MD, MSc Uyên Châu Nguyên
email: u.nguyen@maastrichtuniversity.nl

PhD Mark Potse

MD Kevin Vernooy

MD Masih Mafi-Rad

PhD Jordi Heijman

MD Maria Luce Caputo

MD Giulio Conte

MD François Regoli

PhD Rolf Krause

MD Tiziano Moccetti

MD Angelo Auricchio

PhD Frits W Prinzen

PhD Francesco Maffessanti

A left bundle branch block activation sequence and ventricular pacing influence voltage amplitudes: an in vivo and in silico study

Keywords: Voltage amplitude, intra-cardiac electrogram, heart failure, left bundle branch block, activation sequence, computer models, anisotropy

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Condensed abstract

This study investigates the influence of activation sequence on voltage amplitudes. In patients and computer models without scar, lower unipolar voltage amplitudes in the septum are exclusively associated with a left bundle branch block activation sequence. Ventricular pacing furthermore substantially affects voltage amplitudes with average changes of half of intrinsic value.

What's new?

• This study investigates the influence of activation sequence on voltage amplitudes in patients and provides possible explanations for these changes using computer modeling.

• In patients and computer models without scar, lower unipolar voltage amplitudes in the septum are exclusively associated with a left bundle branch block activation sequence.

• Ventricular pacing substantially affects voltage amplitudes in both patients and computer models with changes of half of intrinsic value.

Introduction

Low voltage amplitudes from electro-anatomic mapping (EAM) are widely considered as a reference tool for scar delineation [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC)Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF] and have been associated with scar on delayed enhancement cardiac magnetic resonance imaging (DE-CMR). [START_REF] Hutchinson | Endocardial unipolar voltage mapping to detect epicardial ventricular tachycardia substrate in patients with nonischemic left ventricular cardiomyopathy[END_REF] Nevertheless, significant overlap in voltage amplitudes between scar and normal myocardium and substantial inter-patient differences [START_REF] Nguyen | Evaluation of the use of unipolar voltage amplitudes for detection of myocardial scar assessed by cardiac magnetic resonance imaging in heart failure patients[END_REF][START_REF] Codreanu | Electroanatomic characterization of postinfarct scars comparison with 3-dimensional myocardial scar reconstruction based on magnetic resonance imaging[END_REF][START_REF] Wijnmaalen | Head-tohead comparison of contrast-enhanced magnetic resonance imaging and electroanatomical voltage mapping to assess post-infarct scar characteristics in patients with ventricular tachycardias: real-time image integration and reversed registration[END_REF] limit the validity of distinct voltage cut-off values to identify scar. [START_REF] Glashan | Whole human heart histology to validate electroanatomical voltage mapping in patients with non-ischaemic cardiomyopathy and ventricular tachycardia[END_REF] These data suggest that additional factors may affect the voltage amplitudes. [START_REF] Josephson | Substrate Mapping for Ventricular Tachycardia: Assumptions and Misconceptions[END_REF] Substantial differences in characterization of scar by low-voltage areas between intrinsic rhythm and ventricular pacing were recently reported, [START_REF] Tung | Directional Influences of Ventricular Activation on Myocardial Scar Characterization: Voltage Mapping With Multiple Wavefronts During Ventricular Tachycardia Ablation[END_REF] indicating that the activation sequence influences voltage amplitudes.

Besides during pacing, an abnormal activation sequence can also occur in the presence of inter-and intraventricular conduction disturbances. For instance, a left bundle branch block (LBBB) activation sequence is characterized by a slow impulse conduction originating from the right ventricular (RV) free wall gradually propagating to the left ventricular (LV) free wall. [START_REF] Auricchio | Characterization of left ventricular activation in patients with heart failure and left bundle-branch block[END_REF] The influence of an LBBB activation sequence on voltage amplitudes has not been elucidated before.

The aim of this study was to investigate the influence of activation sequence on voltage amplitudes at the endocardium and epicardium in patients with heart failure (HF). In order to improve mechanistic understanding of this influence, regional voltage amplitudes in LBBB patients and computer models without scar were studied.

To compare voltage amplitudes during an LBBB activation sequence and a normal synchronous activation sequence, which was not possible in patients, we used highly realistic computer simulations with three customized models of LBBB patients.

Additionally, voltage differences induced by ventricular pacing were evaluated both in patients and in computer models.

Methods

Patient population

Patients referred for a CRT device implantation who underwent LV endocardial EAM [START_REF] Nguyen | Evaluation of the use of unipolar voltage amplitudes for detection of myocardial scar assessed by cardiac magnetic resonance imaging in heart failure patients[END_REF] or epicardial (coronary venous) EAM [START_REF] Rad | Different regions of latest electrical activation during left bundle-branch block and right ventricular pacing in cardiac resynchronization therapy patients determined by coronary venous electro-anatomic mapping[END_REF] were included in this study. To minimize other sources of variation in voltage amplitudes only patients without scar were included.

The Comitato Etico del Canton Ticino in Lugano and the Medical Ethics Committee of Maastricht University Medical Center approved the study protocol.

Electro-anatomic mapping

Patients underwent either endocardial or epicardial EAM. Voltage amplitudes were computed peak-to-peak and activation times were defined as the duration between onset of QRS-complex on the surface electrocardiogram (ECG) to the moment of steepest downslope on the intra-cardiac electrogram.

Endocardial EAM was performed during intrinsic rhythm using the NOGA XP Cardiac Navigation System (Biosense Webster, Irvine, CA, USA) as previously described. [START_REF] Potse | Patient-specific modelling of cardiac electrophysiology in heart-failure patients[END_REF] Unipolar and bipolar electrograms and catheter tip trajectories in 3D space were simultaneously recorded at the entire LV endocardium (bandpass filter: 1-240 Hz).

Care was taken to cover the whole LV cavity until NOGA mapped volume was close to the CMR measured volume and the system automatically discards points with insufficient wall contact. All acquired signals were temporally aligned using the simultaneously recorded surface ECG.

Epicardial EAM was performed during intrinsic rhythm and RV pacing using EnSite NavX (Abbott, Chicago, Ill, USA) as described previously. [START_REF] Rad | Different regions of latest electrical activation during left bundle-branch block and right ventricular pacing in cardiac resynchronization therapy patients determined by coronary venous electro-anatomic mapping[END_REF] A guidewire permitting unipolar sensing and pacing was inserted into the coronary sinus and manipulated to all accessible tributaries, creating an anatomic 3D map of the coronary veins while simultaneously recording electrograms (bandpass filter: 2-300 Hz) and a surface ECG. RV pacing was performed with the implanted RV lead at 10 beats per minute above the intrinsic heart rate in DDD-mode with a short AV-delay to ensure full ventricular capture. Electrograms with poor signal indicating poor contact were manually discarded during the procedure.

Computer models

Three patient-specific computer models were created. [START_REF] Nguyen | An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects[END_REF] Each model geometry was based on CMR imaging of the heart, lungs, and torso surface. The electrophysiological properties of the models were personalized based on the patients' endocardial EAM and 12-lead ECG acquired during sinus rhythm, and geometries were personalized using the CMR-derived heart-lungs-torso anatomies. [START_REF] Nguyen | An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects[END_REF] Simulations were performed with the Propag-5 software [START_REF] Krause | Hybrid Parallelization of a Large-Scale Heart Model[END_REF] on 2304 cores of the Bullx cluster "Curie" (TGCC, CEA, France). A ventricular model with a 0.2-mm resolution and a torso model with 1-mm resolution were used. Propagating electrical activity was simulated based on ionic transmembrane currents according to a monodomain reaction-diffusion equation. [START_REF] Potse | A comparison of monodomain and bidomain reaction-diffusion models for action potential propagation in the human heart[END_REF] The Ten Tusscher-Noble-Noble-Panfilov model of the human ventricular cardiomyocyte [START_REF] Ten Tusscher | A model for human ventricular tissue[END_REF] was used to compute the ionic currents.

Computed transmembrane currents were injected at 1-ms intervals in the torso model and the bidomain equation was solved for the electrical potential throughout the torso, from which electrograms at the LV endocardium and epicardium were extracted. [START_REF] Potse | Cardiac anisotropy in boundary-element models for the electrocardiogram[END_REF] A normal activation sequence (synchronous activation) was simulated by inducing multiple breakthroughs in a thin, rapidly conducting endocardial layer mimicking the Purkinje network activation. A RV pacing simulation was created by single-point pacing in the RV endocardium apex, while a LV pacing simulation was created by single-point pacing in the LV epicardium. Similar to clinical data, unipolar voltage amplitudes were computed peak-to-peak and activation times were quantified as the duration between onset of the QRS-complex and the moment of steepest downslope on the intra-cardiac electrogram.

Data analyses

Data were expressed as mean±standard deviation (SD) or median and interquartile (IQR) range. Analyses were performed in MATLAB 2016b (Mathworks, Natick, MA, USA). Non-parametric test approaches were used for patient data analyses.

Statistical significance was not evaluated in the simulated data, as it has been argued that statistical comparisons of computer models are inappropriate. [START_REF] White | Ecologists should not use statistical significance tests to interpret simulation model results[END_REF] Absolute values were therefore used to describe the simulated data instead. Regional voltage differences in patients and computer models were compared by subdividing the LV into four segments: anterior, lateral, inferior, and septal. Intrinsic voltage amplitudes were compared between segments and for all patients with endocardial EAM using Kruskal Wallis tests.

Pacing-induced voltage changes were evaluated in patients with epicardial EAM and computer models by assessing differences as absolute and relative (percentage of intrinsic value) numbers. For the epicardial EAM patient data, paired comparisons were carried out by matching every electrogram during intrinsic rhythm with the nearest electrogram obtained during RV pacing under the condition that the Euclidean distance between the two electrograms was <15 mm. In the computer models, paired comparisons were performed by comparing electrograms during different activation sequences from the same vertex in 3D space. Relative differences between every paired electrogram (depicted in mV or percentage of intrinsic value) were computed as:

Difference mV = Intrinsic -paced Intrinsic or Difference % intrinsic = Intrinsic -paced Intrinsic * 100%
Absolute differences were computed similarly as:

Difference mV = abs(Intrinsic -paced) Intrinsic or Absolute difference % intrinsic = abs(Intrinsic -paced) Intrinsic * 100%
Bland-Altman analyses were carried out to evaluate the agreement between voltage amplitudes during intrinsic rhythm and pacing.

Results

Study population

A total of 21 patients and 3 computer models were included in the study. Nine patients, all with LBBB, underwent endocardial EAM (p1 to p9) during intrinsic rhythm, while 12 patients underwent epicardial EAM (p10 to p21) during both intrinsic rhythm and RV pacing. Three computer models (s1, s2, s3) were created based on patient-specific geometries obtained from patients p1, p2, and p3. Detailed information on individual patients and computer simulations is provided in Table 1 and 2, respectively.

Regional voltage differences in patients with LBBB

Regional unipolar and bipolar voltage differences were evaluated for the 9 patients who underwent endocardial EAM during intrinsic rhythm, as these patients had a high number of mapped points covering all segments of the LV.

Mean unipolar voltage amplitudes were approximately 4.5 mV (varying between 4.4-4.7 mV, ~33%) smaller in the septum compared to anterior, lateral and inferior within each endocardially mapped patient and for all 9 patients together (anterior: 13.5±5.6, lateral: 13.8±4.8, inferior: 13.5±6.1, septum: 9.1±4.5 mV, p<0.001). Regional unipolar voltage distributions in individual patients and representative voltage maps are provided in Figure 1. In contrast, bipolar voltage amplitudes were more heterogeneously distributed among the LV segments (anterior: 4.8±3.6, lateral:

5.0±3.1, inferior: 5.5±3.8, septum: 4.7±4.5 mV, p<0.001).

Regional voltage differences in simulations with LBBB and normal activation

Regional endocardial unipolar voltage differences were additionally assessed in three computer models. In all computer models, unipolar voltage amplitudes during an LBBB activation sequence were on average 4.0 mV (~24%) smaller in the septum and inferior compared to other LV segments (anterior: 16.7±3.8, lateral: 17.3±3.2, inferior: 13.1±3.7, septum: 13.1±4 mV). After inducing a normal synchronous activation sequence in the 3 computer models, regional unipolar voltage distributions became more uniform (Figure 2), and unipolar voltage amplitudes were on average similar between the segments (anterior: 14.1±3.4, lateral: 14.4±3.9, inferior: 13.7±4.4, and septal: 13.2±4.1 mV).

RV pacing induced voltage differences in patients

An overview of pacing-induced voltage changes for patients and computer models is provided in Figure 3. Pacing-induced unipolar voltage changes were analyzed in the 12 patients who underwent epicardial EAM (Figure 4). Overall, unipolar voltage during RV pacing did not differ significantly from the value during intrinsic conduction (intrinsic: 7.8±5.0, RV paced: 8.0±6.9 mV, p=0.709), but RV pacing caused considerable changes in these voltage amplitudes (IQR: 53% of intrinsic value;

Figure 3). The absolute change in unipolar voltage amplitude between intrinsic rhythm and RV pacing was substantial: 3.6±5.9 mV (52±110% of intrinsic value).

Larger unipolar voltage changes were present in electrograms with a larger amplitude during intrinsic rhythm (Bland-Altman analysis in Figure 4C).

RV and LV pacing-induced voltage differences in computer models

Pacing-induced unipolar voltage differences (Figure 3) at the epicardium and endocardium were additionally evaluated in computer models (Figure 5 (IQR: 63 and 40 for epicardium and endocardium respectively; Figure 3). The absolute mean difference between intrinsic epicardial unipolar voltage and pacing was 6.0±5.9 (40±53% of intrinsic value) for RV pacing and 6.2±5.3 mV (45±56% of intrinsic value) for LV pacing.

Mean endocardial unipolar voltage amplitudes were also affected by changing activation sequence (intrinsic: 13.7±4.3, RV pacing: 14.0±4.7, and LV pacing:

12.7±4.7 mV; Figure 6A), although these differences were much smaller than the epicardial values (Figure 3). The mean absolute difference between intrinsic endocardial unipolar voltage and pacing was 3.3±3.2 (28±33% of intrinsic value) for RV pacing and 3.8±3.2 mV (30±30% of intrinsic value) for LV pacing.

Discussion

This is the first study elucidating the voltage distribution during an LBBB activation sequence and investigating pacing-induced voltage changes in patients with possible explanations provided by computer simulations. We demonstrated that an LBBB activation sequence, in absence of scar, is associated with lower unipolar voltage amplitudes in the septum. We furthermore showed, using computer modelling, that these LBBB-associated low septal unipolar voltage amplitudes are functional, because they completely abolished during a normal synchronous activation sequence. Furthermore, modifying the activation sequence by ventricular pacing substantially changed unipolar voltage amplitudes in patients and computer models.

Together these results strongly indicate that activation sequence is a major determinant of local unipolar voltage amplitude and that the low septal voltage amplitudes in LBBB hearts can be explained exclusively by the specific activation sequence.

The septum: a challenging myocardial region

In our data, endocardial EAM LBBB patients demonstrated smaller unipolar voltage amplitudes in the septum compared to other LV segments. This difference was substantial (average 4.5 mV [~33%] lower) and present in every single patient. More interesting, these regional unipolar voltage amplitudes differences disappeared after restoring a normal activation sequence in the computer models, indicating that these low septal voltage amplitudes are exclusively due to an LBBB activation sequence, as geometrical and tissue properties were kept constant in the model. Low septal unipolar voltage amplitudes have not previously been associated with an LBBB activation sequence, although substantial regional variation in septal voltage amplitudes have been reported by Tung et al. [START_REF] Tung | Directional Influences of Ventricular Activation on Myocardial Scar Characterization: Voltage Mapping With Multiple Wavefronts During Ventricular Tachycardia Ablation[END_REF] .

Detailed EAM studies by Auricchio et al. showed that an LBBB activation sequence is characterized by a slow impulse conduction originating from the RV free wall gradually propagating to the LV lateral wall. [START_REF] Auricchio | Characterization of left ventricular activation in patients with heart failure and left bundle-branch block[END_REF] Long transseptal times, typically >30 ms, were also demonstrated by endocardial mapping in LBBB patients [START_REF] Prinzen | Is echocardiographic assessment of dyssynchrony useful to select candidates for cardiac resynchronization therapy? Echocardiography is not useful before cardiac resynchronization therapy if QRS duration is available[END_REF] and in canine LBBB hearts [START_REF] Strik | Transseptal conduction as an important determinant for cardiac resynchronization therapy, as revealed by extensive electrical mapping in the dyssynchronous canine heart[END_REF] . A long transseptal time reflects an electrical separation between the RV and LV and slow transseptal conduction. The slow conduction across the septum during LBBB activation may be explained by an activation sequence propagating perpendicular to the fiber orientation. Propagation velocity perpendicular to the fiber orientation is half of that along the fiber direction. [START_REF] Spach | The discontinuous nature of propagation in normal canine cardiac muscle. Evidence for recurrent discontinuities of intracellular resistance that affect the membrane currents[END_REF] Interestingly, LBBB even in the absence of reduced LV function already causes regional changes in conduction velocity by lateralization of connexin43. [START_REF] Spragg | Abnormal conduction and repolarization in late-activated myocardium of dyssynchronously contracting hearts[END_REF] Thus, low septal unipolar voltage amplitudes (in the absence of scar) during LBBB activation sequences may be partially explained by slow conduction across the septum.

Pacing-induced voltage changes

The substantial pacing-induced voltage amplitude changes in the LV free wall support the idea that activation sequence created low voltage amplitudes are not a specific property of the septum. Moreover, because during RV pacing there is a whole range of activation sequences within the LV free wall, also a wide range of changes (both increases and decreases) were observed. Clearly, these changes were substantial and more variable at the epicardium (mean absolute difference: 3.6-6.2 mV corresponding to 40-53% of intrinsic value; IQR differences: 53-63% of intrinsic value) compared to the endocardium (mean absolute difference: 3.3-3.8 mV corresponding to 28-30% of intrinsic value; IQR differences: 37-40% of intrinsic value). Therefore, in using voltage maps for scar delineation, activation sequence needs to be taken into account. [START_REF] Tung | Directional Influences of Ventricular Activation on Myocardial Scar Characterization: Voltage Mapping With Multiple Wavefronts During Ventricular Tachycardia Ablation[END_REF] The fact that we observe these wavefront dependencies of unipolar voltage amplitudes may be surprising, because they are considered to have a larger field of view compared to bipolar measurements. Several studies already investigated pacing-induced voltage changes, but these focused solely on bipolar measurements. [START_REF] Brunckhorst | Impact of changing activation sequence on bipolar electrogram amplitude for voltage mapping of left ventricular infarcts causing ventricular tachycardia[END_REF][START_REF] Blauer | Sensitivity and specificity of substrate mapping: an in silico framework for the evaluation of electroanatomical substrate mapping strategies[END_REF] Blauer et al. confirmed in a bidomain model that different catheter inclination angles substantially impact the accuracy of identifying lesions by bipolar mapping. [START_REF] Blauer | Sensitivity and specificity of substrate mapping: an in silico framework for the evaluation of electroanatomical substrate mapping strategies[END_REF] Brunckhorst et al. [START_REF] Brunckhorst | Impact of changing activation sequence on bipolar electrogram amplitude for voltage mapping of left ventricular infarcts causing ventricular tachycardia[END_REF] demonstrated in 11 ventricular tachycardia (VT) patients that RV pacing produced a >50% change in bipolar voltage at 28%

sites and >100% at 10% sites. Therefore, it appears that, the range of fractional differences induced by activation sequence is similar for unipolar and bipolar voltage measurements.

Pacing-induced changes in unipolar voltage amplitudes have been investigated in two studies to date. Tung et al. [START_REF] Tung | Directional Influences of Ventricular Activation on Myocardial Scar Characterization: Voltage Mapping With Multiple Wavefronts During Ventricular Tachycardia Ablation[END_REF] investigated the influence of changing wavefront on scar characterization (by low unipolar and bipolar recordings) in 29 VT patients. A variability of 22% for bipolar and 14% for unipolar voltage characterization of scar was observed with different activation sequences. Amorós-Figueras et al. [START_REF] Amoros-Figueras | Endocardial infarct scar recognition by myocardial electrical impedance is not influenced by changes in cardiac activation sequence[END_REF] analysed voltage amplitude changes during intrinsic rhythm and RV pacing. In healthy myocardium, the IQR of change was 31% for endocardial unipolar voltage amplitudes, remarkably close to the IQR change of 37% that we observed after RV pacing in the computer models at the endocardium.

Clinical implications

To date, unipolar and bipolar voltage mapping are commonly used methods in clinical practice for invasive scar delineation. [START_REF] Priori | ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC)Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC)[END_REF] In this study, we demonstrate that unipolar voltage amplitudes can be affected by different activation sequences and changes up to half of the intrinsic value are common. Furthermore, lower unipolar voltage amplitudes are present in the septum in patients with a LBBB activation sequence.

These findings advocate for additional methods of invasive scar delineation. One potential method would be integration of DE-CMR data into the EAM system. [START_REF] Nguyen | A novel approach for left ventricular lead placement in cardiac resynchronization therapy: Intraprocedural integration of coronary venous electroanatomic mapping with delayed enhancement cardiac magnetic resonance imaging[END_REF] Alternative novel approaches for more accurate invasive scar delineation include the use of impedance measurements [START_REF] Amoros-Figueras | Endocardial infarct scar recognition by myocardial electrical impedance is not influenced by changes in cardiac activation sequence[END_REF] and the wavefront-independent omnipolar electrograms. [START_REF] Deno | Orientation-Independent Catheter-Based Characterization of Myocardial Activation[END_REF][START_REF] Masse | Resolving Myocardial Activation With Novel Omnipolar Electrograms[END_REF]

Study limitations

The study included both endocardially and epicardially mapped patients, which possibly affected voltage amplitudes. As such, this study focusses on relative voltage differences rather than absolute values. Moreover, we connected the two patient populations by using computer simulations from which we could extract both endocardial and epicardial data, covering the entire spectrum of patient data.

The present study primarily investigates unipolar voltage amplitudes and therefore additional studies are necessary to investigate the influence of activation sequence on bipolar voltage amplitudes.

Conclusion

This study demonstrates that an LBBB activation sequence in patients is associated with ~33% lower unipolar voltage amplitudes in the septum, and that these regional voltage amplitude differences resolve once a normal activation sequence is restored in a computer simulation. We additionally demonstrated that pacing-induced changes in the activation sequence substantially affect unipolar voltage amplitudes by ~30-50% of intrinsic value, particularly at the epicardium, while the direction of change can be both positive or negative. These findings are relevant for interpretation of voltage maps.

and 6 respectively

 6 ). Similar to the patient data, the direction of pacing-induced unipolar voltage changes could be either positive or negative and larger differences were present in electrograms with larger voltage amplitudes during intrinsic rhythm (Bland-Altman analyses in Figure5C and 6C). Overall there was a positive correlation between the mean-and absolute unipolar voltage differences (from the Bland-Altman plots in Figure5Cand 6C) for both endocardial and epicardial unipolar voltage amplitudes (R: 0.1477-0.3881, p-values<0.001).Mean epicardial unipolar voltage was distinct for different activation sequences (intrinsic: 17.4±5.9, RV pacing: 18.7±8.3, and LV pacing: 19.6±6.4 mV; Figure5A), with the greatest change in unipolar voltage difference occurring during LV pacing

Figure 1 Regional

 1 Figure 1 Regional differences on endocardial voltage amplitudes in patients with LBBB. (A) Mean±SD of endocardial unipolar voltage amplitudes per patient grouped according to location. Lower unipolar voltage amplitudes are consistently present in the septum. * indicates p ≤0.001 based on Kruskall-Wallis test. (B) Endocardial voltage maps of 2 representative patients with LBBB. UnipV = unipolar voltage amplitude.

Figure 2 Regional

 2 Figure 2 Regional differences in endocardial voltage amplitudes in a computer model. Activation (left) and voltage (middle) maps with accompanying location bar graphs (right) of simulations s2 during an LBBB (top panels) and normal (bottom panels) activation sequence. Unipolar voltage amplitudes are smaller in the septum and inferior during LBBB. These lower voltage amplitudes disappear when a normal activation sequence is restored. UnipV = unipolar voltage amplitude.

Figure 3 Overview

 3 Figure 3 Overview of differences in voltage amplitude values after changing the sequence of activation by pacing. Differences are depicted in percentage of intrinsic value. The red boxplots indicate the differences induced by RV pacing, while the green boxplots indicate the LV pacing-induced changes. The central mark in the box indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively.The whiskers extend to the most extreme data points not considered outliers.Changes are grouped for epicardial EAM patients (n=9) and computer simulations (n=3) with both endocardial and epicardial EAM points.The IQR of RV pacing-induced changes of patients with epicardial EAM is very similar to the changes observed at the epicardium in the computer models. The IQR of unipolar voltage changes is generally larger for the epicardium compared to the endocardium and higher after LV pacing compared to RV pacing. UnipV = unipolar voltage amplitude.

Figure 4 Pacing

 4 Figure 4 Pacing-induced voltage changes at the epicardium in patients. (A) Mean±SD of endocardial unipolar voltage amplitudes per patient during intrinsic rhythm and RV pacing. * indicates p ≤0.05 based on Mann Whitney u-test. (B) Epicardial (coronary venous) voltage map of a representative patient (p19) during intrinsic rhythm and RV pacing. The mean voltage amplitude does not change much, but substantial changes per electrogram are present. (C) Bland-Altman analyses for voltage amplitudes differences between intrinsic rhythm and RV pacing. RV pacing induces substantial changes in voltage amplitudes, although the direction of change varies. The larger the voltage amplitude during intrinsic rhythm, the larger the change after pacing. UnipV = unipolar voltage amplitude.

Figure 5 Pacing

 5 Figure 5 Pacing-induced voltage changes at the epicardium in computer models. (A) Epicardial unipolar voltage maps of computer simulation s1 during different activation sequences induced by pacing. The septal quarters are displayed in white as the epicardium does not contain septum. (B) Mean unipolar voltage amplitudes for every computer model during different activation sequences. (C) Bland-Altman analyses comparing the difference between intrinsic (LBBB) and paced unipolar voltage amplitudes. Similarly, like the clinical data, the mean unipolar voltage amplitudes do not change much, but substantial point-by-point changes are observed. UnipV = unipolar voltage amplitude.

Figure 6 Pacing

 6 Figure 6 Pacing-induced voltage changes at the endocardium in computer models. (A) Endocardial unipolar voltage maps of computer model s1 during different activation sequences induced by pacing. (B) Mean unipolar voltage for every computer model during different activation sequences. (C) Bland-Altman analyses comparing the difference between intrinsic (LBBB) and paced unipolar voltage amplitudes. UnipV = unipolar voltage amplitude.

  Foundation, and by a restricted grant of Biologic Delivery Systems, Division of Biosense Webster a Johnson & Johnson Company. The authors gratefully acknowledge financial support by Fondazione Cardiocentro Ticino, the Theo Rossi di Montelera Foundation, the Mantegazza Foundation, and FIDINAM to the Center of Computational Medicine in Cardiology. UCN received a Biosense Webster, MSD, and Biotronik. AA has been a consultant to Medtronic, Boston Scientific, LivaNova and Abbott, and has received speakers' fees from Medtronic, Boston Scientific and LivaNova. FR received speaker fees from Abbott and Bayer. KV received research grants from Medtronic and Abbott. The authors declare that no competing interests exist.

	Table 1
	Patient characteristics
	Patient Age Sex BMI	LVEF	Type	NYHA Conduction QRSd	Wavefronts	Mapping	Nr of
				(m	2 /kg)	(%)				(ms)			EGMs
	p1	73	F	30		39	NCMP 3	LBBB	139	Intrinsic	LV endo	167
	Kootstra Talent Fellowship research grant from Maastricht University and was p2 70 M 23 35 ICMP 2 LBBB 179 Intrinsic LV endo 88
	p3 additionally funded by a research grant from the Dutch Heart Foundation (grant 54 M 32 34 ICMP 2 LBBB 165 Intrinsic LV endo 131 p4 70 M 24 21 NCMP 2 LBBB 160 Intrinsic LV endo 206
	#2015T61). The funders had no role in study design, data collection and analysis, p5 69 F 30 17 ICMP 3 LBBB 185 Intrinsic LV endo 199
	p6 decision to publish or preparation of the manuscript. 69 F 31 35 ICMP 3 LBBB 156 p7 69 M 26 45 NCMP 2 LBBB 154	Intrinsic Intrinsic	LV endo LV endo	236 163
	p8	85	M	31		32	NCMP 3	LBBB	180	Intrinsic	LV endo	244
	p9	69	M	24		28	ICMP	2	LBBB	170	Intrinsic	LV endo	159
	p10 Financial disclosures 75 M 27 34 NCMP 2 p11 80 M 26 29 ICMP 2	LBBB IVCD	142 136	Intrinsic/RV Intrinsic/RV	LV epi (veins) LV epi (veins)	50 26
	p12	42	F	31		28	NCMP 2	LBBB	160	Intrinsic/RV	LV epi (veins)	27
	FWP has received research grants from Medtronic, St. Jude Medical, LivaNova, p13 75 F 23 15 NCMP 3 LBBB 122 Intrinsic/RV LV epi (veins) 47
	p14	71	F	26		29	NCMP 2	LBBB	156	Intrinsic/RV	LV epi (veins)	41
	p15	65	F	27		10	NCMP 3	LBBB	150	Intrinsic/RV	LV epi (veins)	41
	p16	52	M	34		24	NCMP 3	LBBB	136	Intrinsic/RV	LV epi (veins)	19
	p17	57	F	25		26	NCMP 2	IVCD	158	Intrinsic/RV	LV epi (veins)	34
	p18	60	F	26		20	NCMP 2	LBBB	132	Intrinsic/RV	LV epi (veins)	42
	p19	61	M	21		32	NCMP 2	LBBB	154	Intrinsic/RV	LV epi (veins)	48
	p20	58	M	28		26	NCMP 3	LBBB	150	Intrinsic/RV	LV epi (veins)	13
	p21	80	M	25		24	NCMP 3	LBBB	156	Intrinsic/RV	LV epi (veins)	29
	BMI=body-mass-index, ICMP=ischemic cardiomyopathy, EGMs=electrograms,
	F=female, IVCD=intraventricular conduction defect, LBBB=left bundle branch block,
	LV=left ventricle, LVEF=LV ejection fraction, M=male, NCMP=non-ischemic CMP,
	NYHA=New York Heart Association functional class, QRSd=QRS duration, RV=right
	ventricle.

Funding

This work was granted access to HPC resources of CEA-TGCC under GENCI allocation x2016037379 and was supported by grants from the Swiss National Science Foundation under project 32003B_165802, from the Swiss Heart


Estelle Lebegue
email: estelle.lebegue@niv-rennes1.fr

Carole Farre

Catherine Jose

Joëlle Saulnier

Florence Lagarde

Yves Chevalier

Carole Chaix

Nicole Jaffrezic-Renault

Estelle Lebègue

Joelle Saulnier

Biomimetic vesicles for electrochemical sensing

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Synthetic or natural surfactants that can self-assemble as bilayers are the elementary molecules of vesicles or liposomes. The most common surfactants forming liposomes are phospholipids, the surface-active compound present in cell membranes; liposomes can then mimic biological membranes. The structure of vesicles depends on the dispersion process [START_REF]New RCC: Liposomes: A Practical Approach[END_REF]. The most common structures are multilamellar large vesicles (MLV), small unilamellar vesicles (SUV) of submicron diameter made of a single closed bilayer membrane, and giant unilamellar vesicles (GUV) of a few tens of microns in diameter.

Water-soluble agents can be encapsulated in the inner cavity of the vesicle; water-insoluble agents can be incorporated into the bilayer membrane. Membrane permeability can be greatly reduced by the addition of cholesterol to the bilayer membrane of phospholipids [START_REF] Demel | Effects of temperature and cholesterol on the glucose permeability of liposomes prepared with natural and synthetic lecithins[END_REF]. In addition, phospholipids, as the main component of biomimetic vesicles, have distinct advantages over synthetic materials including lack of toxicity, biodegradability and biocompatibility.

A c c e p t e d m a n u s c r i p t

Consequently, biomimetic vesicles are utilized as versatile carriers in medical, therapeutic, and analytical applications.

Biomimetic vesicles have attracted great attention for applications in the biosensor field over a number of decades as a means to amplify the signal [START_REF] Edwards | Liposomes in analyses[END_REF][START_REF] Liu | Liposomes in biosensors[END_REF]. Biomimetic vesicles can encapsulate various signal probes including dyes, enzymes, salts, chelates, electrochemical and chemiluminescent probes. Consequently, biomimetic vesicles are an excellent candidate component for biosensors to transduce and amplify signals.

Biomimetic vesicles can also serve as cell membrane models because, like liposomes, they have similar components and structure to cell membranes. They can therefore provide a simple platform to simplify the investigated system in the research into related interactions and physiological phenomena with cells [START_REF] Jelinek | Biomimetic approaches for studying membrane processes[END_REF].

The technology for the surface modification of vesicles ensures that a variety of biorecognition elements can be conjugated to the surface of liposomes, including peptide, protein, enzyme, antigen, biotin, avidin and DNA segments. For lysis strategy, surfactant (or detergent) and natural lytic agents, such as pore-forming toxins [START_REF] Lebegue | Responsive polydiacetylene vesicles for biosensing microorganisms[END_REF], have been reported. This review paper focuses on biomimetic vesicles for electrochemical biosensing.

Electrochemistry is a sensitive, fast and convenient analytical technique widely used in the sensor field. There are several advantages to electrochemical detection. First, the electrochemical signal is a stable and sensitive signal that can be rapidly and easily detected.

Secondly, the electrochemical devices are readily miniaturized for the development of portable sensors, without the need for larger detectors. Therefore, electrochemical biosensors based on electrochemical probes encapsulated in biomimetic vesicles as a signal amplifier, have attracted great attention in biochemical analysis.

Biomimetic vesicles in electrochemical bioassays

The general schematic for how a liposome might be deployed in a simple biosensor is illustrated in Fig. 1. A number of different types of vesicle-based assays have been reported using biomimetic vesicles as a signal amplifier, including vesicle immunosorbent assay (VISA) (Fig. 1A) and vesicle DNA hybridization assay (V-DNA-HA) (Fig. 1B), electrochemical redox probes encapsulated in biomimetic vesicles as signal amplifiers being successfully utilized in these assays. technique. Ascorbic acid, uric acid, ferrocene carboxylic acid, potassium ferrocyanide and ferrocene were used as the electrochemical probes for the detection of carcinoembryonic antigen at 5 x 10 -7 g/mL [START_REF] Viswanathan | Disposable electrochemical immunosensor for carcinoembryonic antigen using ferrocene liposomes and MWCNT screen-printed electrode[END_REF], cholera toxin at 1x10 -16 g [START_REF] Viswanathan | Electrochemical immunosensor for cholera toxin using liposomes and poly(3,4-ethylenedioxythiophene)-coated carbon nanotubes[END_REF] and insulin at 10 pg/mL [START_REF] Viswanathan | Dual electrochemical determination of glucose and insulin using enzyme and ferrocene microcapsules[END_REF], respectively.

Nucleic acid sequences and Escherichia coli O157 based on V-DNA-HA [START_REF] Chumbimuni-Torres | Amplified potentiometric transduction of DNA hybridization using ion-loaded liposomes[END_REF][START_REF] Liao | Attomole DNA Electrochemical Sensor for the Detection of Escherichia coli O157[END_REF] for electrochemical signal amplification, using its substrate ascorbic acid 2-phosphate (AA-p).

A detection limit as low as 0.007 ng/mL for PSA was detected using this approach. ALP loaded vesicles have also been used for HIV-p24 antigen detection. The produced ascorbic acid donated an electron to the graphene/g-C3N4 nanohybrid based photoelectrode, provoking an increased photocurrent signal. A detection limit of 0.63 pg/mL was obtained with the proposed PEC method [17].

Biomimetic vesicles in electrochemical microfluidic chip devices

In was captured through anti-CTB (cholera toxin subunit B) antibody conjugated magnetic beads, a GM1-containing vesicle being then linked to CTB; the magnetic immobilization of the magnetic bead, the washing step, the vesicle lysing and the ferri/ferrocyanide detection were performed in the microfluidic chip device. A detection limit of 31.7 ng/mL was obtained [21  ].

Mimicking cell membranes for biosensing

Synthetic vesicles or liposomes based on phospholipids mixed with polyacetylene have been extensively used for mimicking cell membranes [START_REF] Jelinek | Biomimetic approaches for studying membrane processes[END_REF]. For this purpose, the molecular system produced should retain, as much as possible, the physico-chemical properties of the actual cell membrane (such as lipid and protein organization and fluidity). The elaboration of biosensors for hemolytic bacteria is based on the detection of their emitted toxin that has the specific property of forming pores in the cell membrane. The redox-encapsulated vesicle is lysed under the influence of the species presenting pore-forming functions such as bacterial toxins (Figure 2).

Biomimetic vesicles for biosensing towards potential commercialization

In this final section, progress towards the commercialization of a vesicle-based diagnostic chip and related techniques are discussed.

A new technology based on a commercial personal glucosemeter has been developed to quantitatively detect a broad range of disease biomarkers and was proven to be portable, economical and conveniently accessible. Measurements were performed based on releasing encapsulated glucose from antibody-tagged vesicles and subsequently detecting the released glucose using a commercial glucosemeter. The innovative aspect of this approach lies in the quantification of target biomarkers through the detection of glucose, thus expanding the original concept, biomarker phospho-p53 has been detected, with a detection limit of 50 pg/mL

[30] and aflatoxin B1, a contaminant of foodstuffs, has been detected with a detection limit of 0.6 pg/mL [31]. Thrombin was also detected using a commercial glucosemeter: 29-mer aptamer against thrombin functionalized glucoamylase encapsulated vesicles were used, allowing a secondary enzymatic amplification, in the presence of the enzymatic substrate amylose [32]. Redox-inactive molecules-encapsulated vesicles could also be used in electrochemical bioassays. It has been reported that electrochemical nanoimpact titration could allow the determination of the attomole content of redox-inactive molecules such as glutathione within individual vesicles, by using copper (II) as a catalyst [37].

A continuous process to produce hybrid liposome/protein microvesicles has been reported using microfluidics and electrospray [38  ], opening the way to an industrial process for producing biomimetic vesicles.

Acknowlegments : Estelle Lebègue thanks the EC for financial support through a Marie Sklodowska Curie Individual Fellowship.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as :

•Paper of special interest Biosens Bioelectron 2016, 80:426-432. Telomerase, which has been detected in almost all kinds of cancer tissues, is considered as an important tumor marker for early cancer diagnostics. In the present study, an electrochemical method based on liposomal signal amplification platform is proposed for simple, PCR-free, and highly sensitive detection of human telomerase activity, extracted from A549 cells. In this strategy, telomerase reaction products, which immobilized on streptavidin-coated microplate, hybridized with biotinylated capture probes. Then, dopamine-loaded biotinylated liposomes are attached through streptavidin to biotinylated capture probes. Finally, liposomes are ruptured by 21. Bunyakul N, Promptmas C, Baeumner AJ: Microfluidic biosensor for cholera toxin detection in fecal samples. Anal Bioanal Chem 2015, 407:727-736 This biosensor was previously developed and tested in buffer solutions only, using either fluorescence or electrochemical detection strategies. The microfluidic devices were made from polydimethylsiloxane using soft lithography and silicon templates. Cholera toxin subunit B (CTB)-specific antibodies immobilized onto superparamagnetic beads and ganglioside GM1containing liposomes were used for CTB recognition in the detection system. Quantification of CTB was tested by spiking it in human stool samples. Subsequently, cross-reactivity using the heat-labile Escherichia coli toxin was investigated using the electrochemical microfluidic immunosensors and was determined to be negligible. 27. Nasir MZM, Jackman JA, Cho NJ, Ambrosi A, Pumera M : Detection of amphipathic viral peptide on screen-printed electrodes by liposome rupture impact voltammetry. Anal Chem 2017, 89 :11753-11757. Detection of infectious viruses and disease biomarkers is of utmost importance in clinical screening for effective identification and treatment of diseases. We demonstrate here the use of liposome rupture impact voltammetry for the qualitative detection of model amphipathic viral peptide on a screen-printed electrode. This novel, proof-of-concept method was proposed for the quick and reliable detection of viruses by nonfaradaic liposome rupture impact voltammetry with the aid of 1,2-dioleoyl-sn-glycero-3-phosphocholine liposomes. In the proposed assay, the magnetic beads-liposome hybrids offered excellent sensitivity due to primary amplification via releasing numerous glucoamylase from a liposome followed by a secondary enzymatic amplification. The use of portable quantitative device PGM bypasses the requirement of complicated instruments and sophisticated operations, making the method simple and feasible for on-site detection. 38. Gomez-Mascaraque LG, Casagrande Sipoli C, de La Torre LG, Lopez-Rubio A: A step forward towards the design of a continuous process to produce hybrid liposome/protein microcapsules. J Food Eng 2017, 214 :175-181. Microfluidics and electrospraying, two revolutionary technologies with industrial potential for the microencapsulation of lipophilic bioactive ingredients, have been combined to produce hybrid liposome/protein microencapsulation structures in a semi-continuous process, reducing the number of steps required for their manufacture. The Tesla design showed the best mixing performance, as observed by fluorescence microscopy, so it was selected to be assembled to an electrospraying apparatus. The proposed in-line setup was successfully used to produce the micron-sized encapsulation structures, as observed by scanning electron microscopy.

••Paper of outstanding interest

Gao T, Gu

Figure 1A :

 1A Figure 1A : Vesicle immunosorbent assay (VISA). Vesicles are presented as hemivesicles to show the inside.

Figure 1B :

 1B Figure 1B : Vesicle DNA hybridization assay (V-DNA-HA). Vesicles are presented as hemivesicles to show the inside.

  the 90s, a series of vesicle-based immunoassays with a strip format, working on a lateral flow principle, were developed; the first one was proposed by Durst [18] for the detection of the herbicide alachlor. Recently, microfluidic chips produced by means of microtechnology facilities, were preferred. The advantages of the vesicle-based microfluidic chip are the shortening of detection time to only 20 min, and a significantly lower limit of detection, down to pmol/mL. For example, a low concentration of Dengue fever virus was detected using the vesicle-based microfluidic chip through a sandwich DNA hybridization assay [19,20]. Cholera toxin was detected in fecal samples by Baeummer et al using a microfluidic biosensor; the toxinA c c e p t e d m a n u s c r i p t

Figure 2 :

 2 Figure 2 : Amperometric biosensing of pore-forming bacterial toxin based on biomimetic vesicle encapsulation of redox probes. Vesicles are presented as hemivesicles to show the inside.

  applicability of the glucosemeter by broadening the range of target biomarkers instead of detecting only one analyte, glucose. Because of the bilayer membrane of biomimetic vesicles, which can accommodate tens of thousands of glucose molecules, the sensitivity was greatly enhanced by using glucose-encapsulating vesicles as signal output and amplifier. Based on this A c c e p t e d m a n u s c r i p t

Following

  the same design, DNA methyltransferase activity was detected [33  ]. Several biomimetic vesicle-based assays were conducted in real samples: AFB1 was detected in contaminated/spiked peanuts samples and serum samples, using glucosemeter [31; the practicability of the liposomes-amplified PEC sensing strategy was demonstrated by assaying human serum samples and its universality was also demonstrated by developing it into a sensitive microRNA detection method [17].

Figure 3 :

 3 Figure 3 : Working Principle of Enzyme-Encapsulated Liposome-Linked Immunosorbent Assay with Beads/Protein/Liposome "Sandwich" Structure [32].

A c c e p t e d m a n u s c r i p t 12 .

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  s c r i p t methanol and the released-dopamine is subsequently measured using differential pulse voltammetry technique by multi-walled carbon nanotubes modified glassy carbon electrode. 13. Bhuvana M, Dharuman V: Influence of alkane chain lengths and head groups on tethering of liposome-gold nanoparticle on gold surface for electrochemical DNA sensing and gene delivery. Sens Actuators B: chemical 2016, 223:157-165. 14. Divya KP and Dharuman V: Supported binary liposome vesicle-gold nanoparticle for enhanced label free DNA and protein sensing. Biosens Bioelectron 2017, 95: 168-173. 15. Imran H, Manikandan PN, Dharuman V: Graphene oxide supported liposomes for efficient label free electrochemical DNA biosensing. Sens Actuators B: chemical 2018, 260:841-851. 16. Qu B, Guo L, Chu X, Wu DH, Shen GL, Yu RQ: An electrochemical immunosensor based on enzyme-encapsulated liposomes and biocatalytic metal deposition. Anal. Chim. Acta 2010, 663:147-152. 17. Zhuang J, Han B, Liu W, Zhou J, Liu K, Yang D, Tang D, Liposome-amplified photoelectrochemical immunoassay for highly sensitive monitoring of disease biomarkers based on a split-type strategy. Biosens Bioelectron 2018, 99:230-236. 18. Siebert STA, Reeves SG, Durst RA: Liposome immunomigration field assay device for alachlor determination. Anal Chim Acta 1993, 282:297-305. 19. Goral VN, Zaytseva NV, Baeumner AJ: Electrochemical microfluidic biosensor for the detection of nucleic acid sequences. Lab Chip 2006, 6:414-421. 20. Kwakye S, V. N. Goral VN, Baeumner AJ: Electrochemical microfluidic biosensor for nucleic acid detection with integrated minipotentiostat. Biosens. Bioelectron 2006, 21 :2217-2223.

  22. Kolusheva S, Shahal T, Jelinek R: Peptide-membrane interactions studied by a new phospholipid/polydiacetylene colorimetric vesicle assay. Biochemistry 2000, 39:15851-15859. s c r i p t 23. Xu D and Cheng Q: Surface-Bound Lipid Vesicles Encapsulating Redox Species for Amperometric Biosensing of Pore-Forming Bacterial Toxins. J Am Chem Soc 2002, 124:14314-14315. 24. Peng T, Cheng Q, Stevens RC: Amperometric Detection of Escherichia coli Heat-Labile Enterotoxin by Redox Diacetylenic Vesicles on a Sol-Gel Thin-Film Electrode. Anal. Chem. 2000, 72:1611-1617. 25. Kim HJ, Bennetto HP, Halablab MA, Choi C, Yoon S: Performance of an electrochemical sensor with different types of liposomal mediators for the detection of hemolytic bacteria. Sens. Actuators B 2006, 119:143-149. 26. Thet NT and Jenkins ATA. An electrochemical sensor concept for the detection of bacterial virulence factors from Staphylococcus aureus and Pseudomonas aeruginosa. Electrochem. Commun. 2015, 59:104-108.

  34. Simões MG, Hugo A, Alves P, Pérez PF, Gómez-Zavaglia A, Simões PN: Long term stability and interaction with epithelial cells of freeze-dried pH-responsive liposomes functionalized with cholesterol-poly(acrylic acid). Coll Surf B: Biointerfaces 2018, 164:50-57 35. Zhang LF and Granick S: How to stabilize phospholipid liposomes (using nanoparticles). Nano Lett 2006, 6:694-698. 36. Menon P, Yin TY, Misran M: Preparation and characterization of liposomes coated with DEAE-Dextran. Coll Surf A: Physicochem. Eng. Aspects 2015, 481:345-350 37. Cheng W and Compton RG: Measuring the content of a single liposome through electrocatalytic nanoimpact "titrations". ChemElectroChem 2016, 3 :2017 -2020

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