra of each separated standard sample. This is shown in Figure 3a using N2 as desolvation gas. The peak at -7 V is for protonated AABA. BABA and GABA observed at -5.6 V and -5.5 V, respectively, are the two components of the broad DMS band. In the top panel of Figure 3a, the black curve which nicely fits the experimental points is the result of an optimized fit using three Gaussians. Each Gaussian is associated with one isomer, and its position is fixed at the one determined experimentally with the separated standard.

A similar protocol was followed with N2 seeded with methanol. Results are given in Figure 3b. The CV value of AABA, BABA, GABA determined experimentally is -24.6, -20.0, and -16.9 V, respectively. As can be seen in the top panel of Figure 3b, the experimental data are well fitted using three Gaussian functions centered at CV values fixed at those determined for separated samples. Some subtle differences between isomer spectra can be observed in the lower energy part of the spectra. In the 1350-1550 cm -1 range, a broadband is observed for the three protonated isomers. These bands correspond to C-H and N-H bending modes, and in particular the ammonium umbrella mode. As can be seen in Table 1, the latter mode is sensitive to the nature of the isomer. Another cluster of bands is observed between 1050 and 1250 cm -1 . As can be seen in Table 1, this band can be assigned to the C-O stretching and C-O-H bending modes.

IRMPD efficiency depends on both IR laser and ion characteristics. It has been shown in the literature that both the temporal structure and the high intensity of IR free electron lasers are particularly well suited for IRMPD spectroscopy. 12 Nonetheless, IRMPD efficiency is also inherently limited by the IR absorption cross section at a specific wavelength. As can be found in the literature, nonlinear effects have been observed due to the multiple photon character of the IRMPD process. In most cases, the IRMPD efficiency scales with predicted IR absorption cross section providing that the IR cross Chapter Ⅲ. Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 79 section is above a threshold which depends on the ion type. In the present case, the fact that no IRMPD signal could be observed at 1099 and 1650 cm -1 , i.e. predicted absorption bands of protonated BABA, is probably due to this limit in terms of sensitivity.

The most diagnostic region for structural assignment, however, is the C=O stretching region, in the 1650-1850 cm -1 range. For protonated AABA, BABA, and GABA, the experimental band is centered at 1780, 1740 and 1725 cm -1 , respectively. This trend is in good agreement with theoretical prediction: the C=O stretching frequency for protonated AABA, BABA, and GABA, is predicted at 1769, 1722 and 1703 cm -1 , respectively. It can thus be concluded that the C=O stretching range is the most structurally diagnostic region. In particular, the protonated AABA and GABA isomers are likely to be selectively fragmented if the IR laser is tuned to 1780 and 1690 cm -1 , respectively (vide infra). As discussed above, the three isomers are baseline resolved when DMS is performed with N2 seeded with methanol. Our purpose here is to characterize modifier-assisted DMS The corresponding IRMPD band observed when the CV value is fixed at -24.6, -20 and -16.9 V are centered at 1775, 1730, and 1710 cm -1 , respectively. This trend nicely reflects the one predicted for the protonated AABA, BABA, and GABA for which the C=O stretching band predicted at 1769, 1722, and 1703 cm -1 . It can thus be concluded that For the three isomers, the lowest energy structure has a hydrogen bond (HB) between the ammonium and the carboxylic C=O group. This type of intramolecular HB allows for charge solvation and is also observed for protonated aminoacids, 10 for example. In the present case, the HB gives rise to the formation of a five-, six-, and seven-membered ring for protonated AABA, BABA, and GABA, respectively. As expected, the Fragmentation mass spectra are provided in the annex. For each protonated isomer, two figures are provided. One corresponds to the extracted ion chromatogram corresponding to the IRMPD spectrum. In each case, the IRMPD spectrum was recorded starting at high wavenumber (i.e. CO stretch region). As a result, the wavenumber decreases as a function of time of the chromatogram. A fragmentation mass spectrum is also given for each protonated isomer. This fragmentation mass spectrum is an averaged mass spectrum over the time range corresponding to the CO stretching region.

As can be seen in the fragmentation mass spectrum of protonated AABA, only the m/z 58 fragment can be observed when the IR laser is on resonance with an IR active vibrational mode. In the case of protonated GABA, two fragments are observed. A specific fragment, m/z 87, and also the m/z 86 fragment. This latter fragment is also observed upon IR induced fragmentation of protonated BABA. Nevertheless, the m/z 44 fragment is also observed, which is specific of this isomer.

In the following, we will turn our attention to the assignment of the DMS peaks obtained in the absence of modifier gas (Figure 2a). DMS-MS/MS(IRMPD) spectra recorded at four selected IR FEL wavenumbers are given in Figure 6 along with the DMS-MS spectrum without IR activation. By tuning the laser wavenumber in the C=O stretching region, selective fragmentation of the BABA isomer could not be observed. This is illustrated in Figure 6d with the DMS-MS/MS(IRMPD@1730 cm -1 ) spectra. Simultaneous fragmentation of both protonated BABA and GABA isomers was observed. Similar observation were made at other wavenumber values between 1690 and 1780 cm -1 .

Chapter Ⅲ. Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 85 Specific depletion of the protonated BABA isomer could however be obtained at 1372 cm -1 (Figure 6b). At this laser wavenumber, the fit of the broadband centered near -5.6 V reveals only a depletion of the BABA contribution, which is consistent with the Gaussian fit of the m/z 44 and 86 fragments (Figure 6b, right), also centered at -5.6 V.

Overall, DMS-MS/MS(IRMPD) at three selected laser wavenumbers allows for the identification of the three isomers, and in particular for the resolution of two overlapping components. These results are consistent with those drawn from the comparison of the DMS spectrum of the equimolecular mixture with those of the individual standard DMS spectra given in Figure 3b. The broad peak in Figure 2a results from the overlap of two contributions centered at -5.6 and -5.5 V associated with protonated BABA and GABA isomer, respectively. The -7.0 V peak can be assigned to the protonated AABA isomer.

Modifier-assisted DMS using methanol allows for the resolution of the otherwise overlapping protonated BABA and GABA. The CV values observed for maximum transmission of the three protonated isomers without and with methanol addition are given in table 2, along with the CV shift value.

86

A tentative rationalization of these results can be made considering the charge availability based on the lowest energy structures. In the three cases, a folded structure is found with an intramolecular hydrogen bond between an ammonium hydrogen and the carbonyl oxygen. One can reasonably think that the larger is the stabilization of the ammonium charge through this hydrogen bond, the weaker is the interaction with the methanol.

Based on both the -NH3 + ---O=C distance and the predicted CO stretch, it is clear that the intramolecular hydrogen bonding strength increases from AABA to BABA and GABA.

As a result, one can expect the reverse order for the -NH3 + ---methanol interaction strength. As a result, the larger CV shift is expected for protonated AABA. The difference in structural parameters between the two other isomers is smaller, but we can expect a slightly larger CV shift for protonated BABA than for protonated GABA, as observed experimentally with GABA.

The fact that the structural parameters are relatively similar for BABA and GABA as compared to those of AABA may also explain why the two former isomers cannot be resolved with N2 only, while the protonated AABA is observed at lower CV value. In the two former cases, the interaction with N2, relatively weak as compared to that with methanol, is nearly the same. In the latter case of AABA, however, the positive charge is more available and the CV shift is larger.

CONCLUSION

Wavenumber-selective IR activation is integrated in two DMS-MS/MS workflows for the identification of isomers of small molecules such as metabolites using tandem mass spectrometry. Separation is performed using a homebuilt DMS device which has been specifically designed for the coupling with our high resolution FT-ICR MS/MS instrument.

Performance of the two workflows is illustrated on the separation of three ABA isomers.

Chapter Ⅲ. Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 87

Our objectives here range from fundamental understanding of the DMS separation process to developing complementary methodology for improving the identification of isomeric species in the context of targeted metabolomics analyses. From a fundamental point of view, IRMPD spectroscopy is a particularly interesting tool since it does not only offer identification but also structural information on DMS-selected ions. This is particularly useful since the ordering of DMS peaks is not easily a priori predictable, especially when modifier gases are seeded in the carrier gas.

The first IRMPD spectra of modifier-assisted DMS separated isomers have been recorded.

They match well with the IR absorption spectra predicted by DFT. In particular, a clear trend is observed in the C=O stretching region, which is consistent with isomer-specific hydrogen bonding motifs predicted for the lowest energy isomers by DFT calculations.

A second operation mode of the DMS-MS/MS(IRMPD) instrument consists selective or even specific depletion of DMS peaks. At each step of the DMS scan, DMS-and massselected ions are irradiated by the IR beam at a selected wavenumber. This second workflow would be particularly well suited for a rapid screening of target compounds within complex mixture, as in targeted metabolomics. Indeed, with the CV parameter set for the optimal transmission of a targeted ion to the ion trap, high ion count can be obtained allowing for subsequent infrared interogation.

There are multiple developments of MS/MS instruments integrating IMS separation and laser induced fragmentation, [13][14][15][16][17][18] such as IMS-UV, 13,16,19,20 IMS-IR, 15,21 and isomer specific IR spectroscopy: UV-IR by Rizzo et al, 22 IR-IR by Johnson. 23 These couplings of IMS, MS/MS and optical spectroscopy are specifically designed for probing the conformations of large and flexible molecules, such as polysaccharides. 20 The potential interest of infrared activation has also been recently highlighted [24][25][26][27] for the structural characterization of unknown metabolites, as observed using regular LC-MS/MS

INTRODUCTION

AAs and related compounds constitute a class of biomarkers which is routinely analysed for early diagnosis of metabolic diseases (MDs). 1 Separation of these metabolites within, for example, urine or blood samples, is still routinely performed by high-performance liquid chromatography (HPLC) coupled to spectrophotometric detection. 2 Rapid quantification of underivatized AAs in plasma using a HILIC column, 3 and more recently mixed mode stationary phase and a binary gradient of elution, 4 coupled with MS/MS have been reported. Despite these improvements providing high degree of selectivity, separation and identification of isomeric metabolites remain a difficult task. 5 Additional or alternative dimensions of separation could thus be of interest for the separation of isobaric and isomeric metabolites. [6][7][8][9] Ion mobility spectrometry (IMS) is an interesting alternative for the separation of small molecular weight ions such as ionized metabolites. 10 Efforts toward separation of isobaric and isomeric species led to the development of high resolution instruments and/or methodologies. Separation of diastereoisomeric chiral compounds such as AAs and peptides has been addressed recently. For the separation of D and L AA enantiomers, both derivatization 11 and formation of a proton bound dimers 12 with a chiral agent have been shown to be successful.

Mobility separation of peptides with 4-29 residues including a single D-AA at different residues and locations has recently been reported. 13 Significant enhancement of the resolving power can also be achieved by introducing a fraction

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of He 14 or polar molecules 15 in the carrier gas, as found in particular for AAs. 16 Our aim is to evaluate the analytical performance of differential mobility spectrometry (DMS) hyphenated to MS/MS for the analysis of AAs and related compounds. Protocols based on DMS-MS/MS experiments were developed for the quantification of these metabolites, especially in the presence of isobars and/or isomers, in plasma samples. In addition, we were also interested in evaluating the capacity of DMS for providing an additional metabolite identifier. Using drift time instrument, as recently discussed by Barran and coworkers, 17 mobility drift-time, 18 or even collisional cross sections (CCS) 19,20 can be used for identification of pesticides. This idea of using CCS as identifier was further pursued for metabolites. [21][22][23][24] Using DMS, the additional identification parameter would be the so-called compensation voltage (CV) which has to be tuned for the transmission of a specific metabolite ion.

This chapter provides a detailed presentation of part of our results published recently. 25 Twenty 20 common AAs and related compounds were analyzed using DMS-MS/MS protocol. The performance in terms of peak-to-peak resolution and peak capacity was compared to those using drift tube type IMS instruments, 11-13, 16, 17, 26 in particular for the separation of isobars and isomers. Potential effects of matrix or concentration of the analytes on the CV values were also evaluated, and found to be negligible. An interesting probe of the robustness of the method was also provided by the stability of the CV position for series of DMS-MS experiments carried over 6 months, during which the DMS device was often removed and replaced. Mixture of reference standards. A standard mixture made of 33 AAs and related compounds as listed in Table 1 was used for analysis. This mixture is based on a commercial AA standard solution (Type B, AN-II purchased from FUJIFILM Wako Pure Chemical Industries, Osaka, Japan). It was complemented by glutamine (Gln), asparagine (Asn), tryptophan (Trp), pipecolic acid, alloisoleucine (aIle), and sulfocysteine which were purchased from Sigma-Aldrich (St. Louis, Missouri, USA).

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collected by venous puncture into heparin containing tubes. In practice, blood was centrifuged rapidly after collection. Plasma was subsequently deproteinized by adding sulfosalicylic acid 30% (10:1, v:v) and then stored at -20°C. Just before analysis, samples were diluted in acidified water:methanol. The resulting 20-fold diluted plasma samples were used for electrospray ionization.

DMS-MS experiments were carried out using a modified Esquire 3000+ quadrupole ion trap mass spectrometer (Bruker-Daltonics, Bremen, Germany) featuring an ElectroSpray Ionization (ESI) source. 27 The setup is based on the one developed by Glish and coworkers. 28 Identical positive ESI conditions have been used throughout.

The voltage of the ESI emitter was set to 4.5 kV and the entrance glass transfer capillary was grounded. The flow rate of the sample was 40 µL.h -1 .

In the following, only DMS voltage values are given. In the perspective of comparison with data recorded under other experimental conditions, however, it is important to provide electric field, or even better reduced electric field values.

Considering the gap between the electrodes (0.7 mm), an electric field E value of 1.43 kV.m -1 is generated within the DMS gap when applying a DV value of 1.8 kV.

The derivation of reduced electric field E/N value requires the evaluation of the number of molecules per volume unit. The temperature of the DMS cell was found to be 70°C. At this temperature, the air density is 1.029 kg.m -3 . Assuming that air is composed of 80% of N2 and 20% of O2, the density number is 2.15×10 15 molecule.m -3 . The reduced electric field E/N value is thus 1.20 ×10 -19 V.m 2 , which corresponds to 120 Td, using the definition of the Townsend unit (1 Td = 10 -21

V.m 2 ). i.e. evolution of CV value for the optimal transmission of each AA as a function of the DV value, were derived. For this purpose, standard solutions were used. These solutions of AAs were prepared in a water:methanol solvent mixture acidified with formic acid (0.5%) at a final concentration of 100 µM.

RESULTS AND DISCUSSION

In this section, the performance of DMS for the separation of AAs is first evaluated, with a special focus on peak capacity and isobars and/or isomers resolution. Then, the compensation voltage value of the AAs and related compounds within a mixture of standards are determined, in both water:methanol solution and plasma.

We were especially interested in evaluating whether the CV parameter could provide a means for the identification of metabolites. Three classes of AAs can be distinguished based on the evolution of CV as a function of DV (Figure 1a). These three types of behaviors have been observed for other systems 30 and interpretations have been proposed 31,32 based on the Protonated Arg and Trp were found to be the only two systems of type C under our experimental conditions. This type C behavior is observed when the interaction between the ion and carrier gas atoms of molecules is weak. As a result, collisions are supposed to occur. This collisional regime is typically observed when the ion charge is not accessible 33 or when it is delocalized. 34 Considering the delocalization of the positive charge for protonated Arg, it is thus not surprising to observe that this ion behave as type C.

Protonated Lys, His, Phe, Tyr, Met, Gln, Ile, Leu, Glu and Val are type B ions. Note that some of these ions have been already found to behave as B type ions, such as protonated Leu, 35 and protonated Glu. 36 DMS spectra of each individual AA for three DV values, 1.6, 1. values (Å 2 ) derived using time-separation IMS instrument 17,22,37,38 are given in Table 2, for a given molecule, CCS value derived by the four groups can be significantly different.

For protonated L-leucine, for example, CCS values of 135 20 , 124 39 , 128 38 , and 130.9 40 Å 2 have been reported in different databases.

This dispersion of the CCS values has been the core of different inter-laboratory and interplatform reports in the literature, for steroid, 41 for example. This issue is thoroughly discussed in a recent review entitled "Recommendations for reporting ion mobility Mass Spectrometry measurements". 42 In the present case, it should be noted that only data reported by Zheng et al. 22 correspond to CCS values derived from a DT-IMS instrument.

The corresponding PNNL database provides CCS values for a large set of small molecules. 22 The ultimate purpose is to use these CCS values, which are unique physicochemical features associated with each specific molecule, as a molecular descriptor in addition to the exact masses of the molecular ion and its fragments as well as LC retention time. 43 CCS values reported by Barran, 39 Paglia et al., 36 and Davidson et al. 35 were derived from a TWIMS instrument. In this case, calibration of the instrument based on measurements of drift time values for a set of calibrants has be done in order to derive CCS values from drift time measurements. 42 In Figure 2 2). CV values are given in volt units. CCS (in Å 2 ) are taken from (a) ref. 22 , (b) ref. 44 , (c) ref. 38 , and (d) ref. 37 In this study, both peak-to-peak resolution (Rpp) and peak capacity (Pc) have been evaluated for our DMS data. Rpp and Pc are defined in equations ( 1) and ( 2), respectively.

The CVmin and wmin (CVmax and wmax) are the CV peak position and full width at half maximum (FWHM) values, respectively, corresponding to the minimum (maximum) CV value. Similarly, the Rpp is derived using the CV peak position and width of two molecular ions (equation 1).

𝑅 𝑝𝑝 = 1.178 × 𝐶𝑉 1 -𝐶𝑉 2 𝑤 1 +𝑤 2 (1)
𝑃 𝑐 = 2 × 𝐶𝑉 𝑚𝑎𝑥 -𝐶𝑉 𝑚𝑖𝑛 𝑤 1 +𝑤 2 (2)
Pc was found to increase with DV, from 4. and ~80% resolved AAs (Rpp>1.0)) for a high performance drift tube instrument using N2 as a carrier gas. 37 Separation of L-Leucine and L-Isoleucine (m/z 132) is often used as a test case. Their CCS values from the PNNL group 22 were found to be 135 and 137 Å 2 , respectively (see Table 2). The difference in CCS value is obtained from peak apex and no statistical analysis is reported regarding peak width. Assuming that the FWHM of TWIMS peak is on the order of 1%, 45 the peak-to-peak resolution is on the order of 0.87. In this particular case, the DMS Rpp value is lower (0.15). Using the same CCS database, the Rpp value for two couples of isobars, Asp-Orn and Gln-Lys, is 0.5 and 0.24, while much larger Rpp values are obtained using DMS (2.1 and 1.8, respectively). CV as metabolite identifier: no matrix, nor concentration effects. Different representations of our DMS-MS data recorded for a selected set of AAs and related metabolites in two matrices, plasma and water:methanol, are reported in Figure 4.

2D plots where ion intensity is plotted in both m/z and CV dimensions are given.

For a sake of clarity, a log scale has been used. A 2D plot corresponding to DMS-MS data of a mixture of standards diluted in water:methanol is given in Figure 4a. As expected, a higher density of contributions both in CV and m/z dimensions is observed in plasma. Nevertheless, the 2D plots of standards and plasma show common features. As can be seen in Figure 4c, no significant matrix effect on CV position nor in the DMS peak width are observed. This is confirmed through the comparison of the CV values determined for a selected set of metabolites in a plasma and diluted in water:methanol solution (Table 3). The standard deviation (SD) derived using a set of 36 plasma measurements is smaller or equal to 0.1 V, except for the lightest ions, i.e. protonated glycine (0.2 V), alanine (0.3 V), and serine (0.2 V). Considering the SD values, there is an excellent agreement between the two sets of values, and thus no evidence of matrix effects.

Beside matrix effects, concentration effects on DMS peak width and position could also be expected. DMS peaks were characterized for a set of AAs and related compounds when spiked in plasma P1 (pool of healthy plasmas) using six dilution factors spanning two orders of magnitude of concentration (1-200 µM). Overall, both FWHM and position of DMS peaks were found constant over these experiments. For the molecules of interest here, the mean value of CV standard deviation was 0.1 V. The maximum CV standard deviation (0.18 V) was found for protonated glycine, for which signal instability has already been mentioned.

Similarly, the peak width was also found to be not correlated with the concentration value. The mean FWHM value was 0.72 V, and the standard deviation was 0.1 V in the concentration range examined. CV peak assignment. Peak assignment in the positive CV range is a difficult task and has to be carried carefully. Proton and other cation bound homo-and heterodimers are typically transmitted at positive CV values. 46 These dimers have been shown to be partially fragmented in the source region.

This type of fragmentation which occurs at the exit of the glass capillary, is often referred to as "in source fragmentation". In the present context of DMS-MS experiments, it is important to recall that the glass capillary is just downstream of the DMS device. 46 As a result, this so-called in-source fragmentation, is also referred to "post-DMS fragmentation". 47 DMS peaks associated to a dimer, but corresponding to fragmented dimers, which are observed in the positive CV range are found to be broad. This is due to the fact that multiple hetero-dimers may contribute to the DMS peak. As evidenced in Figures 4a and4b, 2D plots in the positive CV range do not significantly depend on the m/z value. This is due to the fact that in addition to homodimers, hetero dimers formed with water, methanol, or another metabolite can contribute to the ion signal. The charge of these dimeric ions is embedded, and thus not available for strong interaction with the carrier gas. As a result, all these ions have type C behavior, and are transmitted at very similar CV values. This can be illustrated in Figure 4c, where wide peaks were observed in XIC of different m/z in the positive CV range around 2.5 V. For example, a wide peak at 3.1 V is observed for m/z 104 ions which correspond to the protonated dimer of ABA.

Post-DMS fragmentation could potentially be an issue for peak assignment.

Nevertheless, since protonated monomers and their corresponding dimers are observed at the same CV positions, it is not an issue. As shown in a study by Glish and coworkers, 47 evidence for formation of monomers through post-DMS fragmentation of dimers can be simply evidence by adjusting voltage parameters of the capillary exit relative to the skimmer.

Peak assignment may be complicated due to slight post-DMS fragmentation of some protonated monomer of standards which may also occur. This is the case for protonated phenylalanine (m/z 166) which has an m/z 120 fragment shown in Figure 4c with "#" marks. These fragments can be easily assigned because they are generated through post-DMS dissociation and are thus found at the CV position of the corresponding precursor ion.

Finally, one must also pay attention to isotopic peaks which are shown with a "&" mark in Figure 4c. These isotopic peaks can be relatively intense as in the case of arginine (MH + , m/z 175), which is abundant in the plasma. Protonated arginine is observed at 0.95 V, and a significant contribution associated with the first isotopic peak is also observed in the m/z 176 extracted ion chromatogram (XIC). Similarly, CV as metabolite identifier: reproducibility and robustness. The robustness of the method in terms of accuracy on the peak position over time is an important issue in the perspective of using CV as a metabolite identifier. 3. The maximum standard deviations were found for proline (0.14 V) and valine (0.12 V) while other standard deviation values are smaller than the CV scan step (0.1 V). The DMS specificity is also illustrated in Figure 6. When the DMS filtering is turned on, only the targeted ions are transmitted, as well as their corresponding IS (i.e.

protonated Val-d8 or Pro-d7). Note that the maximum transmission of the IS is achieved at a CV value slightly different from that of the corresponding standard.

Nevertheless, the CV difference typically ranges between 0.0 and 0.5 V, i.e. at a lower value than the peak FWHM value (0.8 V).

The MS instrument parameters were tuned so as to avoid saturation of the trap. This is the reason why the peak intensity of protonated valine (m/z 118) and proline (m/z 116) are the same using either DMS-off or DMS-on mode. If the mass spectra integrated over the DMS peaks are considered (Figure 6c), however, a 10-fold intensity increase is observed. A similar discussion the DMS specificity has been reported by Chen and coworkers in a study of two radiation biomarkers in human and non-human plasmas using a triple-quadrupole instrument. 48 Considering the limited mass-resolution of our quadrupole ion trap instrument, the separation of isobaric species is an important issue. The performance of DMS can be assessed by inspecting the DMS spectrum in Figure 4c where separation of isomeric and isobaric compounds was exhibited. The corresponding CV values and CCS values for a selected set of isobaric and/or isomeric species discussed below are given in Table 2.

Apparent disagreements in terms of DMS peak position may in fact the result of the different relative concentrations of (partially) unresolved isomeric compounds in the two matrices (water:methanol and plasma). This is the case for example for the broad DMS peak observed near -7.5 V for m/z 90 ions (Figure 4c). It results from contribution of α-and β-alanine. 49 In a previous study of the group, it was found that under the same experimental conditions, and using N2 only as a carrier gas, α-and β-alanine are not fully resolved, the latter being observed at slightly more negative (-0.3V) CV value than the former. The difference between the two m/z 90 XIC near -7.5 V (Figure 4c) is thus simply due to the fact that β-alanine is only found as trace amounts in the plasma while it is a component of our standard mixture.

More important is the issue of the separation of α-alanine and sarcosine which cannot be distinguished based on their fragmentation mass spectra. These two ions were found to be baseline separated using N2 only as a carrier gas. 49 The sarcosine peak was observed at -10.6 V. This DMS peak in observed for the standard mixture, but not for the plasma, where Sarcosine is expected at a very low concentration in a pool of healthy plasmas.

A similar issue occurs in the XIC at m/z 132 (Figure 4c). Indeed, leucine and isoleucine have similar concentrations in the standard mixture, but not in the plasma. As a result, the shape of the unresolved DMS peak in the XIC at m/z 132 (Figure 4c) changes accordingly. In addition, note that allo-isoleucine is also included in the standard mixture, while it is not expected in plasma P1 which is from healthy volunteers.

Measurements on each individual leucine isomer were performed, and the CV values of leucine, Isoleucine, and allo-isoleucine were found to be -3.0, -2.8, and -2.9 V, respectively. Better peak-to-peak resolution is obtained with isobaric cis-and trans-hydroxyproline, which were found to be transmitted at -3.6 and -4.0 V, respectively.

Three baseline separated peaks were observed at -4.0, -2.6, and -1.4 V for m/z 133 ions. Based on measurements on individual metabolites, the peaks at -4.1 and -1.3

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V can be assigned to asparagine and ornithine, respectively. As can be seen from Figure 2, the DMS resolution is better than that obtained with a drift-tube instrument. For example, ornithine and asparagine measured CCS were found to be 130 and 132 Å 2 , respectively. 22 Similarly, protonated glutamine and lysine were found to be baseline resolved and found at -2.7 and -0.3 V, respectively. Their measured CCS, however, were found very close (133 and 132 Å 2 , respectively). 22

CONCLUSIONS AND PERSPECTIVES

We herein provide evidence that DMS hyphenated to an MS/MS instrument is a valuable alternative approach for the separation and identification of AAs and related compounds. As retention time in LC-MS/MS, compensation voltage (CV) value allowing for the maximum transmission of a specific metabolite could allow for its identification. It is thus important to stress that the position of the DMS peak position values of metabolites of interest do not depend on concentration and/or matrix effects. Variations of CV positions were found to be stable within ±0.1 V, as compared to peak width (FWHM) of 0.72 V. For a set of AAs and related compounds, we showed here that CV values are reproducible over a large period of time (6 months). The proposed DMS method itself is robust since these experiments were carried out on an open MS/MS platform, and the DMS device thus had to be mounted and unmounted several times.

DMS could thus be an alternative separation method for MS/MS based targeted metabolomics for the identification of metabolites. As discussed in the recent literature DMS can also be hyphenated to LC-MS/MS. 7,[50][51][52][START_REF] Fu | Differential Mobility Spectrometry Coupled with Multiple Ion Monitoring in Regulated LC-MS/MS Bioanalysis of a Therapeutic Cyclic Peptide in Human Plasma[END_REF] For example, a LC-DMS-MS/MS platform was probed for the separation of a mixture of eight isobaric compounds at m/z 316 by Hopfgartner. 51 Such two dimensional separation LC-IMS-MS/MS method allows for resolving chromatographic peaks, and also for an

INTRODUCTION

Modern clinical research for more adequate diagnosis and prognosis strongly relies on metabolomics-based strategies. 1 The challenge is first to identify biomarkers in a complex network of metabolic pathways. Then, robust methods are required for a quantitative analysis of targeted metabolites. 2 For diagnosis of metabolic diseases (MDs), 3 amino acids (AAs) and related compounds constitute a class of targeted metabolites. This is the case, for instance, for the diagnosis of aminoacidopathies such as Phenylketonuria or Tyrosinemia. 4 Separation, identification, and quantification in the frame of clinical diagnosis often rely on MS/MS coupled with a chromatographic method. A metabolite can be characterized by its retention time, and its MS/MS fragmentation pattern. While isobars can also be resolved using high-resolution MS/MS or gas/liquid chromatographic (GC/LC) separation methods, separation of isomers remains an important issue. As demonstrated by the distribution of detected human serum metabolites by chromatographic system, isomers take the proportion of about 27% of the identified compounds. 5 The presence of isomers and isobars compounds remains a challenge for absolute identification of biomolecules in complex matrix. 6 DMS-MS workflows have been shown to overcome some limitations of GC-or LC-MS/MS such as low chromatographic resolution of isomeric and isobaric species. 7,8 Furthermore, while a set of specific chromatographic conditions are required for each class of metabolite, a single DMS device could be used for all metabolites. 9 In the previous chapter, DMS-MS performance was positively assessed for the assignment of common AAs and related compounds, including isobars and isomers, in plasma samples. It was found in particular that each metabolite could be characterized by a compensation voltage (CV) value, which was found to be stable over long period of time (six months). DMS-MS could thus be a highthroughput alternative to LC-MS/MS for targeted metabolomics in general, and for quantitative analysis of biomarkers in particular. Despite several success stories reported in the literature, for example the rapid DMS-MS analysis of drugs and their metabolites in bio-samples, [10][11][12] more systematic DMS-MS quantitative analysis of metabolites in complex biological samples are required. In this chapter, quantification of AAs and related compounds based on a DMS-MS protocol is assessed. Our objective is two-fold. Four plasma samples were analysed, including two from MD patients.

Our second objective is to make the DMS-MS acquisition as fast as possible. A second protocol is proposed, and it relies on the stability of the CV value associated with each metabolite. Based on the robustness of results discussed in the previous chapter, the idea would be to quantify each metabolite based on a single DMS-MS spectrum recorded at the specific CV value corresponding to this metabolite. Before performing experiments, simulations were performed using previously recorded data. 13 For this purpose, instead of a two-dimensional (2D) fit of DMS peaks, one-dimensional (1D) fits at fixed CV values were performed. Based on these results, a dedicated protocol allowing for recording only DMS-MS spectra at specific CV value was then designed.

EXPERIMENTAL METHODS

Mixture of standard solution of AAs and internal standards.

A standard mixture made of 33 AAs and related compounds as listed in Table 1 (left column) was used for calibration purpose. A mixture of stable isotope internal standards (IS) was made using reference compounds from Eurisitop (Saint Aubin, France). The list of these IS as well as their final concentrations in the samples are given in Table 1 (right column). This IS mixture was used for the quantification protocols. Four plasma samples were used for quantification analysis. These samples are denoted as P1, P2, P3, and P4. The quality control P1 plasma is a pool of plasmas taken from healthy volunteers. Plasma P2 and P3 were collected from patients with Phenylketonuria and Tyrosinemia, respectively. P4 is a random plasma.

These samples were not specifically collected for validation of this study. They were collected for clinical diagnosis purpose as part of the patient's routine care.

Informed consent was obtained from patients for the use of leftover samples for analytical developments. The study was approved by the ethics committee of Concentrations of AAs and related compounds in each plasma sample along with their corresponding uncertainties are given in Table 2. For each compound, the LC-MS/MS uncertainties were derived from the corresponding inter-day standard deviation (SD) of an equivalent concentration calculated from plasma controls daily analysed over a period of 5 months. As can be seen in this table, concentrations in the random plasma P4 are significantly different from those in the pool of plasmas For quantitative analysis, spiked plasma samples containing standard solution mixture and IS were analyzed. For each compound, a linear calibration curve was derived. Different options were explored for the matrix to be used for performing the corresponding calibration experiments. Not surprisingly, strong matrix effects on the linear slope were observed. The choice was made to use the pool of plasmas (P1) as a background matrix. The set of standards of AAs and related compounds (Table 1) was spiked into the P1 plasma at six dilution factors spanning 2 orders of magnitude of concentration (Table 3).

Concentration values of IS are those given in Table 1. Except for pipecolic acid and alloisoleucine, which are found at very low concentrations in plasma. The dilution levels were chosen so that the AA concentrations in analyzed plasma lie in the range of the spiked P1 solutions. In practice, plasma samples were deproteinized using sulfosalicylic acid with a volume ratio of 10:1. After thoroughly vortexing, mixtures were centrifuged at 13,000 round per minute (rpm) for 15 minutes. The supernatant was then Plasma samples with a final dilution factor of 20 were then injected into the electrospray source and analyzed by DMS-MS. The workflow is illustrated in Figure 1. Quantitative determination of metabolite levels in the four plasma samples (P1-P4) was then performed. The sample preparation protocol is similar to that used for the calibration, except that no standard solution of AAs was spiked. Each day one or several plasma samples were analyzed. Data for building a calibration curve were also collected. In order to check for potential carry-over, a blank run was systematically performed between two plasma runs by DMS-MS. The concentrations of metabolites were calculated from their corresponding peak area ratios using an average calibration curve. This approach is consistent with that used at hospital Robert Debré.

DATA ANALYSIS

DMS-MS spectra were recorded, and extracted ion chromatograms corresponding programming and statistical analysis. The R-written code package is available online. 14 This suite of programs is described hereafter. It allows for a fit of the DMS-MS peaks in two dimensions (m/z and CV) assuming Gaussian profiles. Peak characteristics, and in particular peak area of standard and corresponding IS are first determined.

These data can then be used for statistic and quantification purpose. In particular, linear calibration curves are constructed where peak area ratio (standard/related IS) is plotted against nominal concentration of standards.

Our program allows for the analysis of a sequence of several DMS-MS data sets, and for each of them a list of several targeted compounds. For this purpose, two files are first read by our program. A target table file, "tgTable", contains the list of compounds to be analyzed. This file is made of 4 columns as shown in the Table 4. A target name is given, and the reference values of the m/z and CV values, "m/z_ref" and "CV_ref", respectively. Exact or approximate values can be given, and a 2D

Gaussian fit is performed in a window around these values. Alternatively, Gaussian fit can be performed using constrains, for example by fixing the m/z or the CV position. Comments can also be given in this table. Lines starting with "#" will be considered as comment lines. In the present example, Glycine will not be analyzed.

The sequence of DMS-MS data sets to be analyzed is given in the other input file called "taskTable". As shown in Table 5, each task is characterized by 6 columns.

MS/MS and DMS operations are synchronized but recorded as two separated files.

The "MS_file" is an ascii file extracted using the Bruker DataAnalysis software. These MS data files are located in different folders specified as relative path (column 6).

The "DMS_file" is a simple text file generated by our custom built LabView program. Merging the MS and DMS data just requires a pair of time and CV values which are given in columns named "t0" and "CV0". These two character strings are thus extremely important, since they allow for the conversion of acquired time to CV values in the extracted ion chromatograms. While the CV step used is generally 0.1

V, the time step may vary since the ICC mode (ion charge control, for the automatic control of accumulation time) is used in order to avoid saturation of the ion trap.

As mentioned above, these two files allow for the targeted analysis of a set of data.

For each targeted metabolite, the corresponding DMS peak is fitted, and the corresponding parameters (optimized CV, m/z, and corresponding fwhm values) are saved. In addition, figures are also generated and saved on demand. These files are saved under a specific directory, named after the date of the DMS-MS analysis, as well as the full name of the set of MS and DMS data. One can thus easily compare Our suite of programs also allows for statistical and quantitative analyses. In order to build a linear calibration curve, one needs to specify the dilution factor in the "dilu" column (Table 5) for each set of data. Quantitative analysis is performed using isotopically labeled compounds, and a specific input file is used named "quantTable" with 5 columns, as illustrated in Table 6. This file contains the list of compounds and internal standards used for quantification. Here, "Name" is the name of targeted compounds, i.e. Gly; in column "IS", the name of the corresponding labelled internal standard (IS) is given; "CAA_Plasma" is the concentration of AA in pool of plasma matrix; "CAA_ref" is the initial concentration of standard solution spiked in the calibration sample; "CIS_ref" is the concentration of IS spiked in the plasma sample.

The data processing is illustrated in the following using the example of tyrosine 𝐺(𝑥, 𝑦; 𝐴, 𝑥 0 , 𝑦 0 , 𝜎 𝑥 , 𝜎 𝑦 ) = 𝐴

√2𝜋𝜎 𝑥 𝜎 𝑦 𝑒 - 1 2 ( 𝑥-𝑥 0 𝜎 𝑥 ) 2 𝑒 - 1 2 ( 𝑦-𝑦 0 𝜎 𝑦 ) 2
Our R written program allows for various restrictions and/or constrains to be applied on the fitting procedure. For example, the CV and m/z range to be explored upon the fitting for a concentration range of analytes of interest is prepared, so that concentration of a metabolite can be calculated based on peak area ratio of the metabolite and its corresponding IS and the concentration of IS.

In practice, our R-written program derives the statistics and linear regressions from the 2D fits of the different DMS-MS sets of data. In a specific R-written module, called "quantify", the concentration [CAA] of targeted metabolite (named AA) can be calculated using the equation:

[CAA] = [CAA_Plasma] + [CAA_ref] × dilu
For the linear calibration, the concentration of the targeted metabolite, [CAA], is the sum of two contributions: the endogenic one and the spiked one, where "CAA_Plasma" "CAA_ref", defined in Table 6, have the same values for a specific set of experiments on a given plasma. The dilution factor, however, taken from "dilu" column of Table 5, can take different values. The peak area ratio of AA and related IS (ratio) is calculated as: ratio=Area (AA)/Area (IS)

where, "Area (AA)" and "Area (IS)" are the peak area of AA and IS, respectively. The concentration of related IS [CIS] is read from the column of "CIS_ref" in Table 6. Linear regression is thus produced by plotting calibration curve for "ratio" as a function of Lines in green is the linear regression with variable intercept. For each linear regression, the 95% confidence interval is represented by two dotted lines. Vertical yellow dotted line indicates the estimated limit of detection (LOD) value.

The linear regression with intercept equals 0 is used to calculate the concentration of unknown sample. Assuming that b is the slope of the linear regression with a zero intercept, the concentration of targeted amino-acid metabolite [AA] is derived as:

[AA]=[CIS]/b × Area(AA)/Area(IS). LOD, accuracy and precision. The sensitivity of the method is generally defined by the LOD value, which corresponds to the lowest concentration value that can be detected. 16,17 LOD can be determined using different approaches: 18,19 signal-to-noise method, blank determination, and linear regression. Here, LOD was derived from the linear regressions.

LOD is defined as 3×SDb/S, where S is the slope of linear regression fit, and SDb is the standard deviation of the intercept of the regression lines.

This method is an interesting alternative to the well-known signal-to-noise method when using an MS/MS instrument, such as our 3D quadrupole ion-trap, which does not involve background noise. 18,20 LOD values for targeted AAs and related compounds are given in Table 7. For each metabolite, a calibration curve was constructed each time, the final quantitative curve was thus obtained from the average values of different measurements.

These LOD values were determined from the final linear regression. These results demonstrated that the accuracy and precision values were within acceptable criteria and the quantitative method was reliable and reproducible for determination of metabolite concentrations in plasma samples. More importantly, the concentration increase of the biomarker for these two metabolic diseases determined by the DMS-MS method were in excellent agreement with those obtained using LC-MS/MS. In the case of Phenylketonuria plasma P2, concentration of Phe was calculated at 477±22 µM with our method, compared to 423±24 µM with an LC-MS/MS protocol used in routine for MD diagnosis. In the case of the plasma P3, concentration of Tyr was found to be 442±49 µM with DMS-MS, compared to 436±24 µM with LC-MS/MS. These results demonstrated the consistency and stability of the treatment protocol across samples.

Intra-day precision. Intra-day quantification experiments were performed on a random plasma (P4). The concentration levels determined with our method are given in Figure 9 along with those determined for clinical diagnosis using LC-MS/MS. AAs and related metabolites were analyzed 6 times, and a blank was recorded between two plasma analyses. Overall, there is a good qualitative agreement between the two sets of concentration derived by the two methods. Compared to the pool of plasma (P1), a concentration increase of 4 out of the 13 selected metabolites (Lys, Thr, Tyr, Met) is observed. Overall, taking into account error bars, concentration levels determined with the two methods are in good agreement.

In the case of Met, however, the increase is slightly overestimated using our DMS-MS protocol. For the three other metabolites (Lys, Thr, Tyr), there is a good quantitative agreement between the DMS-MS results (389±10, 389±18, 193±15, respectively) and the LC-MS/MS ones (397±24, 412±26, 184±10, respectively). We can thus conclude that the optimized DMS-MS system developed in this assay provides a reliable chromatography-free quantification technique for clinical diagnosis.

FAST QUANTIFICATION BASED ON DMS-MS AT FIXED CV

To achieve fast and accurate DMS-MS quantification of targeted analytes, we asked ourselves whether rather than performing full CV scan, we could quantify a metabolite faster by recording a single MS spectrum at a fixed and specific CV value. To evaluate this possibility of fast DMS-MS quantification method, we first performed numerical simulations of these "fixed CV" DMS-MS experiments. Our four plasma samples were used, as well as water:methanol (50:50, v:v) solution spiked with AA and related compounds, as well as their corresponding IS. All the numerical simulations were performed using our R language based script. Using all data sets of our plasmas associated with CV scans, a mean CV value was determined for each metabolite and its corresponding IS. These CV values were used to analyze the same DMS-MS data using 1D (fixed CV) rather than 2D peak fitting. Based on the positive comparison between concentrations derived using 1D and 2D fits, the actual "fixed CV" experiments were then performed. In Figure 10, the bottom and top of the box represent the 25 th and 75 th percentile of the data, respectively. In order to ease the comparison, for each metabolite, concentration values were normalized with respect to the median value associated with the 2D fit. The median concentration of the metabolite is depicted as a horizontal line inside the corresponding box. They were found to be very similar using the two fitting modes for Pro, Tyr, and Orn. As can be seen in Figure 10, there is an overlap between the two boxes except in the cases of Val, Cit and Trp.

Evaluation of the intra-day precision in

The whiskers representation is a line connecting the lower and upper data points, and is very useful to evaluate the variability. Overall, the variability is larger using the 1D than the 2D fitting procedure. Note that when a particularly large value is obtained, the corresponding extreme data point is shown as a circle in Figure 10. It can thus be concluded that quantification results using fixed CV mode are consistent to results of 2D fitting analysis. 8. Concentrations associated with the two analyses of the DMS-MS data sets associated with a random plasma are given, as well as the reference LC-MS/MS values determined at the hospital. For each metabolite, our concentration level values agree well with that determined using LC-MS/MS. High intra-day precision by DMS-MS approach was observed at a concentration range of 25 μM (Cit) to 1109 μM (Ala).

These data suggest that the 1D (fixed CV) fitting is as reliable as the 2D fitting analysis for the quantification of targeted metabolites. Evaluation of the inter-day precision in the pool of plasmas. To assess the inter-day precision and matrix effect, the pool of plasmas from healthy volunteers was analyzed.

Data from 5 sets of experiments recorded over three months were considered.

Concentrations of 16 metabolites were derived by 2 types of fitting modes (2D, 1D (fixed The CV values corresponding to maximum transmission of targeted AAs and their IS are listed in Table 9. As can be seen in this table, the CV value associated with protonated metabolite can be different from that of its corresponding IS. Similar observations had been early reported by Guevremont and coworkers in a DMS-MS study of the two isotopes of negative 35 Cl -and 37 Cl -atomic anions. 21 More recently, it has been shown that by extending the drift path within a SLIM (Structures for lossless ion manipulations) device isotopes of protonated amino-acids can be partially resolved. 22 For the 8 AAs of interest here, the difference (CV value reported in the last column of Table 9) ranges from 0 to 0.4 Volts. The CV value associated to the IS is systematically 9.

Experiments on plasma samples were thus performed at 16 different CV values listed in Table 9. The results are provided in Figure 16

CONCLUSIONS AND PERSPECTIVES

Two protocols for metabolite quantification in plasma samples based on DMS hyphenated to MS strategy are proposed. Isotopically labelled internal standards have been used for the quantification 23 of selected metabolites in human plasmas. The method linearity, intra-and inter-day precision, limit of detection, and trueness analysis were found to be adequate in both physiological and pathological conditions. Concentration levels of metabolites derived with our DMS-MS protocols were found to be in good agreement with those obtained with routine LC-MS/MS protocols used for the diagnosis of MDs at the Hospital Robert Debré (Paris). In particular, two applications dedicated to MD diagnosis have been performed in order to address the topics of metabolic diagnosis and newborn rapid screening. Biomarker analysis time could be significantly reduced using DMS-MS compared to LC-MS/MS approaches. Even using an ion-trap as in the present study, a full CV scan can be performed within ~5 minutes.

Furthermore, we provide a fast and accurate method for metabolites identification and quantification in plasma at fixed CV based on their specific response to DMS, followed by proposing the derived CV as a new biomarker identifier for MDs diagnosis. In the case of In summary, rather than scanning continuously the CV value over a wide range, DMS-MS spectra can be recorded only at CV values corresponding to targeted metabolites.

Furthermore, the data treatment process can be greatly accelerated using our R-written suite of codes. This can greatly improve the analysis efficiency and save time required for emergency diagnosis, typically 60 minutes, allowing for an immediate therapeutic decision. 24 Quantification experiments were achieved using our 3D quadrupole ion trap, which has intrinsic limitations both in terms of sensitivity and linear dynamic range.

Coupling of our DMS with a triple-quadrupole mass spectrometer will further improve sensitivity. As described in the litterature, [25][26][27][28] chromatographic separation could be included in the assay for the quantification of analytes at the low concentration.

In the future, we may see more examples of utilization of DMS in challenging assays (e.g., isomer identification, metabolic pathway analyses) and in cost-and time-reduction strategies for conventional workflows, e.g. offsetting exhaustive sample preparation, etc. 29 The idea is not to replace LC-MS/MS assays, but rather offers an easy, fast and reliable complementary protocols using DMS for specific biomarkers. The hyphenation of HPLC-FAIMS also highlight the great potential of this technique in metabolomics analysis recently. 26 DMS hyphenated technology would be more utilized in challenging assays in the future, such as isomeric separation, metabolomics with reduced running time and improved separation ability compared to conventional workflows.

INTRODUCTION

Introduction to MSUD, and its biomarkers. Maple syrup urine disease (MSUD) is an autosomal recessive disease. It estimated incidence is one in 185,000 live births worldwide. 1,2 Its screening is currently included in new born screening (NBS) programs of many countries. 3 MSUD is caused by a deficiency of the branched-chain 2-oxo acid dehydrogenase complex. 4 A signature of this enzyme malfunctioning is an elevated level of branched chain amino acids (BCAAs), in particular L-leucine (Leu), L-isoleucine (Ile), Lallo-isoleucine (aIle) and L-valine (Val). 5 Based on literature data, aIle is reported as being the actual pathognomonic marker for the diagnosis of MSUD. 6 Chromatographic methods hyphenated to mass spectrometry instruments are routinely used for MSUD analysis. Chemical structures of Leu isomers which are at play for the analysis are given in Figure 1. Leu and Ile are constitutional isomers. On the other hand, Ile and aIle are diastereomers with two chiral centers that only differ in their C3 stereochemistry. Isobaric hydroxyproline (OH-Pro) compounds are also shown in Hydroxyprolinemia is a metabolic, non-fatal, disease, and trans OH-Pro is used as a biomarker. As a result, OH-Pro may interfere with the detection of MSUD using low-resolution mass spectrometry alone. 7

Ion mobility spectrometry (IMS) could thus be an alternative for the separation of Leu isomers and isobars, and ultimately for MSUD diagnosis. IMS hyphenated to MS has recently been shown to be promising not only for the separation of isobaric and isomeric molecules, 22 but also for their identification. 23 IMS is a potential alternative for the separation of small molecules in metabolites. 24 Because of its separation potential and rapid speed, IMS has been applied for separation of clinically relevant compounds, 25 such as AAs. For small molecules, constitutional isomers represent the most resolvable isomer class and then diastereomers. 26 Recently, Leu and Ile have been reported to be partially resolved using either drift tube ion mobility (DT-IMS) 26 or traveling wave IMS (TWIMS). 27 In this work, we evaluated the capacity of DMS hyphenated to MS for identification, separation, and quantification of aIle and OH-Pro for accurate and fast MSUD diagnosis.

Table1 Mass fragmentation of Leu isomers and isobars. Protonated aIle, Ile, and Leu as produced upon ESI share the main ion transition 132→86, which prevents from their differentiation by MS/MS alone. 33 One can argue, however, that these isomers may be distinguished on the basis of the relative intensities of their secondary product ions. Ile and aIle diastereoisomers have the same fragmentation patterns. 11,32 They are characterized by an intense peak at m/z 69, 32 while a fragment at m/z 30 is more abundant for Leu isomer. Based on secondary fragment ions, isobaric OH-Pro isomers can be identified using the fragment at m/z 68 formed under collisional activation, and this 132 stories concerning DMS in metabolomics, the application range of this technique is not fully understood." 1 Within this thesis work, we focused our attention on amino acids and related compounds. Rather than untargeted metabolomics, 2 our approach is that of targeted metabolomics, with a special focus on isomeric, and to some extent isobaric, metabolites.

We were thus primarily interested in the separation and identification of isobaric and isomeric metabolites, which remain a major challenge for LC-MS/MS methods. 3 The quantification performance of our DMS-MS protocol was characterized by comparing the Using the default N2 as a carrier gas, we showed that isobaric species as well as constitutional isomers of AAs and related compounds can be separated in most cases with DMS-MS. Using the same set-up, separation of stereoisomers of saccharides such as epimers can also be easily achieved. 4 Predicting a priori whether two isomers can be easily separated with DMS, however, is a difficult task. This is illustrated with our study of the separation of isomeric aminobutyric acids (ABA), for example. How could we predict a priori that using N2 as a carrier gas, AABA isomer is baseline resolved from two unresolved BABA and GABA isomers? 5 Similarly, how could we predict that protonated sarcosine is baseline resolved from two unresolved α-and β-alanine? 6 Chapter Ⅶ. General Summary and Perspectives 190

Predicting the resolving power of DMS is thus still a challenge, and progresses will not only rely on theoretical modeling but also on systematic studies of the separation of isomeric species. This is especially true when modifiers are introduced in the carrier gas.

Taking the two above examples, great improvement of the resolving power can be observed using methanol as a modifier. 5,6 Nevertheless, predicting the relative negative shifts of two unresolved isomers is particularly difficult. It is for example a surprise for us that the peak ordering of Trp and Lys is different using methanol and isopropanol. It should be noted, however, that great advances have been made in the prediction of modifier effects by the group of Hopkins, based on machine learning approaches. 7 Isomeric separation can be difficult in some specific cases. For example, in the frame of the PhD thesis of C. El Saddik, succinic and methylmalonic acids were found to be very difficult to separate not only with N2 as a carrier gas, but also when a large variety of modifiers were added. 8 In the frame of the present PhD thesis, we were willing to explore the possibility of simultaneous analysis of multiple classes of metabolites. In this context, we investigated the separation of specific acylcarnitines isomers. Namely, 2 isomers of C4DC, 2 isomers of C5:1 and 3 isomers of C5 (Figure 1) were studied. Using N2 as a carrier gas, no isomeric separation could be achieved. The best results in terms of separation were obtained using acetone as a modifier (Figure 2). In the figure, DMS-MS spectra of the three sets of acylcarnitine isomers at different dry gas (N2) temperatures are reported. As reported by others, 9 a positive CV shift is observed when increasing the temperature. Acetone was used as a modifier at a flow rate of 24.45 μl/min, which corresponds to 0.1% in N2. As can be seen in Figure 2, separation of both C4DC and C5:1 isomers can be easily achieved. At 140 °C, the Rpp value is 2.4 for the C4DC isomers and they are baseline separated when the drying gas temperature varies from 140 to 300℃ with no significant change in Rpp value. The separation of the two isomers of C5:1, however, was improved gradually when increasing temperature. At temperature of 140℃, isomers of C5:1 were not separated, and a broad peak was observed. But when the temperature was increased to 200℃, two unresolved peaks were clearly observed and the Rpp value was 0.9. When increasing the temperature, positive CV shifts are observed but with no positive effect on the separation degree.

Separation of the three C5 isomers, however, was not possible under experimental conditions explored during this work. At low temperature, a broad peak is observed. A This difficulty for achieving separation may be due to the fact that they only differ by the isomeric alkyl side chain. These alkyl side-chains are non-polar, and thus do not directly interact with the carrier gas resulting in no separation. We expected that alkyl isomerism may have an indirect effect, such as a charge screening. This was suggested to be at the origin of the separation of tetraalkylammonium ions. 7 To conclude on the DMS separation, more efforts should be devoted to improve the robustness, especially when modifiers are introduced into the carrier gas. More work should also be done to derive systematic data in order not only to model but to understand the role of modifier, and to be able to predict the separation power in a specific case. Finally, improving the separation through the addition of a throttle gas was also investigated. It has an important effect, but ion loss is significant, as already reported in the literature. 10 Two approaches were used for the identification of DMS peaks. The first approach reported in chapter II is based on DMS-MS/MS-IRMPD experiments. Our group has a long experience in exploiting pulsed IR laser sources for the characterization of isomeric ions through IRMPD spectroscopy. 11,12 Structural assignment can be difficult, when multiple low energy isomers are populated, or when the lowest energy isomer cannot be easily accessed due to high energy barrier.

In such a case, different approaches have been applied for performing isomer-resolved spectroscopy. This can be achieved using two lasers, either one UV and one IR laser, 13 or two IR sources. 14 Chapter Ⅶ. General Summary and Perspectives

194

An alternative is to perform spectroscopy of mobility-selected isomers, in the UV [15][16][17][18] and IR range. [19][20][21] In this context, DMS-MS/MS-IR protocols have been used by Rizzo and coworkers, 21 our group, 4 and the group of Compagnon. 22 We proposed something similar for aminobutyric acids. Structural identification of the various isomers was achieved based on the comparison of IRMPD spectra of DMS-and mass-selected ions with IR absorption spectra of candidate structures derived from quantum chemical calculations. The novelty was that modifier-assisted DMS had to be used to separate BABA and GABA isomers.

The added value of this thesis was based on the second operation mode of the DMS-MS/MS(IRMPD) experiments. For this purpose, DMS was used in scanning mode, and identification could be achieved based on specific depletion of DMS peaks at selected IR wavelength associated with an absorption band of a specific isomer. Using this approach, we showed that overlapping bands of BABA and GABA could be fully resolved.

We showed that based on DMS-MS experiments carried out over several months, the standard deviation of CV values associated with a specific metabolite was on the order of 0.1 V, which is significantly smaller than the FWHM (0.7 V). The filtering capability of DMS when operated at a fixed CV value is not a new idea. The added value of our work was to systematically record DMS-MS spectra of AAs and related compounds over several months using a DMS device which had to be mounted and unmounted regularly.

Considering this robustness, it could thus be concluded that CV is an identifier of an isomeric metabolite.

It is often said that retention time (RT) associated with LC may vary from one to the other set of the "same" column. 23 As an alternative, it had been proposed that CCS could be an identifier of an isomeric metabolite. If the CCS is derived from a genuine drift tube instrument, CCS values can be directly measured. Otherwise, CCS has to be derived using Chapter Ⅶ. General Summary and Perspectives 195 a calibration procedure. This has been described in detail by different groups developing CCS databases. [24][25][26][27][28][29][30] In the case of DMS, rather than CV value, it would be better to provide the corresponding electric field or better the reduced electric field value. For the transferability of the CV value to be achieved, a special attention would have to be paid to the quality of the N2 carrier gas.

Our strategy was to confront results with those obtained using LC-MS/MS in the frame of clinical MD diagnosis at the hospital. Good agreement with the concentration results was observed for different plasma samples, and we showed that our DMS-MS method allows for the diagnosis of Phenylketonuria and Tyrosinemia based on the elevation of concentration of specific biomarkers. The real added value of our work was to take advantage of the robustness of the CV value to perform quantification at fixed CV value.

Using this idea, the DMS-MS results were also found very consistent with those of LC-MS/MS, thus opening the door to rapid MD screening.

We applied successfully our two DMS-MS approaches to the quantification of isobaric and isomeric species. Even when there is only partial separation, we showed that quantification could be achieved using a single DMS-MS measuring at a fixed and specific CV value.

PERSPECTIVES

More systematic DMS-MS studies are needed on different classes of metabolites. Within this thesis, we show that DMS-MS is well suited for the quantification of isomeric of amino acids and related compounds. As mentioned above, we were also interested in acylcarnitine compounds. An interesting result was that using N2 only as a carrier gas, the CV

  photonique multiple dans l'infrarouge (IRMPD) a été exploré pour la séparation et l'identification des métabolites isomères. La motivation était double. Notre groupe développe et applique la spectroscopie IRMPD depuis une vingtaine d'années pour identifier les ions isomères, et certaines limites ont été atteintes. L'idée était donc de montrer que le DMS et l'IRMPD pouvaient être combinés pour identifier les ions isomères. Ce travail a été le premier à utiliser le couplage du DMS avec notre instrument MS/MS à haute résolution à résonance cyclotronique ionique à transformation de Fourier (FT-ICR).

Figure 1 .

 1 Figure1. Genes and proteins regulatory processes, and metabolic pathways. Gene mutation and enzyme deficiency are upstream pathways. Enzyme deficiency may change the level of metabolites, which can be probed through metabolomics. Enzyme activity assays and genetic tests are then used to trace back to enzyme and gene levels.5, 6

Chapter Ⅰ .

 . Introduction to the Application of Ion Mobility Spectrometry in Metabolomics 25 the coverage of metabolites, and thousands of metabolic features are detected based on

Figure 1 .

 1 Figure 1. Schematic of ion separation using a drift tube IMS instrument. The four main components are exhibited in the figure, ion source is on the left side, drift tube region and ion detector are in the middle, IMS spectrum is on the right side. Two blue spots represent two isomeric ions. Grey arrow indicates flight direction of ions in drift tube.

Figure 2 .

 2 Figure 2. Schematic of DMS cell operating under "transparent mode". Both two electrodes were grounded in DMS off mode. Three ion trajectories are shown in red, green, and blue in the figure. Gray arrows represent carrier gas flow.

43 Figure 3 .

 433 Figure 3. Schematic of DMS setup illustrating ion transportation and three ion trajectories. One electrode is connected to an asymmetric radiofrequency (1.7 MHz) high voltage generator. Separation voltage (SV) as a function to time (t) is given in the schematic on the bottom. Ions are transported by the carrier gas (gray arrow) from the atmospheric pressure in the ion source to the lower pressure of the mass spectrometer, through the glass capillary transfer. Green ion trace represents the path of filtered ions that pass through the DMS cell. When the two transverse displacement upon high and low fields do not compensate each other (red and blue traces), ions collide with electrode walls.

44 Figure 4 .

 444 Figure 4. Schematic of asymmetric radiofrequency (RF). Blue curve is for one sinusoidal waveform with frequency of 1.7

45 Figure 5 .

 455 Figure 5. Schematic of DMS device working principle under the effect of separation voltage (SV) and compensate voltage (CV). A compensate voltage is connected to one electrode as shown in gray square on the bottom right. Blue, green, and red ions pass through the DMS electrodes and enter into the mass spectrometer. On the right side, DMS spectrum corresponds to the ion count as a function of CV value.

47 Figure 6 .

 476 Figure 6. Illustration of the three types of alpha values as a function of reduced electric field. The reduced electric field strength (E/N) corresponds to the normalized electric field (E) to the background gas density (N), and E/N is expressed

30 Figure 7 .

 307 Figure 7. A simple view of a model of clustering and de-clustering of core ions with water and N2 during an RF cycle. 31 Green ball represents the core ion, blue balls represent water molecules, yellow balls represent N2 molecules. Gray circles indicate the effective collision cross section when ions are in clustering and de-clustering states.

Figure 8 .

 8 Figure 8. Mass spectrum of a mixture of 18 AAs using the DMS transparent mode. As shown in the close-up view, only an overlapped peak is observed at m/z 147.2. Protonated Gln and Lys are not resolved using a low mass resolution instrument such as Esquire.

Figure 9 .

 9 Figure 9. DMS spectrum of two isobaric protonated compounds, Gln and Lys, at m/z 147.2.

Figure 10 .

 10 Figure 10. Illustration of our home built DMS device. The picture on the left side gives a view of installed DMS chamber. The figure on the right side is a closer view of DMS device displaying the two red cables for the electric connection of the DMS electrodes.

Figure 11 .

 11 Figure 11. The picture on the left side shows the two electrodes screwed within the (dark) insulating cyclindrical part.

Figure 12 .

 12 Figure 12. Cross-sectional view of DMS device and schematic of gas flow for ion transportation from the ion source to DMS cell and the counter flow of curtain gas. Solid arrows indicate each component of mounted DMS device, dash arrows indicate directions of carrier gas and curtain gas for ions transportation in DMS cell.

Chapter Ⅱ. Ion Mobility Spectrometry Methodology 54 Bruker

 54 Apex IV Hybrid FT-ICR. A hybrid Fourier transform ion cyclotron resonance (FT-ICR) tandem mass spectrometry, 7 Tesla APEX-QE series from Bruker Daltonics, was first utilized. FT-ICR mass spectrometers are high mass resolution instruments allowing in particular resolution of isobaric ions. This FT-ICR is equipped with an Apollo II ESI source, within which our homebuilt DMS device has been mounted. The configuration of DMS device hyphenated to FT-ICR instrument is illustrated in Figure 13.

Figure 13 .

 13 Figure 13. Schematic of ions transportation passing through the DMS system from ambient pressure to low pressure of our FT-ICR mass spectrometer system. Blue spots represent analyte ions produced in ion source. The order of magnitude of pressure values at each stage are indicated on the bottom.

Figure 14 .

 14 Figure 14. Schematic of working principle of FT-ICR detection. On the left side, in a view perpendicular to the magnetic field, are represented two pairs of electrodes: excitation plates (in dark blue) and detector plates (in purple). Blue circle symbolizes the ions trapped in the magnetic field and excited by the RF excitation voltage. Black arrow represents rotation direction of ions. Blue sine waves represent the induced current. Frequency spectrum in the middle is extracted from induced signal after Fourier transformation. Spectrum on the right side is the mass spectrum.

Figure 15 .

 15 Figure 15. Schematic of DMS coupled to our quadrupole ion trap mass spectrometer. Blue spots represent analyte ions from their production in the ion source through their detection. The grey region represents low pressure region of the tandem mass spectrometer system.

Figure 16 .

 16 Figure 16. Schematic of the exterior of DMS device. DMS electrodes and the hole for inducing throttle gas were indicated by red arrows in the figure.

58 Figure 17 .

 5817 Figure 17. DMS spectra for 4 AAs recorded at different throttle gas flow rates. Panel (a) exhibits DMS peaks for protonated leucine, phenylalanine, tryptophan, and arginine without throttle gas. FWHM value is indicated next to each DMS peak. Panels (b) and (c) show narrowed peaks observed for throttle gas at flow rate of 0.5, and 0.75 l.min -1 , respectively.

  4 mm have been made, and the combination of the two factor (DV and gap) on the CV values of acylcarnitines has been evaluated. The dispersion plots of Valeryl-L-carnitine for the three electrode gap values are presented in Figure 18. In this figure, the compensation field (CF=CV/gap) is plotted as a function of the dispersion field (DF=DV/gap) value. As can be seen in this figure, the three curves are parallel, and for a given DF value, the measured CF values for the three gap values are similar. Nevertheless, a systematic trend is observed, and for a given DF value, the CV value tends to be positively shifted when the gap increases from 0.7 mm to 1 mm and 1.4 mm.

Figure 18 .

 18 Figure 18. Dispersion plots for protonated Valeryl-L-Carnitine recorded for three gap values (1.4, 1, and 0.7 mm) between the two DMS electrodes. Compensation field (CF=CV/gap) is plotted as a function of dispersion field (DF=DV/gap), and electric field are expressed in V/mm units.

Figure 19 .

 19 Figure 19. Effect of using MeOH as on protonated Tryptophan and Lysine analyzed by DMS-MS. Green dotted curve is for Tryptophan and orange dotted curve is for Lysine. In panel (a) DMS spectrum for Tryptophan and Lysine acquired using only N2 as carrier gas without MeOH is presented. In panel (b) DMS peaks for these two AAs after inducing MeOH into carrier gas as modifier at percentage of 0.009% is presented. In panel (c) two DMS peaks after increasing percentage of MeOH to 0.026% is presented. The values of Rpp between Tryptophan and Lysine were indicated next to the peak under different conditions.

  20. CV values of 7 AAs were observed to be shifted to more positive values when increasing gas temperature from 150 °C to 230 °C. As can be observed from the figure, the CV values of these AAs were less dispersed at the maximum temperature of 230 °C.

Figure 20 .

 20 Figure 20. Dispersion plots where CV values are plotted versus dry gas temperatures. Seven AAs are considered: tryptophan, arginine, phenylalanine, methionine, lysine, isoleucine, and proline (Trp, Arg, Phe, Met, Lys, Ile, and Pro).

  of AABA, BABA, and GABA isomers at 10 -4 M were prepared by diluting from stock solutions at 10 -2 M using methanol/water (50/50, v/v) and 5% acetic acid. Similarly, the mixture solution of three isomers was formulated at the final concentration of 10 -4 M for each of them. Protonated ABA ([C4H10NO2] + , m/z 104.071) were generated by electrospray ionization (ESI). The sample was pumped into the ESI emitter at a flow rate of 70 μL.h -1 . The temperature and flow rate of the desolvation gas are set using the Bruker control software at 150 °C and 6 L.min -1 , respectively. All the experiments were performed on a 7 Tesla APEX-Qe (Bruker Daltonics), a hybrid Fourier transform ion cyclotron resonance (FT-ICR) tandem mass spectrometer (MS/MS).

Chapter Ⅲ .

 . Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 73

(

  FWHM) are obtained from Gaussian fits of the experimental DMS spectra. Uncertainties on the CV and FWHM values, estimated with the standard deviation over several experiments, are on the order of magnitude of the bin width of the DMS spectra, i.e. 0.1 V.

Figure 1 .

 1 Figure 1. Schematic of working principle of DMS-MS/MS(IR) experiment. DMS-selected ions are trapped in the ICR cell, and irradiated with the IR beam (red arrow) of the CLIO FEL at a specific wavenumber 𝜈 ̃2. The mass spectrum presented parent and fragment corresponding to the red ions. The DMS spectrum only shows the depletion of red isomer.

Figure 2 .

 2 Figure 2. DMS spectra of an equimolecular mixture of AABA, BABA, and GABA. All spectra were recorded with a dispersion voltage value of 1.8 kV. The DMS spectrum recorded in the absence of modifier is shown in panel (a). Modifier-assisted DMS spectrum recorded with a methanol at a 1.5 μL.min -1 flow rate and DV of 1.8 kV is given in panel (b).

Figure 3 .Figure 4 .

 34 Figure 3. DMS spectrum of an equimolecular mixture of AABA, BABA, and GABA along with those of the individual isomers. The DMS spectra recorded in the absence of modifier are shown in the left panels (a). Modifier-assisted DMS spectrum recorded with methanol at a flow rate of 1.5 μL.min -1 and DV of 1.8 kV are given in the right panels (b). The DMS spectra of protonated AABA, BABA, and GABA are shown in red, green, and blue, respectively. The same color code is used for the three Gaussian fit of the experimental data (black dots). All data were recorded with a DV value of 1.8 kV, with a methanol flow rate of 1.5 μL.min -1 . All extracted ion chromatograms (XICs) are normalized to the maximum.

  selected isomers using IRMPD spectroscopy. DMS-MS/MS (IRMPD) experiments were performed with the DMS device used in a selective mode, i.e. at a fixed CV value.The IRMPD spectrum of the modifier-assisted DMS-and mass-selected m/z 104 ions is thus recorded at three fixed CV values (-24.6, -20 and -16.9 V) corresponding to the maximum transmission of protonated ABA isomers. Identification of each DMS peak can Chapter Ⅲ. Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 80 then be done by comparing experimental IRMPD spectra with calculated IR absorption spectra of the three protonated isomers. IRMPD of the modifier-assisted DMS-and mass-selected ions are shown in Figure 5. As the C=O stretching region is the most structurally diagnostic region, only this region is shown in this Figure. The lowest energy optimized structure of the three isomers are also given in Figure 5.

Figure 5 .

 5 Figure 5. IR spectra of protonated AABA, BABA, and GABA isomers. DMS-selection of m/z 104 ions was achieved using modifier-assisted DMS (methanol at a 1.5 μL.min -1 flow rate). IRMPD spectra recorded using a fixed CV value of -24.6, -20.0, and -16.9 V are shown in panels a), b), and c), respectively. The theoretical spectra are given in black as stick bars. Optimized structures of protonated AABA, BABA, and GABA. Selected bond lengths are given in Angstrom unit. The dotted line in red, gray, and blue corresponding to wavenumber of 1780, 1730, and 1690 cm -1 , respectively, which were used to selectively deplete specific one or two isomers in the C=O stretch region during experiments.

  Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 81 IRMPD spectroscopy combined with quantum chemical calculations allows for structural assignment of DMS peaks.

  H … (carbonyl)O bond distance varies from one isomer to another: 1.95, 1.77, and 1.71 Å for AABA, BABA, and GABA, respectively. The carbonyl C=O bond distance is also sensitive to the HB distance.The trend observed for the carbonyl C=O frequency is consistent with the evolution of their HB and C=O bond distances associated with the cyclic HB feature. The C=O stretching frequency was found to be 1775, 1730, and 1710 cm -1 for protonated AABA, BABA, and GABA, respectively. The shorter is the (ammonium)H … (carbonyl)O distance, the stronger is the HB involving the carbonyl C=O, and the larger is the observed frequency red-shift.Another way for assigning DMS peaks is to perform selective depletion of DMS peaks using IR activation. For this purpose, DMS is used in scanning mode by recording DMS-MS/MS(IRMPD) spectra at a selected wavenumber. Protonated AABA, BABA, and GABA can be characterized by monitoring the corresponding specific fragment at m/z 58.07, 44.05, and 87.05, respectively.

Figure 6 .

 6 Figure 6. DMS-MS/MS(IRMPD) spectra of an equimolecular mixture of AABA, BABA, and GABA recorded under five different conditions: laser off (a), and laser turned on at 1372 (b), 1690 (c), 1730 (d), and 1780 cm -1 (e). In each panel (ae), in addition to the extracted ion chromatograms (XIC) of the parent m/z 104 ion (left), the XICs of the four fragments are shown on the right. Specific fragments of protonated AABA, BABA, and GABA are shown in red (m/z 58.07), green (m/z 44.05), and blue (m/z 87.05).

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 95 4.2 MATERIALS AND EXPERIMENTAL METHODS Methanol and water (50:50, v:v) were used as solvent acidified with formic acid (0.5%). Methanol was purchased from Merck KGaA (Darmstadt, Germany) and formic acid from Fisher Scientific (Hampton, New Hampshire, USA). Both are reagent grade. Deionized water was obtained using Millipore Direct-Q 3 UV (Lilipore corporation, Burlington, Massachusetts, USA). Certified graduated pipettes were used throughout for sample preparation.

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 97 DMS spectra are generated by plotting extracted ion chromatograms (XIC) as a function of CV value, which is scanned using a 0.1 V increment. DMS peak centroid (CV) and full width at half-maximum (FWHM) are extracted by fitting each DMS spectrum using Gaussian functions. The fitting procedure is performed using a homebuilt R language based program. 29 DMS spectra of components of the standard mixture and dispersion plots of individual AA. Unless otherwise stated, DMS spectra presented here were recorded with a DV value set to 1.8 kV. All the CV values corresponding to optimal transmission of a particular metabolite given in the text refer to these conditions. DMS spectra were recorded at different DV values ranging from 1.2 to 2.0 kV. Using the CV values derived from the fit procedure of each DMS peak, dispersion plots,

Figure 1 .

 1 Figure 1. Differential ion mobility data for the 20 protonated AAs recorded at different dispersion voltage (DV) values using N2 as carrier gas. (a) DMS dispersion plots, or evolution of CV as a function of DV; DMS spectra for each individual AA at DV = 1.6, 1.8, 2.0 kV are given in panels (b). A CV value of 1 V corresponds to an electric field of 1.43 kV.m -1 or ~0.07 Td. To facilitate visualization, each DMS peak is individually normalized.

  Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 100 interaction between ions and the carrier gas. Protonated Asn, Asp, Thr, Cys, Pro, Ser, Ala and Gly are associated to type A ions, for which ion-molecule clustering and declustering are supposed to occur at low and high electric field, respectively.

8 ,

 8 and 2.0 kV, are given in Figure 1b. Regarding the width (FWHM) of the DMS peaks, two observations can be made. First, it does not vary with the DV value. Second, a nearly linear increase (R 2 =0.88) of the FWHM value with m/z is observed, the values for the AAs ranging from 0.52 (Gly, m/z 76) to 0.88 V (Trp, m/z 205). The DV of 1.8 kV was thus used in the following experiments considering both the peak capacity and the peak width. Separation of isobars and isomers of small molecules such AAs and related compounds has been investigated by several groups using drift-tube or related IMS instrument. DMS and drift-tube IMS results are discussed in the following. Collision cross section (CCS)

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 101 along with CV values derived using our DMS-MS experiments. As can be seen in this table,

  , CV and CCS values for a set of protonated molecules are plotted as a function of m/z. As can be seen in this Figure, both CV and CCS increase as a function of m/z. Protonated Glycine which has the smallest m/z 76 value was located at most negative CV position -10.7 V and has the smallest CCS value of 117.4 Å 2 . 40 Conversely, protonated Tryptophan with the largest m/z 205 value was found to correspond to most positive CV value of 0.2 V and the largest CCS value 146.5 from the same CCS database.40

Figure 2 .

 2 Figure 2. CV and CCS values for a selected set of AAs and related species plotted as a function of m/z (See list in Table

  8 at 1.2 kV to 23.8 at 2.0 kV. The evolution of Pc (black curves) and Rpp (red curve) as a function of the DV value is presented in Figure 3. The best separation performance is observed for DV value of 1.8 kV except for few AA pairs. Protonated Lys (B type) and protonated Trp (C type) are poorly separated at 1.8 kV (Rpp=0.1), while a better separation is observed at lower DV values (for instance, Rpp=0.48 at DV=1.2 kV). In a consistent manner, the fraction of the pairwise combinations of protonated AAs which are baseline resolved (Rpp=1.5) increases from 12% at DV=1.2 kV to 70% at DV=2.0 V. It can thus be concluded that these Rpp and Pc values are very close to those estimated (Pc~17

Figure 3 .

 3 Figure 3. Fraction of resolved pairs for every pairwise combination of the 20 protonated AAs (black lines, circle marks, left scale) and peak capacity (red line, square marks, right scale) as a function of the dispersion voltage (DV). The different black lines correspond to five threshold values, Rpp greater than 0.61, 0.83, 1, 1.23 and 1.5 which represent 10% of separation, a half-height separation, 75%, 90% of separation and baseline separation, respectively.

Chapter Ⅳ . 107 Figure 4 .

 .1074 Figure 4. DMS data recorded for a set of standards (AAs and related compounds) in water:methanol and for a plasma sample (P1) containing IS. The corresponding 2D plots, with m/z as a function of CV, are given in panels (a) and (b), respectively. Extracted Ion chromatograms (XIC) of a selected set of metabolites of interest are given in panel (c) where data for the plasma (red) and standards in water:methanol solution (black) are superimposed. Peak assignment in the XIC panel (c) is provided. 3-letter nomenclature symbols are used for AAs, as well as for Aminobutyric acid (ABA),Citrulline (Cit), Ornithine (Orn), and Sarcosine (Sar). Peaks corresponding to post-DMS (in-source) fragments or also first and second isotopic peaks are labelled with an "#" and an "&" symbol in blue, respectively. Unassigned DMS peaks are symbolized by a "*" mark in blue. A CV value of 1 V corresponds to an electric field of 1.43 kV.m -1 or ~0.07 Td.

  Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 108

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 110

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 111 contributions of the first isotopic peak of protonated leucine and isomers (m/z 132) is found in the m/z 133 XIC.

  Figure 5 can be found in Table3. The maximum standard deviations were found

Figure 5 .

 5 Figure 5. 6 DMS spectra recorded for proline (Pro), valine (Val), glutamine (Gln), lysine (Lys), phenylalanine (Phe) and arginine (Arg) over a period of 6 months. Experiments were performed with samples corresponding to spiked pool of plasmas (filled circles) and standard solution of AAs and related compounds in water:methanol solution (open circles).A CV value of 1 V corresponds to an electric field of 1.43 kV.m -1 or ~0.07 Td.

Figure 6 .

 6 Figure 6. Mass spectra recorded with DMS-off (black) or DMS-on (color) for a pool of plasma samples P1. (a) DMS off. (b) DMS on, and CV set at -5.3 V (red) and -3.7 V (blue) for optimal transmission of protonated proline (m/z 116) and valine (m/z 118), respectively. (c) Mass spectra integrated over the DMS peaks of protonated valine (blue) and proline (red). A CV value of 1 V corresponds to an electric field of 1.43 kV.m -1 or ~0.07 Td.

Chapter Ⅳ .

 . Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 114

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 127

First, absolute quantification

  of AAs and related compounds in plasma samples using a DMS-MS protocol is presented. Robustness of the results is an important issue for convincing the community of the perspective of using DMS for the clinical diagnosis of MDs. For this purpose, quantification experiments were carried out on plasma samples from Robert Debré hospital (Paris) which had been analysed in the context of routine MD diagnosis. Concentrations derived with our protocol are thus compared to those obtained with an LC-MS/MS protocol specifically designed for clinical diagnosis at the hospital, validated according to the ISO 15189 standards.

  Robert Debré University Hospital (Paris, France), and in compliance with French Public health regulations (Code de la Santé Publique-Article L1121-3, amended by Law n°2011-2012, December 29, 2011-Article 5). Concentrations of the AAs of interest in these plasma samples have been determined by a LC-MS/MS method validated according to the ISO 15189 standards for clinical diagnosis. These quantification experiments were performed in an accredited university hospital laboratory where MD diagnoses are routinely performed. In brief, after butylation, compounds are separated by reverse phase liquid chromatography, then ionized by electrospray in positive mode and identified by multiple reaction monitoring (MRM) using MS/MS.

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 131 from healthy volunteers (P1). Compared to the same P1 reference, the concentration of the biomarker in plasma P2 (Phenylalanine) and P3 (Tyrosine) is an order of magnitude larger.

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 132

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 133 transferred to an Eppendorf tube. Then, 13.8 µL of the supernatant was mixed with 25 µL of the IS mixture, and diluted in a water:methanol (50:50, v:v) solution containing standards and 1.25 µL of formic acid to reach a total volume of 250 µL.

Figure 1 .

 1 Figure 1. Scheme of the protocol of plasma sample preparation. Plasma is indicated in red inside the Eppendorf tube on the left with sulfosalicylic acid on the bottom in light blue. The tube on the right is the final sample, which is composed of deproteinized plasma in red and internal standard in blue and water/methanol in light blue. The final dilution factor of plasma sample is 20.

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 134 to each targeted metabolite and its corresponding IS were analyzed. A data analysis program based on R language was specifically developed for the DMS peak simulation and quantitative data processing. The programming was conducted by Philippe Maître and Pascal Pernot who has a lot of experience with data

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 135

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 136 the performance of the analysis (2D fit, 1D fit with fixed m/z or CV value, for example) for a given set of MS and DMS data.

(

  Tyr). An ion chromatogram was recorded during 7 minutes and the XIC at m/z 182 is shown in Figure 2. In practice, the chromatogram mode is used, and we wait for a signal of the Bruker software control in order to start the DMS scan mode, where the CV starting and step values, generally ±0.1 V, are given. An output MS trigger signal is used to trig the change of CV value. Few pairs of time and CV values are reported in the labbook, and the first pair (t0 and CV0) is used by our R program. In the present case, DMS operation started at CV0 set to 4 V at a t0 time of 0.08 minutes. It is important to stress that the ICC mode can be used, where the time Chapter Ⅴ. Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 137 step between two consecutive MS/MS acquisition may significantly vary from one step to another.

Figure 2 .

 2 Figure 2. Extracted ion chromatogram for m/z 182, [Tyr+H] + (left) and corresponding DMS spectrum (right).

  Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 138 procedure can be restricted. Typically, some shift of the m/z value may occur, but to a small extent. Similarly, considering the robustness of the CV value, variations of CV values are typically restricted to ±0.1 V. The FWHM range is found to be quite constant, and essentially depends on the glass capillary transfer used for the experiment. Its variations can thus also be restricted. In the same vein, either the CV or the m/z value can be fixed, and 1D fit can be performed, as illustrated below.A 2D fit in the (m/z, CV) dimensions is illustrated in Figure3for protonated Tyr in a plasma sample. A 2D representation of the DMS-MS peak is shown on the left panel. The data are coded in color intensity from pale yellow to red on the map. Two solid red lines correspond to the reference (input) values of CV (-1.3 V) and m/z (182.2 Da). The horizontal and vertical dotted red lines correspond to the Gaussian centroids, i.e. to the output m/z 182.164 Da and -1.473 V values. On the right panel, a 1D representation of the experimental data (green) and fitted curve (blue) as a function of CV is given with m/z fixed to its optimized value. All the fitted parameters are given next to the 1D representation. The FWHM in m/z dimension is 0.235, and 0.8 V in the CV dimension. The integrated peak area is 66.5.

Figure 3 . 2

 32 Figure 3. 2D simulation of DMS-MS peak for targeted protonated Tyr using our R-written code. Solid red lines on the map on the left indicate the input (reference) CV and m/z values, while dashed lines are the output values after peak fitting. The spectrum on the right side is the output figure of DMS spectrum, where green curve is the experimental spectrum and the blue curve is a 1D representation of the 2D Gaussian fitted spectrum. Also given in the right panel are the output values of the 2D Gaussian fit: m/z, CV, FWHM in the m/z and CV dimensions, and peak area.

Figure 4 .

 4 Figure 4. The CV profile for the DMS peak of protonated Tyr by 1D simulation at fixed m/z.

Figure 5 .Fixed

 5 Figure 5. The m/z profile for the peak of Tyr by 1D simulation with fixed CV.

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 142 "CAA/CIS", as shown in Figure 6. The calibration curve is generated by taking the mean value of different replicates. It corresponds to the straight line in red in Figure 6, i.e. the linear regression with a zero-intercept which is used to estimate the concentration of target AA. Solid line in green is the linear regression with intercept as a variable. The corresponding data are used to derive the LOD values shown as a vertical yellow dotted line. For each linear regression, the 95% confidence interval is represented by two dotted lines.

Figure 6 .

 6 Figure 6. Quantification plot of area ratio of AA and IS as a function of CAA/CIS as generated by R-written program. Blue spots represent different data sets with error bar. Straight line in red is the linear regression with zero-intercept.

Chapter Ⅴ . Figure 7 .

 .7 Figure 7. Calibration curves for AAs spiked into the pool of plasmas. DMS-MS analyses were performed in the dynamic linear range of 1-200 µM. Error bars correspond to the standard deviation of 5 independent measurements in different days over 3 months.

  (a) Range of R 2 values determined for each linear regression; (b) LOD values, defined as 3*SD(intercept)/slope using the linear regression method, are the mean values of each linear regression. The validated DMS-MS protocol was applied to the quantification of AA concentrations in four plasma samples. To demonstrate the feasibility of the DMS-MS protocol, concentration values of metabolites were measured in a pool of plasmas from healthy patients (P1) as well as plasmas from patients with a known metabolomics disease (P2 and P3). Five sets of calibration experiments obtained over 3 months were used for linear calibration. After statistical processing, an average calibration curve was derived for each metabolite of interest. Concentration levels of metabolites were then calculated based on the averaged calibration curves. Results for 13 AAs and related compounds were compared to results obtained by LC-MS/MS as shown in Figure 8. Quantification results obtained with LC-MS/MS for a purpose of clinical diagnosis of MD at the hospital are given in blue bars. Error bars for the LC-MS/MS protocol were derived for each compound from the corresponding inter-day standard deviation (SD) of an equivalent concentration Chapter Ⅴ. Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 146 calculated from plasma controls daily analysed over a period of 5 months. For the present DMS-MS protocol (in red), the error bars correspond to the standard deviation over the five series of inter-day experiments.

Figure 8 .

 8 Figure 8. Concentration (µM) of 13 selected metabolites in three plasma samples: a) pool of plasma samples (P1) from healthy volunteers, b) Phenylketuniria plasmas (P2), and c) Tyrosinemia plasmas (P3). DMS-MS experiments (red bars) were performed on different days, and standard deviations were indicated as error bars. LC-MS/MS data are shown as blue bars, and error bars correspond to relative standard deviation (RSD) calculated over 5 months.

  Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 147 Inter-day precision. Inter-day quantification experiments were also carried out on two plasmas from MD patients. Results for plasmas P2 (Phenylketonuria) and P3 (Tyrosinemia) are given in panel (b) and (c) of Figure 8, respectively. Overall, similar quantitative results as for plasma P1 can be observed, except for Phe and Tyr in panel (b) and (c), respectively. As in the case of the healthy plasma sample, their concentrations are overestimated using our DMS-MS protocol as compared to LC-MS/MS.

Chapter Ⅴ . Figure 9 .

 .9 Figure 9. Concentration (µM) of selected metabolites in a random plasma (P4). DMS-MS data (red bars) correspond to the mean value derived from 6 intra-day experiments. Standard deviations are indicated as error bars. LC-MS/MS data are shown as blue bars, and error bars correspond to RSD calculated over 5 months.

  a random plasma. Simulation were first performed on data sets associated with a random plasma. Six DMS-MS measurements were made, and a representation of the results associated with the two fitting procedures is proposed in Figure10. Fourteen AAs are related compounds are considered here. For each metabolite, results associated with simulation of the fixed CV experiments are given in blue, against those obtained using the reference 2D fit given in red. CV values used for the fixed CV 1D fit correspond to the mean values derived from 2D fits of all plasma samples.

Figure 10 .

 10 Figure 10. A boxplot representation of the concentration values of 14 metabolites derived from 2D and 1D (fixed CV) fitting procedure of the same data sets. Concentration of each metabolite is normalized to the median value of 2D fit. The red (blue) box corresponds to concentrations derived by 2D (1D) fit. The maximum and minimum as well as outliers are indicated in the box by lines and circles, respectively. Error bars were calculated from 6 replicates within one day.

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 151 Mean concentration values and corresponding standard deviation values for the 14 metabolites are given in Table

Chapter Ⅴ .

 . Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 152 CV)). As mentioned above, 1D fit were performed at two different specific CV values for the metabolite and its corresponding IS. The two sets of results are shown in Figure 11.

Figure 11 .

 11 Figure 11. Histogram representation of the concentration (μM) of 16 metabolites in the pool of plasmas using a single set of DMS-MS data. Concentrations derived using a 2D fit are presented in red, those derived using a 1D (fixed CV) fit are given in blue. DMS-MS error bars correspond to the standard deviation over five independent measurements.

Figure 12 .

 12 Figure 12. Extracted ion chromatograms of 14 AAs associated of the DMS-MS analysis of two plasma samples. Panel (a) represents the spectrum for pool of healthy plasmas; panel (b) represents the spectrum for a plasma sample from phenylketuniria patient. The peak for Phe is indicated in red.

Figure 13 .

 13 Figure 13. Histogram of concentrations (μM) of 16 metabolites in Phenylketuniria (panel (a)) and Tyrosinemia (panel (b)) plasma samples derived using two DMS peak fitting procedures of the same set of data. Results corresponding to 2D (1D (fixed CV)) fit are given as red (blue) bars. Error bars for each metabolite correspond to standard deviation of 4 independent measurements over 3 months within the same plasma sample.

FixedFigure 15 .

 15 Figure15where the area ratios of 8 targeted AAs against IS are given. For each compounds, three experiments were conducted. First, scan CV DMS-MS experiments were performed. Two peak fittings were performed assuming a 2D Gaussian profile (blue bars) and a 1D Gaussian profile (red bars). In the latter case, the peak fittings of the AA and its IS were each performed at their specific CV value. As found for another set of data and discussed above, the results obtained using a 2D or a 1D fit of the DMS-MS scans are very similar.

  (panel (b)) against those obtained in standard water:methanol solution (panel (a)). Overall, there is a larger dispersion of the results when plasma samples rather than standard solutions were analyzed. This is reflected by the error bar values which are significantly larger in the former than in the latter case. When DMS-MS data associated with a CV scan were considered the ratios calculated by performing a 1D fit at fixed CV (red bars) and those calculated using a 2D fit (blue bars) are much closer in the case of standard solution (panel (a)) than of plasma samples (panel (b)). If the error bars are considered, however, it can be assumed that DMS-MS experiments can be restricted to 16 CV values, except in the case of Arg. The same conclusions can be drawn when comparing data associated with experiments at fixed CV (yellow bars, fixed CV).

Chapter Ⅴ . Figure 16 .

 .16 Figure 16. Histogram of area ratio of targeted AAs versus IS for 8 AAs within standard solution in panel (a) and in spiked pool of plasmas in panel (b). Blue (orange) bars corresponding to results of 2D (1D) fitting analysis of scan CV experiments. Ratios represented as yellow bars are obtained from two DMS-MS experiments, one at CV value of AA, the other from the corresponding IS.

  Quantification of Amino Acids and Related Compounds in Plasmas by DMS-MS 160 the four plasmas, the concentrations of metabolites derived by DMS-MS are in good agreement with those obtained through routine LC-MS/MS diagnosis at the hospital. In particular, two plasmas from Phenylketonuria and Tyrosinemia patients are estimated within the overlapped concentration range of LC-MS/MS. The abnormal elevation of biomarkers Phe and Tyr can be accurately detected with less time cost at fixed CV.

Figure 1 .

 1 Figure 1. Both cis-and trans-OH-Pro are metabolites which can also be found in plasma samples. Using low-resolution MS instruments, Leu and OH-Pro isomers cannot be resolved. This is of high importance since OH-Pro concentration may increase significantly in the cases of hydroxyprolinemia patients.

→68

  transition is used for quantification. 31 189 CHAPTER Ⅶ. GENERAL SUMMARY AND PERSPECTIVES 7.1 GENERAL SUMMARY We shared and still share the observation made by Wernisch et al: "Despite several success

  measured concentrations with those determined by LC-MS/MS on plasma samples collected for clinical diagnosis of metabolic diseases (MD). Our concerns were the robustness and speed of our DMS-MS protocol, in the perspective of clinical diagnosis, and emergency diagnosis in particular.

Chapter Ⅶ. General Summary and Perspectives 191 Figure 1 .

 1911 Figure 1. Chemical structures of isomeric acylcarnitine compounds. 2 isomers of C4DC: (a) Methylmalonyl-L-carnitine, (b) Succinylcarnitine; 2 isomers of C5:1: (c) Tiglyl-L-carnitine, (d) 3-Methylcrotonyl-L-carnitine; 3 isomers of C5: (e) Valeryl-L-carnitine, (f) Methylbutyryl-L-carnitine, (g) Pivaloyl-L-carnitine.

Figure 2 .

 2 Figure 2. DMS spectra of a mixture of 3 sets of isomers of acylcarnitines (see Figure 1) in H2O/MeOH solution at different dry gas temperatures. Peaks for 2 isomers of C4DC, 2 isomers of C5:1, and 3 isomers of C5 are indicated in blue, red, and black, respectively.

Chapter Ⅶ .

 . General Summary and Perspectives193global positive CV shift is observed, but no effect on the separation of the three isomers.

10 LIST OF ABBREVIATIONS AND SYMBOLS

  La métabolomique est l'étude à grande échelle des métabolites impliqués dans les voies métaboliques à l'échelle cellulaire. Elle participe à une caractérisation biochimique plus complète de l'ensemble des métabolites. La métabolomique constitue donc un outil essentiel pour la compréhension des processus biologiques des maladies et a largement contribué à la découverte de biomarqueurs ces dernières années. Dans ce travail, nous nous sommes intéressés à l'évaluation d'une Aujourd'hui, le dépistage de biomarqueurs permet un diagnostic précoce de nombreuses maladies, et peut également répondre aux besoins de diagnostic d'urgence. L'identification des métabolites fournit aussi une interprétation biologique des maladies métaboliques. Deux approches complémentaires sont utilisées pour la découverte de biomarqueurs dans l'analyse métabolomique : l'approche non ciblée et celle ciblée. L'approche non ciblée consiste à déterminer la différence entre les échantillons pour sélectionner les biomarqueurs candidats. Les approches du profil métabolique et de l'empreinte digitale fournissent des données pour la sélection des biomarqueurs. Ensuite, la métabolomique ciblée est utilisée pour identifier et quantifier les métabolites sélectionnés. Les propriétés biochimiques des biomarqueurs sont expliquées et validées sur la base de la connaissance des voies métaboliques.

	iPro RESUME EN FRANÇAIS Isopropanol	v	Velocity
	IR	Infrared	V	Volt
	IRMPD	Infrared multiple photo-dissociation	XIC	Extracted ion chromatograms
	MD IS	Metabolic diseases Internal standard	kg.m -3 μL.h -1	Kilogram per cubic metre Microliters per hour
	°C ISP	Celsius Isopropanol	kV	Kilovolt
	µM Gly	Micromole Glycine	l.min -1 Gly-13C2	Liter per minute Glycine-13C2
	1D α-Ala	One-dimensional α-Alanine	LC α-Ala-13C2	Liquid chromatography α-Alanine-13C2
	2D β-Ala	Two-dimensional β-Alanine	LOD	Limit of detection
	Å AABA	Angstrom α-Aminobutyric Acid	M	Mole
	AAs BABA	Amino acids β-Aminobutyric Acid	m/z	Mass-to-charge ratio
	BCAAs GABA	Branched chain amino acids γ-Aminobutyric Acid	MeOH	Methanol
	CCS	Collision cross section	MeV	Mega electron-volt
	CE	Capillary electrophoresis	MHZ	Megahertz
	CID	Collision induced dissociation	mm	Millimetre
	CV	Compensation voltage	MS	Mass spectrometry
	Da	Dalton	ms	Microseconds
	DBS	Dried blood spots	MS/MS	Tandem mass spectrometry
	DFT	Density functional theory	MSUD	Maple syrup urine disease
	DMS	Differential mobility spectrometry	N2	Nitrogen
	DTIMS	Drift time ion mobility spectrometry	NBS	New born screen
	DV	Dispersion voltage	NMR	Nuclear magnetic resonance
	E	Electric field	Pc	Peak capacity
	ESI	Electro spray ionization	PKU	Phenylketonuria
	EtOH	Ethanol	RF	Radiofrequency
	f	Frequency	Rpp	Peak to peak resolution
	FAIMS	Field Asymmetric Ion Mobility Spectrometry	s	Second
	FT-ICR	Fourier transform ion cyclotron resonance	SD	Standard deviation
	FWHM	Full width at half maximum	SLIM	Lossless ion manipulations
	GC	Gas chromatography	SV	Separation voltage
	H2O	Water	td	Drift time
	He	Helium	Td	Townsends
	HILIC	Hydrophilic interaction liquid chromatography	TIMS	Trapped IMS
	HPLC	High-performance liquid chromatography	TOF	Time-of-flight
	ICC	Ion charge control	tR	Residence time
	IM-MS	Ion mobility mass spectrometry	TWIMS	Traveling Wave IMS

Métabolomique.

nouvelle méthode d'analyse des biomarqueurs pour le dépistage de maladies métaboliques héréditaires (MD). Également appelés erreurs innées du métabolisme, les MD sont causées par des défauts génétiques et peuvent être diagnostiquées par la mesure quantitative de métabolites ciblés dans les fluides biologiques.

Le détournement des voies métaboliques est l'une des façons dont les cellules adaptent leu métabolisme en réponse à un stimuli ou aux variations de leur environnement. La dérégulation des voies métaboliques peut ainsi conduire à des maladies. Les concentrations de métabolites sont sensibles aux changements d'activité des enzymes dont ils sont les substrats ou produits. Le grand nombre de voies métaboliques, et de métabolites correspondants, est à l'origine du défi analytique. Plus de 2 000 métabolites sont actuellement inclus dans les cartes des voies métaboliques. Afin d'utiliser les métabolites comme biomarqueurs diagnostiques ou prédictifs, une condition préalable est d'identifier leurs rôles dans les voies métaboliques. Ces métabolites sont de petites molécules ayant poids moléculaire <1500 Da, chimiquement diversifiées, ils peuvent être des hydrates de carbone, des AA, des acylcarnitines, des lipides, des nucléotides ;..... Les AA, les acylcarnitines et les acides organiques sont des biomarqueurs couramment ciblées pour de nombreuses maladies.

Le diagnostic des maladies par la métabolomique.

  have been used in this field of metabolites profiling for decades. EI source generate highly reproducible and informative fragmentation patterns. However, GC-MS analysis is not compatible with thermolabile and non-volatile compounds. The atmospheric pressure

ionization (API) such as electrospray ionization (ESI) source while has extended the range of detected molecules to peptides, protein and lipids. However, ions produced in API sources only provide limited structural information. Either MS/MS or sequential MS n experiments for further structural characterization of compounds are necessary. High resolution mass spectrometry (HRMS) is one of the most used tools for metabolome analysis, including time of flight (TOF), fourier transform ion cyclptron resonance (FT-ICR)

  using GC-MS/MS, but at the cost of a long time of analysis, which is further increased by the derivatization. Metabolomics analysis using LC-MS/MS can be faster. Nevertheless, since only a set of molecules with comparable polarities can be separated under a given

set of chromatographic conditions. Therefore, diagnosis relies on multiple GC-MS and LC-MS/MS protocols. Typically, for most MDs the classical metabolic workup includes organic acids profile in urine by GC-MS (4 to 8 hours), plasma amino acids by LC-MS/MS (1 to 3 hours), and at least blood acylcarnitines by LC-MS/MS (2 to 4 hours). One of the main drawbacks of LC is that it requires handling large volumes of solvents which can be dangerous and/or difficult to recycle. Furthermore, while isobar resolution can be achieved with high resolution MS/MS, isomer separation often remains a challenge.

27, 28 1.4 IMS AS AN ADVANCED ANALYTICAL TECHNIQUE Many

different traditional and emerging analytical techniques have been applied for metabolomics studies. In Table

1

, the advantages and disadvantages of different analytical techniques commonly used in metabolomics are listed. NMR spectrometry can be powerful in determining structural configurations of isomers, but it is limited in biological samples due to the low sensitivity and large sample requirement. MS-based techniques are more widely used in metabolite profiling because of their high throughput and sensitivity but isomeric separation ability is still a limitation. Chromatographic combined MS platforms have improved selectivity but not without drawbacks, e.g., LC-MS is limited in long separation time and chromatographic condition dependent. As highlighted in

Table 1 .

 1 Comparison of main analytical technologies for metabolomics.7, 30, 31 Techn

		Identification	Analysis	Advantages			Disadvantages
	iques	dimensions	time
	NMR	Chemical	Minutes Non-destructive;		Low sensitivity;
		shift: 1 H, 13 C		Rapid;			Redundancy of signals;
					Highly selective;		Large sample requirement.
	MS	m/z		Millisec	Very high throughput and sensitivity;	Inability to separate isomers and
		of	parents	onds to	No cross-sample contamination;	isobaric species;
		and		seconds	Low-cost analysis;		Significant ion suppression;
		fragments		Low sample requirement.		High ionization discrimination (ESI).
	LC-	Retention	Minutes	Minimal sample preparation;		Retention times are highly dependent
	MS	time; m/z	to hours	Selectivity and high sensitivity;	on chromatographic conditions;
					LC can be coupled to any type of MS;	Lack of large metabolite databases;
					Applicable to many types of	Long separation times.
					molecules.
	GC-	Retention	Minutes	Structure	information	obtained	Limited to volatile compounds;
	MS	time; m/z	to hours	through in-source fragmentation;	Require sample derivatization;
					Availability of universal databases for	High	temperature	can	induce
					identification;		decomposition;
					Separation efficiency and high peak	Low ionization discrimination.
					capacity.
	CE-	Retention	Minutes Excellent for analysis of polar	Relatively low throughput profiling;
	MS	time; m/z		molecules in aqueous samples;	Poor migration times reproducibility.
					Both inorganic and organic anions can
					be analyzed;
					Low running costs and sample
					consumption.
	IMS	Drift	time;	Millisec	High throughput and robust;
		m/z; CCS	onds to	Ability to determine the collision
				seconds	cross-section (CCS) of ions;
					High peak capacity and selectivity;
					Separation of isomeric and isobaric
					compounds;
					Very high throughput;
					Reduces noise and interferences.

CCS and m/z are highly correlated parameters, i.e. IMS and MS are not strongly orthogonal; Ion losses can be important.

Table 1 .

 1 CV values of 4 AAs and their peak to peak resolution (Rpp)

		Without throttle gas		Throttle gas ~0.5 l.min -1		Throttle gas ~0.75 l.min -1
		CV	FWHM	Rpp	CV	FWHM	Rpp	CV	FWHM	Rpp
	leucine	-2.2	0.90	-	-2.4	0.52	-	-2.5	0.36	-
	phenylalanine	-0.3	0.85	1.28	-0.5	0.55	2.09	-0.5	0.43	2.98
	tryptophan	1.5	0.99	1.15	1.4	0.60	1.95	1.3	0.45	2.41
	arginine	3.3	0.92	1.11	3.0	0.54	1.65	3.0	0.46	2.20

Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry 72 enables

  sensitive detection of six underivatized aminobutyric acid isomers. Baseline separation was achieved, but at the expense of relatively long running time (~30 min).7

2, 7

methods. LC-MS/MS performance for separation of these species, derivatized using Marfrey's reagent, has been evaluated by Vemula et al in 2017.

2

More recently, a new LC-MS/MS method was developed that Chapter Ⅲ. Infrared

Table 1 .

 1 Experimental and calculated vibrational frequencies for protonated AABA, BABA, and GABA. Vibrational frequencies are given in units of cm -1 . Calculated intensities (in units of km.mol -1 ) are given in parentheses.

		AABA		BABA		GABA
	Description	exp	theory	exp	theory	Exp	Theory
	C-O st.	1130-	1166 (124)		1099 (97)	1177	1176 (213)
	C-O-H bend	1195	1174 (87)	1177	1182 (155)
	CH bend	1385-	1393 (109)	1380-	1412 (186)	1400-	1419 (99)
	-NH3 + umb	1480	1447 (169)	1495	1462 (181)	1545	1464 (118)
	C=O st.	1780	1769 (279)	1740	1722 (273)	1725	1703 (353)

Table 2 .

 2 The DMS behavior of three isomers with N2 only and addition of MeOH as modifier along with predicted

	structural parameters
				AABA	BABA	GABA
	DMS data	CV (N2 only)	V	-7.0	-5.6	-5.5
		CV (MeOH/N2)	V	-24.6	-20.0	-16.9
		CV shift	V	-17.6	-14.4	-11.4
	DFT data	-NH3 + ---O=C	Å	1.95	1.77	1.71
		C=O	Å	1.21	1.22	1.23
		Predicted CO stretch	cm -1	1775	1722	1703
	Predicted CV shift	Intramolecular HB		Weak	Medium	Strong
		Expected -NH3 + ---methanol interaction		Strong	Medium	Weak
		Negative CV shift		Large	Medium	Small

Infrared Isomer-Specific Fragmentation for the Identification of Aminobutyric Acid Isomers Separated by Differential Mobility Spectrometry

Table 1 .

 1 Composition of the standard mixture consisting of 33 AAs and related compounds.

	Standards
	Compound	[M+H] + (m/z)	Compound	[M+H] + (m/z)	Compound	[M+H] + (m/z)
	Glycine	76	Pipecolic acid	130	Methionine	150
	α-Alanine	90	Hydroxyproline 132	Histidine	156
	β-Alanine	90	Isoleucine	132	2-Aminoadipic Acid 162
	α-Aminobutyric Acid	104	Leucine	132	Phenylalanine	166
	β-Aminobutyric Acid	104	Alloisoleucine	132	Arginine	175
	-Aminobutyric Acid	104	Ornithine	133	Citrulline	176
	Serine	106	Asparagine	133	Tyrosine	182
	Proline	116	Aspartic Acid	134	Sulfocysteine	202
	Valine	118	Lysine	147	Tryptophan	205
	Threonine	120	Glutamine	147	Cystathionine	223
	Taurine	126	Glutamic Acid	148	Cystine	241

Samples and subjects. Blood samples were obtained from patients suspected of MD based on routine diagnostic at Robert Debré Hospital (Paris, France). Blood was Chapter Ⅳ. Separation

and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry

and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 103Table 2 .

 2 Compensation voltage values (in V), and collisional cross section (CCS) values (in Å 2 ) taken from (a) ref. Zheng et al. 22 , (b) ref. Barran 44 , (c) ref. Paglia et al. 38 , and (d) ref. Davidson et al.37 1 V corresponds to an electric field of 1.43

	Chapter Ⅳ.
	kV.m -1 or ~0.07 Td
	Name	[M+H] + m/z Ion Type	CV (All)	CCS (a)	CCS (b)	CCS (c)	CCS (d)
	Glycine	76	A	-10.7				117.4
	Sarcosine	90	A	-10.8
	β-Alanine	90	A	-8.2
	α-Alanine	90	A	-7.9				117.1
	α-Aminobutyric acid	104		-6.2
	β-Aminobutyric acid	104		-5.7
	-Aminobutyric acid	104		-5.7
	Dimethylglycine	104		-1.2	124		116
	L-Serine	106	A	-6.6				121.1
	L-Proline	116	A	-5.3	126	125	118	121.8
	L-Valine	118	B	-3.7	134	121		124.8
	L-Threonine	120	A	-4.8			122	123.5
	Cysteine	122	A	-4.8	150		113	124.6
	Trans-4-Hydroxy-proline	132		-4	130		122
	Cis-4-hydroxy-proline	132		-3.6
	L-Leucine	132	B	-3	135	124	128	130.9
	L-Allo-Isoleucine	132		-2.9
	L-Isoleucine	132	B	-2.8	137		129	129.5
	L-Asparagine	133	A	-4.1	132		118	124.7
	L-Ornithine	133		-1.3	130		121
	L-Aspartic Acid	134	A	-4.4			122	128
	unassigned	134		-2.6
	unassigned	135		-2.7
	Glutamine	147	B	-2.7	133		124	127
	L-Lysine	147	B	-0.3	132		127	127.6
	L-Glutamic Acid	148	B	-2.9			123	128.2
	L-Methionine	150	B	-2.3	134		127	129.4
	L-Histidine	156	B	-1.5	132		127	128.5
	L-Phenylalanine	166	B	-1.4		140	135	137.2
	1-Methylhistidine	170		-0.2			129
	3-Methyl-L-histidine	170		0.4		133
	L-Arginine	175	C	0.9		136	133	132.8
	L-Citrulline	176		-0.7		137	132
	L-Tyrosine	182	B	-1.4		146	139	141.9
	L-Tryptophan	205	C	0.2	154		145	146.5

Isomers and isobars are indicated by vertical dashed lines. Chapter Ⅳ. Separation

Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 104

Table 3 .

 3 Compensation voltage (CV) values derived for a selected set of metabolites in a plasma or corresponding standards diluted in water:methanol solution. Mean CV values and the corresponding standard deviation (SD) are given in volt unit.

		Plasma		Water:MeOH
		CV	SD	CV	SD
	glycine	-10.7	0.2
	alanine	-7.6	0.3	-7.9	0.3
	serine	-6.6	0.2	-6.8	0.2
	proline	-5.3	0.1	-5.4	0.2
	valine	-3.7	0.1	-3.9	0.2
	threonine	-4.8	0.1	-4.9	0.2
	asparagine	-4.2	0.1	-4.3	0.1
	glutamine	-2.7	0.1	-2.8	0.1
	lysine	-0.3	<0.1	-0.3	0.2
	methionine	-2.3	0.1	-2.4	0.2
	histidine	-1.5	0.1	-1.5	0.2
	phenylalanine -1.3	0.1	-1.4	0.2
	arginine	0.9	<0.1	1	0.2
	citrulline	-0.7	<0.1	-0.7	0.2
	tyrosine	-1.4	0.1	-1.4	<0.1
	tryptophan	0.2	0.1	0.2	0.1

and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry

Chapter Ⅳ. Separation

of Amino Acids and Related Compounds in Plasmas by DMS-MS 129Table 1 .

 1 Composition of the standard mixture consisting of 33 AAs and related compounds. The list of corresponding isotopically labeled compounds used as internal standards (IS) for quantification is also given, as well as their final concentration (µM) in the samples.

	Standards		Isotopically stable internal standards (IS)
	Compound	[M+H] + (m/z) Compound	[M+H] + (m/z)	Concentration (μM)
	Glycine	76	Glycine-13 C2	78	27
	α-Alanine	90	α-Alanine-13 C2	92	20
	β-Alanine	90
	α-Aminobutyric Acid	104
	β-Aminobutyric Acid	104
	-Aminobutyric acid	104
	Serine	106	Serine-d3	109	13
	Proline	116	Proline-d7	123	20
	Valine	118	Valine-d8	126	20
	Threonine	120	Threonine-13 C1, d2	123	7
	Taurine	126	Taurine-d4	130	7
	Pipecolic acid	130	Pipecolic acid-d9	139	1
	Hydroxyproline	132
	Isoleucine	132	Isoleucine-13 C6	138	2
	Leucine	132	Leucine-d3	135	13
	Alloisoleucine	132	Alloisoleucine-d10	142	13
	Ornithine	133	Ornithine-d6	139	7
	Asparagine	133	Asparagine-15 N2	135	7
	Aspartic Acid	134
	Lysine	147	Lysine-d4	151	13
	Glutamine	147	Glutamine-d5	152	67
	Glutamic Acid	148
	Methionine	150	Methionine-d3	153	8
	Histidine	156	Histidine-13 C1	157	13
	2-Aminoadipic Acid	162
	Phenylalanine	166	Phenylaline-d5	171	7
	Arginine	175	Arginine-15 N2	177	7
	Citrulline	176	Citrulline-13 C1	177	1
	Tyrosine	182	Tyrosine-13 C9, 15 N1	192	7
	Sulfocysteine	202
	Tryptophan	205	Tryptophan-d5	210	27
	Cystathionine	223
	Cystine 130

Chapter Ⅴ. Quantification

Table 2 .

 2 Concentration (µM) of AAs and related compounds of interest in four plasmas determined using the

	LC-MRM protocol.
	Compound	[M+H] + m/z	P1	P2	P3	P4
	Glycine	76	235 ± 11	165 ± 7	259 ± 12	± 24
	α-Alanine	90	323 ± 19	247 ± 15	431 ± 26	± 66
	Serine	106	132 ± 9	130 ± 9	111 ± 8	± 15
	Proline	116	212 ± 10	138 ± 7	147 ± 7	± 22
	Valine	118	244 ± 12	216 ± 11	186 ± 9	± 10
	Threonine	120	131 ± 8	159 ± 10	103 ± 6	± 25
	Taurine	126	125 ± 11	62 ± 6	58 ± 5	± 36
	Pipecolic acid	130	2 ± 0.2	1 ± 0.1	1 ± 0.1	± 1
	Isoleucine	132	69 ± 6	68 ± 6	46 ± 4	± 4
	Leucine	132	140 ± 7	120 ± 6	97 ± 5	± 6
	Alloisoleucine	132	1 ± 0.1	0 ± 0.1	1 ± 0.1	± 0
	Asparagine	133	47 ± 3	44 ± 3	47 ± 3	± 1
	Ornithine	133	75 ± 5	50 ± 3	68 ± 4	± 7
	Glutamine	147	556 ± 26	458 ± 22	652 ± 31	± 32
	Lysine	147	158 ± 10	153 ± 9	164 ± 10	± 24
	Methionine	150	28 ± 2	23 ± 1	23 ± 1	± 3
	Histidine	156	76 ± 3	77 ± 4	78 ± 4	± 4
	Phenylalanine	166	85 ± 5	423 ± 24	49 ± 3	± 6
	Arginine	175	72 ± 3	44 ± 2	69 ± 3	± 3
	Citrulline	176	23 ± 2	17 ± 1	28 ± 2	± 2
	Tyrosine	182	67 ± 4	32 ± 2	436 ± 24	± 11
	Tryptophan	205	61 ± 5	75 ± 6	69 ± 5	± 3

Table 3 .

 3 Composition of the calibration solutions spiked in plasma P1 for calibration purpose. Final concentrations (µM) of the targeted compounds in the six spiked P1 plasma solution are listed.

	Compound	[M+H] + m/z	C0	C1	C2	C3	C4	C5
	Glycine	76	12	14	15	29	47
	α-Alanine	90	16	18	20	34	51
	Serine	106	7	8	10	24	42
	Proline	116	11	12	14	28	46
	Valine	118	12	14	16	30	47
	Threonine	120	7	8	10	24	42
	Taurine	126	6	7	8	15	24
	Pipecolic acid	130	<1	2	4	18	35
	Isoleucine	132	3	5	7	21	38
	Leucine	132	7	9	11	25	42
	Alloisoleucine	132	<1	2	4	18	35
	Asparagine	133	2	4	6	20	37
	Ornithine	133	4	6	7	21	39
	Glutamine	147	28	31	35	63	98
	Lysine	147	8	10	11	25	43
	Methionine	150	1	3	5	19	36
	Histidine	156	4	6	7	21	39
	Phenylalanine	166	4	6	8	22	39
	Arginine	175	4	5	7	21	39
	Citrulline	176	1	3	5	19	36
	Tyrosine	182	3	5	7	21	38
	Tryptophan	205	3	5	7	21	38

Table 4 .

 4 The structure of input file for targets list: "tgTable"

	Name	Comments	m/z_ref	CV_ref
	# Gly_AA	C2H5NO2H	76.1	-10.7
	Ala-AA	90.054	90.1	-7.6

Table 5 .

 5 The structure of input file for targets list: "taskTable"

	MS_file	DMS_file	t0	CV0 dilu Path
	C0_AS_DV-	Fichier_Dims	0.08 6	0	Esquire_MS/MS_Data/20190517_AA/
	1800_1.D.ascii	20190517-000000.txt
	DMS-MS data are recorded using the chromatogram mode of the MS instrument.

Table 6 .

 6 The structure of input file for targets list: "quantTable".

	Name	IS	CAA_Plasma CAA_ref	CIS_ref
	# Gly_AA	Gly-13C2	11.75	1750	26.7
	Ala-AA	Ala-13C2	16.15	1750	20

Table 7 .

 7 LOD values in units of µM derived using linear regression method for 12 metabolites.

	Compound	[M+H] + m/z	R 2 value (a)	Linear Range (µM)	LOD (b) (µM)
	Serine	106	0.995-0.999	7-182	3.9
	Threonine	120	0.998-1.000	7-182	2.0
	Asparagine	133	0.926-0.998	2-177	8.1
	Glutamine	147	0.938-0.994	28-378	8.9
	Lysine	147	0.868-1.000	8-183	6.5
	Methionine	150	0.999-1.000	1-176	1.0
	Histidine	156	0.973-0.996	4-179	2.8
	Phenylalanine	166	0.979-0.998	4-179	7.3
	Arginine	175	0.935-1.000	4-179	1.8
	Citrulline	176	0.863-0.950	1-36	5.6
	Tyrosine	182	0.978-1.000	3-178	2.7
	Tryptophan	205	0.998-1.000	3-178	2.1

Table 8 .

 8 The concentration (μM) and corresponding error bars of 14 AAs and related compounds in a random plasma.Data derived from 2D (1D (fixed CV)) fit of the same DMS-MS data sets are reported in columns 2 and 3 (4 and 5).

	Values associated with LC-MS/MS data sets are given in columns 6 and 7.
	AA	Concentration	Error bar	Concentration	Error bar	Concentration	Error bar
		2D	2D	1D (fixed CV)	1D (fixed CV)	LC-MS/MS	LC-MS/MS
	Ala	747.6	80.3	946.6	109.7	1109.0	66.5
	Ser	242.0	33.5	279.2	57.5	209.0	14.8
	Pro	624.5	27.3	610.9	27.6	456.0	25.5
	Val	208.7	6.8	177.6	3.8	207.0	10.6
	Thr	389.4	18.3	421.3	13.7	412.0	25.5
	Asn	106.9	11.5	103.0	10.7	67.0	4.1
	Gln	531.1	13.8	553.6	32.3	672.0	31.6
	Lys	389.3	10.0	420.5	24.5	397.0	24.2
	Met	81.6	3.3	72.5	4.2	52.0	3.4
	His	101.9	2.0	109.2	7.3	93.0	4.3
	Phe	102.9	5.1	88.0	9.2	95.0	5.3
	Arg	70.9	2.6	72.0	6.4	59.0	2.6
	Cit	48.3	4.1	33.0	3.9	25.0	1.8
	Tyr	192.6	15.3	193.6	11.1	184.0	10.3
	Trp	49.6	3.6	36.4	4.7	41.0	3.2
	Orn	145.1	7.4	146.3	14.4	115.0	7.4

  . An overview of literature data for the separation of isomers of BCAAs

	Methods	Derivatization Ionization	MS/MS Analytes	Separation			Reference
	FAIMS	No	ESI	No	Leu/Ile	Partially resolved, ~Rpp	Barnett, et
						0.70				al, 1999 28
	GC-MS	Yes	EI	No	Val, Leu, Ile	Baseline	resolved	4	Deng, et al,
					and Phe	isomers				2003 29
	MS/MS	No	ESI	Yes	Leu, Ile	No separation		Bishop, et
										al. 2007 10
	LC-	No	ESI	Yes	aIle, Ile, Leu,	aIle, Ile, Leu were partially	Oglesbee,
	MS/MS				Val, OH-Pro	separated within a triple	et al, 2008 19
						peak, Val and OH-Pro
						were baseline separated
	MS/MS	Yes	ESI	Yes	Leu, Ile/aIle,	No separation		Fingerhut,
					Val, OH-Pro					et al, 2009 21
	UPLC-	No	ESI	Yes	BCAAs	aIle, Ile, Leu, Val were	Alodaib, et
	MS/MS					baseline separated,		al, 2011 30
	LC-	No	ESI	Yes	Leu, Ile, Val,	Well separated		Wang,	et
	MS/MS				OH-Pro					al 31
	UPLC-	No	ESI	Yes	BCAAs, OH-	Well separated		Fingerhut,
	MS/MS				Pro	Leu, Ile/ aIle, Val		et al, 2016 18
	DTIMS	No	ESI	No	11 isomers at	Leu and Ile are partially	Dodds, et al,
					C6H13NO2(131	separated,		1.2%	2017 26
					Da)	difference in CCS, RP~60.
						aIle	and	Ile	are
						unresolvable.
	MS/MS	No	ESI	Yes	Leu, Ile, aIle	No separation		Casas, et al,
										2020 11
	MS/MS	No	ESI	Yes	Leu, Ile/aIle	No separation		Jiang, et al,
										2020 32

Figure 1. Chemical structures of isomers and isobars of interest of leucine: L-isoleucine and L-alloisoleucine are isomeric compounds, while cis-and trans-hydroxyproline are isobaric compounds, and all can be found in plasma samples.

Scheme 1. The workflow for the diagnosis of MSUD in NBS and second-tier test in dry blood spots. The diagnostic criterion and cutoff values for each step are indicated. The TPN abbreviation represents dietary factor of total parenteral nutrition.19

In order to probe the effect of the DV value on peak capacity and resolution, DMS spectra of 20 common AAs in water:methanol solution were recorded at DV values ranging from 1.2 kV to 2.0 kV. The set values of flow rate of the N2 desolvation gas and its temperature are 6.5 L.min -1 and 220 °C, respectively. This was found to provide a good balance between DMS resolution and ion transmission. Results of DMS-MS measurements are reported in Figure 1. DMS dispersion plots, i.e. evolution of CV as a function of DV for each individual AA, are reported in Figure 1a.

increase of signal-to-noise ratio. 7 In the perspective of using DMS for identification purpose, the addition of selected organic molecules to the carrier gas is undoubtedly the approach of choice for improving DMS resolution and specificity. Drastic improvement of ion, and especially isomeric ions, separation has been observed by adding alcohol or other polar and/or proton-acceptor molecules in the buffer gas, 15,[START_REF] Eiceman | Separation of ions from explosives in differential mobility spectrometry by vapor-modified drift gas[END_REF][START_REF] Krylova | Effect of moisture on the field dependence of mobility for gas-phase ions of organophosphorus compounds at atmospheric pressure with field asymmetric ion mobility spectrometry[END_REF][START_REF] Ruskic | Modifier Selectivity Effect on Differential Ion Mobility Resolution of Isomeric Drugs and Multidimensional Liquid Chromatography Ion Mobility Analysis[END_REF] and in the case of AAs in particular. 16,26 A systematic study of the effect of different modifiers on the separation of AAs and related compounds is under study in our group. Using methanol as an added modifier to N2 as carrier gas, we already showed baseline separation of isomeric ions could be obtained in the case of sarcosine, α-and βalanine, 49 as well as for the α-, β-, and -aminobutyric acid isomers. [START_REF] Wang | Infrared isomer-specific fragmentation for the identification of aminobutyric acid isomers separated by differential mobility spectrometry[END_REF] As reported in the literature, adding of a fraction of He to the carrier gas 14,[START_REF] Baird | High-Resolution Differential Ion Mobility Chapter Ⅳ. Separation and Identification of Amino Acids and Related Compounds Based on Differential Mobility Spectrometry 125 Separations/Orbitrap Mass Spectrometry without Buffer Gas Limitations[END_REF][START_REF] Winter | Separation of Isobaric Mono-and Dimethylated RGG-Repeat Peptides by Differential Ion Mobility-Mass Spectrometry[END_REF] or increasing the waveform amplitude [START_REF] Baird | Differential Ion Mobility Separations/Mass Spectrometry with High Resolution in Both Dimensions[END_REF] should also be considered for high resolution DMS. observed at a more positive value than the corresponding AA. The CV value is 0 for Arg-15N2 as compared to Arg. From the analysis of other metabolites, not reported in Table 9, it seems that when an IS corresponds to the substitution of one or several N and/or C atoms, the CV value is close or equal to zero. Significant CV shifts only occur with H/D substitutions. The largest CV value is found for Val whose IS is Val-d8. The chemical structures of the IS where deuterium are shown is given in Table 9. As can be seen in this table, deuterium atoms are incorporated in no exchangeable positions. While one can understand that H/D exchange has a larger effect on the interaction with buffer gas molecules, there is no obvious correlation between the number of D and the corresponding △CV value. For instance, the △CV value is 0.2 for Met/Met-d3, while it is 0.4 for Val/Val-d8. analysis allowed expanding NBS programs for inborn errors of metabolism including MSUD. 8,9 It offers high throughput analysis and selectivity, especially for small molecules such as metabolites. In the context of MSUD diagnosis, however, precise quantification of aIle is a difficult issue. Isomers Leu, Ile, and aIle have essentially the same fragmentation mass spectra. 10,11 As a result, screening of MSUD by MS/MS alone is based on the determination of the total concentration, often referred to as Xle. 12,13 Unfortunately isobaric OH-Pro also gives rise to the same MS/MS transition as Leu. High level of OH-Pro, as observed in the case of hydroxyprolinemia, can thus lead to false positive results of MSUD in NBS using low resolution MS. 7,14 Hence, a second-tier diagnosis is often needed for definitive MSUD diagnosis. 15,16 Separation of isomeric and isobaric Leu is thus required. Chromatographic preseparation methods hyphenated to MS/MS have been successfully applied for biomarkers

168

identification, and quantification in bio-samples. 17 For example, Ultra Performance Liquid Chromatography hyphenated to MS/MS (UPLC-MS/MS) was proposed as a rapid and sensitive method for the determination of BCAAs to reduce false positive diagnosis of MSUD. 18 Oglesbee and coworkers 19 also reported on an LC-MS/MS method for separation of isomeric aIle, Ile, and Leu, and isobaric OH-Pro compounds. As a result, accurate quantification of aIle can be achieved after separation through a second-tier protocol.

While chromatographic methods generally allow for good isomeric and isobaric separation, their long running time makes them unsuitable for large scale or highthroughput testing in NBS. 20 Based on MS and LC-MS/MS strategies, various concentration criteria of BCAAs in biosamples have been applied in the screening programs. The major criterion for MSUD suspicion is the elevation of total concentration of [Leu+Ile+aIle] (Xle), while elevated Val is taken as a minor criterion. 21 As illustrated in Scheme 1, for primary screening, the total Potential analytical method based on IMS. Ion mobility separation of Leu isomers has also been reported. 26,28 In particular, Leu and Ile were reported to be partially resolved with an Rpp value of 0.70 under negative ESI mode by FAIMS in 1999. 28 More recently, Leu and Ile have been reported to show differences in ion mobility using both drift tube IMS (DT-IMS) 26 and traveling wave IMS (TWIMS). 27 An investigation on prediction of ion mobility separation capabilities of 11 Leu/Ile isomers has been conducted by Dodds, et al.

using DT-IMS. 26 In this assay, Leu and Ile isomers were separated at half-height, but aIle and Ile diastereoisomers yield a single broad IMS peak.

EXPERIMENTAL METHODS

Plasma sample preparation.

Blood was centrifuged rapidly after collection into heparin containing tubes. Plasmas were subsequently deproteinized by adding sulfosalicylic acid 30% (10:1, v:v) and then stored at -20°C. Just before analysis, samples were diluted in acidified water:methanol (50:50, v:v) solution, and 20-fold diluted plasma samples were used for electrospray injection. The protocol of sample preparation for quantitative curves is as described in chapter 5. The pool of plasmas taken from healthy volunteers was used as matrix. Concentrations of OH-Pro and Xle in each calibration sample are listed in Table 2. Mixture of isomers and isobars of interest in standard solution. 5 pairs of isomers and isobars and their IS were dissolved in water:methanol (50:50, v:v) at concentration of 100 μM, and formic acid was added at 0.5% to improve protonation efficiency. Isotopically labeled compounds were used as IS: Ala-13C12 was used for Sar and Ala, Orn-d6 for Orn, Asn-15N2 for Asn, Gln-d5 for Gln, Lys-d4 for Lys, His-13C1 for 1-, and 3-Met-His, aIle-d10

for aIle and trans-OH-Pro.

Instrument methods. All DMS-MS experiments were performed using our low mass-resolution quadrupole ion-trap instrument. Experimental conditions of DMS-MS instrument are as described in chapter 5. All DMS-MS analyses presented here were analyzed using our dedicated suite of R-written codes. 34

RESULTS AND DISCUSSION

Concentrations of Leu isomers and OH-Pro isobars in different plasmas determined by LC-MS/MS at the hospital. Concentrations of isomers of interest in four different

plasmas are given in Table 3. In the pool of plasmas sample (P1), concentrations were determined as 69±6 μM for Ile, 140±7 μM for Leu, 1±0.1 μM for aIle, and 12 μM for OH-Pro. Concentrations vary from one plasma (P1) to the others, (P2, P3, and random plasma P4), nevertheless Leu is always found to be the most concentrated isomer, and aIle concentration is found on the order of 1 μM in these no-MSUD plasmas. Relative OH-Pro/Xle concentration varies to a larger extent from one plasma to the other, for example in plasma P2 the concentration ratio of OH-Pro/Xle is 0.03, while it is found to be 0.39 in random plasma P4. Separation of Leu isomers. DMS-MS measurement of individual samples of Leu isomers and isobars was conducted using pure N2 as a carrier gas. DMS spectra of three isomers (Leu, Ile, and aIle) and two isobars (cis and trans OH-Pro) are shown in Figure 2 (a) and (b), respectively. FWHM value was found to be around 0.74 V.

CV values of Leu, Ile, and alle were found to be -3.0, -2.8, and -2.9 V. The peak-topeak resolution (Rpp) between Leu and Ile is calculated to be 0.16, and the value is even lower (0.08) for Ile and aIle. A broad and unresolved peak is thus anticipated for Xle. Different polar molecules, such as H2O, methanol (MeOH), ethanol (EtOH), isopropanol (iPro), and isopropanol (ISP) were introduced into the N2 carrier gas at flow rate of 6 L.min -1 . Upon addition of these polar molecules, an expected negative CV shift of DMS peak was observed. However, the separation degree of Leu and Ile was not improved. Only when iPro was used as the modifier, a broader peak (FWHM=1.6 V) associated with two unresolved peaks was observed. Attempts to improve isomeric resolution, in particular through an increase of the modifier flow, were not successful.

To conclude, whereas Leu and Ile isomers cannot be separated, they can be well separated from both cis-and trans-OH-Pro. By setting CV value of DMS at -2.9 V, only alle and its isomers would be transmitted to the mass spectrometer, and Xle can be quantified. Trans-OH-Pro isobars, can also be selected by adjusting the CV value to -4.0

V. While a better separation could probably be achieved using chemical modifiers, it is interesting to stress that using the default carrier gas (N2), a simple and robust DMS-MS protocol could be proposed to reduce the false positive diagnosis of MSUD in NBS. 2), and the IS was spiked at a constant concentration of 13.3 μM. For Xle, the concentration range was 10.5 to 535.5 μM, which was higher than OH-Pro.

Construction of quantitative calibration curves for

Calibration curves were derived from an average of four replicates in different days.

DMS-MS spectrum of Leu isomers and isobars in plasma samples. The extracted DMS-

MS spectra for protonated ions at m/z 132 in the pool of plasmas and spiked pool of plasmas (corresponding to calibration sample C100 in Table 2) are presented in Figure 4 (a) and (b), respectively. As can be seen in Figure 4b, two overlapping peaks can be distinguished. One intense and broad peak located at -2.8 V can be attributed to Xle. The smaller peak located at more negative CV position (-3.7 V) was assigned to cis-and trans-OH-Pro isobars. The present results suggest that isobaric OH-Pro compounds can be quantitatively determined by DMS-MS within plasma samples without interference of Leu isomers. The LOD derived from the linear regressions were 9.2 μM for OH-Pro and 17.0 μM for Xle in plasma matrix in this method which can be used to evaluate the precision. Application of routine LC-MS/MS method as a second-tier test for MSUD, however, is rather time-consuming. Here, DMS-MS method is proposed to be a fast and accurate diagnosis technology for isomeric/isobaric biomarkers identification and quantification.

Quantification of

More systematic analyses of plasma samples from MSUD and hydroxyprolinemia patients should be performed in order to evaluate the robustness of our DMS-MS protocol. In other cases of isomers, accurate quantification has also been achieved without isomeric interference in mixture, and similar results were obtained using scanning CV and fixed CV working modes.

New SLIM-based IMS for improved isomeric separation. Efforts have been made to improve isomeric separation by increasing the mobility path length. New technologies extended ultra-long flight path in ion mobility region, which allow for the separation of isomers with subtle structural difference. 37 For instance, isomeric glycan species can be well separated using SLIM. Coupling of SLIM of cryogenic infrared spectroscopy has been achieved on these species. 38 Cyclic IM device for isomeric polymer analysis. Cyclic ion mobility is also very promising for the resolution for complex samples. Cyclic IMS instruments offer additional capabilities compared to linear IMS for polymer analysis. 39,40 The ability of separation of isomeric pentasaccharide species and the 6 + ions ubiquitin clearly demonstrated the potential of Cyclic IMS for structural elucidation of isomeric species. 39 position of AAs and related compounds were found in the range of -10 to 0 V, while acylcarnitine compounds are observed between 0 and 4 V.

The real potential added-value of DMS-MS protocols is to perform multi-class quantification protocol using a single DMS-MS instrumental method for efficient clinical diagnosis. This would allow for the quantification of multiple biomarkers in a single run.

From the instrumental point of view, it is important to better control of the nature of the carrier gas. The gas temperature and stability of the gas flow have great impact on CV values of analytes. Also coupling of DMS with more sensitive MS, e.g., triple quadrupole, would improve the limit of detection.

To improve the identification of low amount of components in samples, many new experimental approaches have been reported. Two dimensional separation LC-DMS-MS/MS method have been successfully used for phospholipid separation by Campbell and coworkers. It was shown in particular that separation, resolution, specificity, and signal-to-noise ratio were significantly improved by addition of DMS to LC-MS/MS. 31 Application of 3D-printed open port probe (3DP-OPP) with differential ion mobility spectrometry (DMS) mass spectrometry for quantitative analysis in urine was reported recently. 32 The fast and reliable way of sample introduction using 3DP-OPP and robotic may provide a quick way to quantify compounds of interest below approved cut-off levels.

Improving isomeric or isobaric separation still remains a challenge for metabolomics.

Efforts have been made to improve isomeric separation by extension of the drift path length. New technologies with refined geometry structures of ion mobility instrument have been reported recently, such as structures for lossless ion manipulations (SLIM) based IMS 33 and Cyclic IM device. 34,35 The ion mobility region has also been modified to a cyclic sequence to obtain increased resolution for complex samples. Cyclic IMS instruments offers additional capabilities compared to linear IMS for polymer analysis.

Supplementary Information

DMS SEPARATION OF ACYL-CARNITINE RELATED COMPOUNDS

As can be seen in table S2-1, the structural information of 3 sets isomers of acyl-carnitine related compounds were listed, along with the abbreviation and source.