Imad Adjali

Ayichatou Gueye

Shermila Mostarshedi

Benoit Poussot

Florence Nadal

Jean-Marc Laheurte

Keywords: electromagnetic coupling, highly coupled dipoles, impedance matrix, RFID antenna matching

What is the problem being addressed by the manuscript and why is it important to the Antennas & Propagation community?

This paper investigates the effects of electromagnetic coupling between randomly distributed dipoles on their matching properties from a statistical point of view. The well-established UHF RFID may be used in a context of high densities of tags. The proximity of tags can result in an important electromagnetic coupling between tags which alters antenna's key parameters (e.g. radiation pattern and matching). Consequently the quality of communication between the RFID reader and the tags will be degraded. The statistical study of coupling effects can help the RFID engineer to maintain the link quality even in a context of high density of tags.

What is the novelty of your work over the existing work?

A pure deterministic modelling of tag behaviour in random configurations would not be realistic. Although intensive research on mutual coupling is available in literature, the impact of the mutual coupling for high densities of RFID tags has never been modelled statistically. This work presents the matching evaluation of dipoles from a statistical point of view. The novelty is to be able to predict the matching properties of a set of random dipoles by their density. Combined to a future study on radiation pattern, this method offers the possibility to express the RFID system parameters (e.g. read-range) statistically.

I. INTRODUCTION

UHF RFID (Radio Frequency IDentification) technologies have a long established role for tracking and identification in various fields of application such as food traceability [START_REF] Chen | Using RFID technology in food produce traceability[END_REF], logistics management in textile industry [START_REF] Hum | Fabric area network-a new wireless communications infrastructure to enable ubiquitous networking and sensing on intelligent clothing[END_REF] or supply chain management [START_REF] Gaukler | Applications of RFID in supply chains[END_REF]. For instance, UHF RFID tags installed into hotel and hospital linens, towels, uniforms and medical gowns allow for the real-time management and tracking of the laundry activity. This use case is characterized by huge concentrations of tagged items in reduced volumes during the shipping, the storage and the washing steps. Other use cases can be found in the shoe or clothing retail where items can be stacked or aligned yielding to high densities of RFID tags. High density of tags can give rise to an important electromagnetic coupling between tag antennas which alters antenna's key parameters such as the radiation pattern and the matching properties. The decrease in antenna performance ultimately degrades the RFID system performances, e.g. the read range and the read rate of tags. The empirical strategies which are currently proposed rely on either a multiplication of the number of reading systems to bring diversity [START_REF] Wang | PlacementofmultipleRFID reader antennas to maximise portal read accuracy[END_REF] or a modification in the tags' environment by using mechanical vibration or electromagnetic stirring [START_REF] Kruest | Systems and methods for stirring electromagnetic fields and interrogating stationary RFID tags[END_REF]. The implementation of these solutions results in a heavy and costly infrastructure which is not suitable for large scale applicability of RFID systems and does not guarantee a total reliability. Color versions of one or more of the figures in this communication are available online at http://ieeexplore.ieee.org.

Digital Object Identifier AP1810-1949

A solution to optimize the RFID infrastructure may reside in a better understanding and a fine modeling of coupling effects but obviously in such a complex random context, a purely deterministic approach is not appropriate. Although intensive research on mutual coupling [6] has been conducted for over fifty years, the impact of the mutual coupling for high densities of RFID tags has never been modeled statistically to predict their group behavior. By using a more rigorous statistical analysis, our motivation is to build the preliminary version of a statistical tool dedicated to the performance evaluation of any design of tag antenna for a given density of randomly distributed tags. This statistical tool will also be used to optimize the number and positions of the reader's antennas.

In this paper, we assume that the electromagnetic interactions observed in a set of randomly distributed commercial tags built around dipole-like antennas are statistically similar to those of straight dipole antennas. Under this assumption, the driven dipole antenna corresponding to the activated (or read) tag is mismatched by the presence of surrounding dipoles corresponding to neighboring unactivated tags. This paper aims at studying statistically the mismatch of the driven dipole antenna surrounded by a randomly distributed set of dipoles. The other consequence of the antenna coupling, i.e. the distortion of the radiation pattern, is not treated in this paper.

The core of the electromagnetic simulation should present two important characteristics. First, the method should estimate the input impedance of a randomly oriented dipole including mutual coupling with randomly distributed loaded surrounding dipoles. Secondly, the method needs to be fast enough to process a large number of random samples. In this paper, we use the Induced ElectroMotive Force (IEMF) technique [START_REF] Brillouin | Sur l'origine de la résistance de rayonnement[END_REF]- [13] which is a simple analytical method to estimate the self and mutual impedances of two electrically thin dipoles including coupling effects. The technique needs to be extended to the case of a set of randomly oriented thin dipoles. Other analytical or numerical techniques, such as in [START_REF] Gera | Simple expressions for mutual impedances[END_REF]- [START_REF] Abdallah | A numerical technique for calculating mutual impedance and element patterns of antenna arrays based on the characteristics of an isolated element[END_REF] can also be used but only for non-inclined dipoles.

The manuscript is organized as follows: in Section II the impedance matrix for two randomly oriented thin dipoles is derived using the IEMF technique and this technique is extended to a set of N thin dipoles. The Numerical Electromagnetics Code (NEC) [START_REF] Burke | Numerical ElectromagneticCode(NEC)[END_REF] is used as a validation reference. In Section III, expressions of the input impedance and the input reflection coefficient are given for a dipole with surrounding dipoles arbitrarily loaded. Then, NEC simulations for isolated and surrounded antennas are compared to IEMF for thin dipoles and to measurements for thick dipoles. In Section IV, NEC and IEMF are both applied Matching Evaluation of Highly Coupled Dipoles Quantified by a Statistical Approach to perform statistical studies of the mismatch of a dipole as a function of the density and the loadings of the surrounding dipoles.

II. EXTENDED IEMF AND IMPEDANCE MATRIX

The IEMF (Induced ElectroMotive Force) method was introduced by Brillouin in 1922 [START_REF] Brillouin | Sur l'origine de la résistance de rayonnement[END_REF]. This method is based on power conservation, which allows calculating the selfimpedance of a small antenna (loop, dipole) with uniform current distribution. In 1929, Pistolkors developed this method in order to calculate the self and mutual resistances of half-wave dipoles [START_REF] Pistolkors | Theradiationresistanceofbeamantennas[END_REF]. In 1941, Schelkunoff showed that the IEMF method may be used to calculate the self-reactance of dipoles [START_REF] Schelkunoff | Theory of antennas of arbitrary size and shape[END_REF]. Later, the IEMF method was used by King [10] to calculate the mutual impedance between staggered parallel antennas of arbitrary length and Baker [11] presented the integral form of the IEMF method for a V shape configuration of dipoles, to be evaluated numerically. In all cases, the self and mutual impedances are obtained supposing a known current distribution (e.g. sinusoidal) on the dipoles. For a few configurations, the expressions of the mutual resistance and reactance are in terms of sine and cosine integrals and can be obtained analytically, for others the evaluation remains numerical. Here we consider two staggered arbitrarily oriented dipole antennas of length l and diameter a as presented in Fig. 1. The center of the dipoles are situated at (y 01 , z 01 ) and (y 02 , z 02 ) and their inclination angles with respect to z axis are noted  1 and  2 respectively for dipole 1 and 2. If both antennas are considered to be infinitely thin (diameter⩽10 -3 λ) [START_REF] Balanis | Antenna theory: analysis and design[END_REF], the current distribution on the dipoles can be assumed to be sinusoidal. The self and mutual impedances can thus be calculated by applying the IEMF method. The expressions of the self impedance as presented in [START_REF] Balanis | Antenna theory: analysis and design[END_REF] are given below:

(1) [START_REF] Hum | Fabric area network-a new wireless communications infrastructure to enable ubiquitous networking and sensing on intelligent clothing[END_REF] where is the characteristic impedance of the medium, and are the cosine and sine integrals respectively, 0.5772istheEuler'sconstantand is the wave number. For the mutual impedance, we use the expressions presented in [11]:

(3)
where the distance from the centre of dipole 2 to some point along its axis is called . E 21 is the electric field radiated by dipole 1 at this point and is given by: (4) where:

(5) (6) I 01 denotes the current magnitude over dipole 1. As we have extended the IEMF formulas presented in [11] to any arbitrary position and orientation of a pair of dipoles in a plane [13], the following equations give the new extended geometrical parameters corresponding to Fig. 1 to be inserted in general equations from ( 4) to ( 6):

(7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)
Using the above formulas for two dipoles, the impedance matrix of a set of thin dipoles can be calculated by the superposition principle. We call this approach "extended IEMF" where the IEMF method is applied for each pair of dipoles in the set. The superposition is valid if adding an extra dipole to a pair of antennas does not alter the previous estimated values of the self and mutual impedances of the pair, thus the condition of minimum scattering is satisfied [START_REF] Roscoe | Large finite array analysis using infinite array data[END_REF]. In the case of thin dipoles, for which the IEMF method is sufficiently accurate, this condition is not restrictive and results in very small distances.

The extended IEMF method needs to be validated by a reference method for two reasons. First, the IEMF method is here applied to an arbitrary position and orientation of two dipoles. Secondly, the IEMF method has originally been developed for two dipoles and here it is extended to a set of dipoles. The extended IEMF for a set of half-wave thin dipoles is validated for two configurations by a comparison with Numerical Electromagnetics Code (NEC) which is a popular simulator for wire antennas based on Method of Moments. This simulator is relatively fast and can be easily automated in order to generate the necessary database for statistical studies. The frequency under consideration is 892 MHz which corresponds to the medium frequency between the European and US UHF RFID bands. In this paper λ corresponds to the wavelength calculated at this frequency.

A. 1D Set of Randomly Distributed Parallel Dipoles

A randomly distributed set of 10 thin identical parallel dipoles of diameter 10 -6 λ is considered (see Fig. 2). The dipole centers are distributed randomly in one dimension. The diagonal elements of the N×N impedance matrix are equal to the self-impedance (Z ii ) of a single dipole. The mutual impedances Z ij = Z ji are obtained for any pair (i, j) of dipole in the array in the absence of the other dipoles. The results are compared to NEC simulations and the absolute error on each element of the matrix is shown in Fig. 3 where the axes correspond to the dipole index (from 1 to 10). Unlike the extended IEMF technique, NEC takes the interactions between all dipoles into account. As shown in Fig. 3, the absolute error values on the real and imaginary parts of the impedance are limited to a maximum of 7 Ω. The core of the NEC code is called by a MATLAB program which allows the automation of the procedure requiring multiple simulations. Fig. 4 depicts the evolution of the computation time of an impedance matrix for both the extended IEMF and NEC as a function of the number of dipoles in the set. The calculation time grows drastically as the number of dipoles in the set increases. This is due to the fact that in NEC, wires are discretized into short segments with linear variations of current and voltage, unlike the IEMF method, where the entire dipole is considered as one segment with a known current distribution. In other words, IEMF has a single complex unknown while NEC deals with n complex unknowns for n discretizations. The IEMF method shows consequently lower computation times. In this paper, NEC results have been obtained by discretizing the half-wave dipoles into 51 segments (λ/100). Fig. 4 shows that in the case of 10 dipoles, the extended IEMF technique is approximately 40 times faster than NEC.

B. 2D Set of Randomly Distributed Dipoles

A randomly distributed set of 10 thin identical dipoles is presented in Fig. 5. The centers are distributed randomly in two dimensions and each dipole has a random orientation under the condition that dipole overlaps are forbidden. Two distribution areas are considered: 2λ×2λ and λ×λ. The impedance matrix of each set has been calculated as previously explained and the result has been compared to the matrix provided by NEC. The absolute error on the real and imaginary parts of the impedance for each element of the matrix is shown in Fig. 6. The maximum absolute error is limited to 9.3 Ω and is observed for the highest antenna density. It is not pertinent to calculate a relative error in this phase because an insignificant error on a low impedance of a matrix element can result in a very high relative error. We will present relative error values for the parameters of interest, i.e. the input impedance of each driven dipole.

III. INPUT IMPEDANCE OF A DRIVEN DIPOLE SURROUNDED BY LOADED DIPOLES

The input impedance of a driven dipole surrounded by loaded dipoles is calculated by using the equivalent N-port network presented in Fig. 7. The input impedance at port i is calculated when all the (N-1) other ports are loaded with Z L . Knowing that the general Z matrix definition leads to and that for each loaded port , the input impedance at port i defined by is given by:

in mod ( 22
)
where is the inverse of the NN matrix obtained by adding Z L to the main diagonal of the general Z matrix of the network everywhere except on the (i, i) th element:

(23) It can be experimentally shown that for physical loads (Z L presenting a positive real part), this matrix is always invertible.

It is important to notice that the absolute errors on the impedance matrices of Fig. 3, Fig. 6 (a) and Fig. 6 (b) result in a relative error on between NEC and extended IEMF defined as . The maximum of the absolute value of this error observed for one driven dipole among 10 is equal to 4% for Fig. 2,4.3% for Fig. 5 (a) and 25% for Fig. 5 (b). In Section IV, we will focus on the statistical matching behavior of the dipoles where those errors will prove to have no influence on the general statistical conclusions. The two following subsections present an experimental validation of for the isolated and surrounding dipoles over a large frequency band.

A. Isolated Dipole

The input impedance of a thin isolated dipole of length 17 cm (≃0.5 λ) and diameter 0.3 m (≃10 -6 λ) has been calculated by IEMF and compared to NEC. The input impedance of a thick isolated dipole of length 17 cm and diameter 1 mm has also been calculated by NEC and compared to measurements. As shown in Fig. 8, the measurements have been performed in the anechoic environment of the Voyantic Cabinet [START_REF]Tagformance pro[END_REF] used to determine the radiation pattern of UHF RFID tags. Thick dipoles have been fabricated with copper wires and fixed to a block of polystyrene whose relative permittivity is close to 1. The driven element has been fed differentially by a 2-port Vector Network Analyzer (VNA). The input impedance of the driven element is finally extracted from the measured [S] matrix using the procedure described in [START_REF] Palmer | Simple broadband measurements of balanced loads using a network analyzer[END_REF]. Fig. 9 presents the real and imaginary parts of the input impedance as a function of the frequency. NEC results for both thin and thick dipoles are in excellent agreement with IEMF and measurements, respectively. This confirms that NEC can be used as a reference code for both thin and thick cases as measurements are not easy to carry out for extremely thin dipoles and IEMF accuracy is limited for thick dipoles.

B. Surrounded Dipole

For the randomly distributed dipoles (1D or 2D) presented in Fig. 2 and Fig. 5, the input impedance is calculated for different driven dipoles. Fig. 10 depicts the input impedance of the driven dipole (either dipole n°1 or n°7) in the parallel configuration (Fig. 2) as a function of the frequency in the case of thin or thick dipoles. Surrounding dipoles are short-circuited. The results not only prove the validity of the procedure (NEC compared to extended IEMF for thin dipoles and to measurement for thick dipoles) but also show a high dispersion of the input impedance in a random configuration. Consequently, each surrounded dipole might be highly mismatched or not according to its position in a random configuration.

IV. STATISTICAL ASSESSMENT

This section is dedicated to the statistical assessment of the mismatch for a surrounded dipole in different random configurations. To this end, we define the reflection coefficient Γ in i for the i th surrounded dipole as follows:

(24)

where the reference impedance Z ref is chosen to be the complex conjugate of the self-impedance of an isolated dipole. The self-impedance of an isolated thin dipole of diameter 0.

A. Reflection Coefficient

Ten identical dipoles are randomly distributed over surfaces of dimensions 44, 33, 22 and 11, i.e. using four different densities. For each dipole density, 200 random configurations have been generated with thin and thick dipoles. The magnitude of the input reflection coefficient(Γ in ) for each of the 10 dipoles has been calculated in dB when all other dipoles are either short-circuited or matched. Therefore, 200×10 = 2000 samples are available for each dipole density. The cumulative distribution functions (CDF) are presented in Fig. 12. An excellent agreement is observed between the three series of CDF obtained by extended IEMF and NEC. This figure leads to an important conclusion: the statistical behavior of thick and thin dipoles is similar even if the intrinsic values of their input impedances and thus the reflection coefficients are quite different. For both loadings and for a given mismatch level, the number of dipoles presenting better matching properties increases as the distribution surface increases (or the dipole density decreases). In the case of short-circuited surrounding dipoles, the percentage of dipoles presenting a reflection coefficient smaller than |Γ in | = -10 dB, is equal to 33% for the highest density (11) and reaches 97.5% for the lowest density (44). Finally, the loads of the surrounding dipoles have an important influence on the input reflection coefficient of a surrounded dipole. We observe that for a given dipole density, the percentage of mismatched antennas decreases if the surrounding dipoles are terminated by a matched load compared to the case when they are short-circuited. For the highest dipole density (11), the percentage of dipoles presenting a reflection coefficient better than |Γ in | = -10 dB increases from 33% with short-circuited surrounding dipoles to 77% with matched surrounding dipoles. This result can be physically explained by the higher radar cross-section of short-circuited dipoles compared to matched dipoles yielding a higher backscattering and a higher disturbance on the driven dipole. It must be mentioned that RFID tag antennas are normally designed to be matched to the RFID chip impedance when the tag is unactivated. Therefore, the matched configuration of surrounding dipoles can be associated to a realistic RFID scenario. However, despite these statistical results, it is not yet possible to conclude on the read rate of the RFID tags, without studying the impact of the mutual coupling on the radiation pattern. In other words, the absolute gain of the tag antenna in the reader's direction includes the mismatch of the driven dipole as well as the radiation gain (or directivity) of this latter in that direction. This analysis is however out of the scope of this paper.

Finally, it must be noticed that the relative errors on input impedances between NEC and extended IEMF observed for a few configurations in Section III do not have significant impact on the general conclusions that can be drawn using the statistical results as observed in Fig. 12. Here the statistical behavior of a large number of configurations is considered and the locally high or low errors are of no interest.

B. Bandwidth

The typical reflection coefficient of an isolated dipole is plotted as a function of frequency in Fig. 13. For a given threshold of the reflection coefficient, the isolated dipole shows a certain frequency bandwidth. The bandwidth of the isolated dipole is used as a reference in the study of the surrounded dipole. The statistical influence of the neighboring dipoles over the bandwidth of a surrounded dipole is performed using the same scenarios as in the previous subsection. The same number of dipoles is randomly distributed using the four different densities, and 100 random configurations have been generated with thin dipoles with short-circuited neighboring dipoles. For each distribution density and for a fixed threshold of the reflection coefficient, the percentage of dipoles covering a given desired bandwidth around 892 MHz can be read over the plots of Fig. 14.

For example, the percentage of dipoles satisfying a 3 MHz bandwidth for a -10 dB threshold is equal to 97.1% for a 44 distribution surface while the percentage reduces to 68.7% for a 11 surface where the coupling effects are more important due to the higher distribution density.

V. CONCLUSION

A set of RFID tag antennas has been modeled by a set of arbitrarily distributed dipoles where the matching properties of the isolated driven tag are modified by the presence of neighboring dipoles. As the position, orientation and density of the tags in the targeted RFID scenarios are random parameters, a purely deterministic approach cannot describe the coupling phenomena realistically. It has been shown that the impedance matrix of the set, including the coupling effects, can be constructed with the rather fast numerical electromagnetic simulator NEC. For thin dipoles, a purely analytical approach based on the IEMF technique has been used with a considerable saving of computation time. It has also been demonstrated that the statistical behavior of thin dipoles simulated by extended IEMF is similar to those of thick dipoles simulated by NEC.

Our approach clearly allows quantifying the statistical influence of the electromagnetic coupling on the matching for different dipole densities. In on-going works, our approach is extended to statistically assess the radiation pattern distortions. In further developments, instead of a single driven element and surrounding coupled dipoles, our formulation will consider a plane wave illuminating all dipoles and modeling the reader signal as suggested in [6]. The ultimate objective is to provide a quick NEC-based tool to evaluate the statistical performance of any design of wire antenna in usecases where high densities of tags are observed. In that sense, this work is the first attempt to handle the group behavior of tag antennas in the UHF RFID context.

  Manuscript received October 2018. I. Adjali, A. Gueye, S. Mostarshedi, B. Poussot and J.-M. Laheurte are with Université Paris-Est, ESYCOM (FRE2028), CNAM, CNRS, ESIEE Paris, Université Paris-Est Marne-la-Vallée, F-77454 Marne-la-Vallée, France (phone: +33160957160; e-mail: imad.adjali@u-pem.fr). F. Nadal is with Université Paris-Est, ESYCOM (FRE2028), CNAM, CNRS, ESIEE Paris, ESIEE Paris, 93162 Noisy-le-Grand, France.

Fig. 1 .

 1 Fig. 1. Two staggered arbitrarily oriented thin dipoles in zOy plane.

Fig. 2 .

 2 Fig. 2. Randomdistributionof10thindipolesovera1.6λlength.

Fig. 3 .

 3 Fig. 3. Absolute error values in Ohms between the extended IEMF and NEC on the real (left) and imaginary (right) parts of the impedance matrix of a randomly distributed set of 10 parallel dipoles.

Fig. 4 .

 4 Fig. 4. Computation time of the impedance matrix obtained by the extended IEMF and NEC as a function of the number of dipoles.

Fig. 5 .

 5 Fig. 5. Random distribution of 10 thin dipoles over two dimensions for two differentdistributionareas(a)2λ×2λand(b)λ×λ.

Fig. 6 .

 6 Fig. 6. Absolute error values in Ohms between the extended IEMF and NEC on the real (left) and imaginary (right) parts of the impedance matrix for a randomly distributed set of 10 dipoles and for two different distribution areas (a)2λ×2λand(b)λ×λ.

Fig. 7 .

 7 Fig. 7. Input impedance of a loaded N-port network.

Fig. 8 .

 8 Fig. 8. Experimental setup for the impedance measurement of a random distribution of 10 thick dipoles in anechoic chamber (one driven dipole and nine surrounding short-circuited dipoles).

Fig. 9 .

 9 Fig. 9. Input impedance of an isolated thin and thick dipole.

Fig. 10 .

 10 Fig. 10. Input impedance of dipole (a) n°1 and (b) n°7 in the set of randomly distributed parallel dipoles (Fig. 2). Two other dipoles have been chosen in the considered 2D randomly distributed sets (Fig. 5) and the results are shown in Fig. 11. We observe more fluctuations of the real and imaginary parts in the case of the highest density of dipoles (Fig. 11.b).

Fig. 11 .

 11 Fig. 11. Input impedance of a dipole in the set of randomly distributed dipoles for different distribution areas (a) dipole n°5 in 2λ×2λ (Fig. 5.a) and (b) dipole n°1 inλ×λ(Fig. 5.b).

3

 3 m is calculated by extended IEMF and NEC: Z self_thin_IEMF = 77.6+j91 Ω and Z self_thin_NEC = 81.7+j93.59 Ω. The self-impedance of an isolated thick dipole of diameter 1 mm is obtained by measurements and NEC: Z self_thick_meas = 93.93+j68.09 Ω and Z self_thick_NEC = 95.65+j67.55 Ω. The statistical studies are conducted through two different axes: the reflection coefficient and the bandwidth of surrounded dipoles.

Fig. 12 .

 12 Fig. 12. Cumulative distribution functions of the input reflection coefficient of a dipole, surrounded by (a) short-circuited dipoles and (b) matched dipoles, as a function of the densities obtained by extended IEMF (thin dipoles) and NEC (thin and thick dipoles).

Fig. 13 .

 13 Fig. 13. Reflection coefficient of the isolated thin dipole under study as a function of frequency.

Fig. 14 .

 14 Fig. 14. Percentage of dipoles presenting at least a given bandwidth for a fixed threshold of the reflection coefficient for different distribution densities (a) 44, (b) 33, (c) 22 and (d) 11.


Hilary Knowles

Nicole Santucci

Joshua Studdert

Hwee Ngee Goh

Keren Kaufman-Francis

Nazmus Salehin

Patrick P L Tam

Pierre Osteil

Differential impact of TGFβ/SMAD signalling activity elicited by Activin A and Nodal on endoderm differentiation of epiblast stem cells

Allocation of cells to an endodermal fate in the gastrulating embryo is driven by Nodal signalling and consequent activation of TGF pathway. In vitro methodologies striving to recapitulate the process of endoderm differentiation, however, used TGF family member Activin in place of Nodal. This is despite Activin not known to have an in vivo role in endoderm differentiation. In this study, five Epiblast Stem Cell lines were subjected to directed differentiation using both Activin A and Nodal to induce endodermal fate. A reporter line harbouring endoderm markers FoxA2 and Sox17 was further analysed for TGF pathway activation and WNT response. We demonstrated that Activin A treated cells remain more primitive streak-like when compared to Nodal treated cells that have a molecular profile suggestive of more advanced differentiation. Activin A elicited a robust TGF/SMAD activity, enhanced WNT signalling activity and promoted the generation of DE precursors. Nodal treatment resulted in lower TGF/SMAD activity, and a weaker, sustained WNT response, and ultimately failed to upregulate endoderm markers. This is despite signalling response resembling more closely the activity seen in vivo. These findings emphasise the importance of understanding the downstream activities of Activin A and Nodal signalling in directing in vitro endoderm differentiation of primedstate epiblast stem cells.

INTRODUCTION

During gastrulation, the epiblast cells are allocated to the precursors of ectoderm, mesoderm and definitive endoderm (DE) lineages. The differentiation of epiblast cells to the DE precursors is driven Transforming Growth Factor (TGF) / Nodal signalling activity as well as WNT signalling [START_REF] Gadue | Wnt and TGF-beta signaling are required for the induction of an in vitro model of primitive streak formation using embryonic stem cells[END_REF][START_REF] Tam | Mouse gastrulation: the formation of a mammalian body plan[END_REF].

Cytokines of the TGF superfamily are functionally divided into two subfamilies; the Nodal subfamily (which includes Activins), and the BMP subfamily [START_REF] Schmierer | TGFbeta-SMAD signal transduction: molecular specificity and functional flexibility[END_REF]. Ligands of both TGF subfamily signal through receptor complexes consisting of type I (or ALK) receptors and constitutively active type II (ActRII) receptors. Upon ligand binding, ALK receptors are phosphorylated and transduce intracellularly via phosphorylation of SMAD proteins [START_REF] Massagué | Smad transcription factors[END_REF]. Nodal and BMP subfamily ligands are transduced through separate downstream SMAD cascades. BMP family ligand binding results in SMAD1, SMAD5 and SMAD8 (SMAD1/5/8) phosphorylation [START_REF] Chang | Smad5 Is Essential for Left-Right Asymmetry in Mice[END_REF][START_REF] Huang | Defective pulmonary vascular remodeling in Smad8 mutant mice[END_REF][START_REF] Massagué | Smad transcription factors[END_REF], whereas Nodal family ligands on the other hand induce phosphorylation of SMAD2 and SMAD3 (SMAD2/3) [START_REF] Massagué | Smad transcription factors[END_REF][START_REF] Wu | Structural basis of Smad2 recognition by the Smad anchor for receptor activation[END_REF]. Nodal binding to ALK4/ActRII requires co-receptor Cripto (encoded by Tdgf1) [START_REF] Yan | Dual roles of Cripto as a ligand and coreceptor in the nodal signaling pathway[END_REF][START_REF] Yeo | Nodal signals to Smads through Cripto-dependent and Criptoindependent mechanisms[END_REF]. Specifically, Cripto functions as a co-receptor to Nodal when membrane bound by a glycosylphosphatidylinositol-anchor (GPI-anchor). Cleavage of Cripto from the GPI-anchor results in a shortened, biologically active soluble protein [START_REF] Lee | A GPI processing phospholipase A2, PGAP6, modulates Nodal signaling in embryos by shedding CRIPTO[END_REF][START_REF] Yan | Dual roles of Cripto as a ligand and coreceptor in the nodal signaling pathway[END_REF]. Activin is capable of signalling without Cripto, with Activin binding to the ActRII receptor [START_REF] Gray | Cripto forms a complex with activin and type II activin receptors and can block activin signaling[END_REF]. Loss-of function (LOF) mutation of Nodal or Cripto results in defective gastrulation in vivo [START_REF] Ding | Cripto is required for correct orientation of the anterior-posterior axis in the mouse embryo[END_REF][START_REF] Gu | The type I activin receptor ActRIB is required for egg cylinder organization and gastrulation in the mouse[END_REF]. In studies examining Embryonic Stem Cell (ESC) priming for cardiomyocyte differentiation, a dose-dependent increase of soluble Cripto is observable after Activin treatment (Duelen et al., Parisi et al.).

In an in vivo context, Nodal signalling induces expression of WNT antagonists, contributing to the patterning of the primitive streak [START_REF] Perea-Gomez | Nodal antagonists in the anterior visceral endoderm prevent the formation of multiple primitive streaks[END_REF]. Without Wnt3 activity, mesoderm derivatives do not emerge at the primitive streak [START_REF] Liu | Requirement for Wnt3 in vertebrate axis formation[END_REF], and Wnt3-null embryos are phenotypically similar to mesoderm-deficient development embryos [START_REF] Hsieh | Mesd encodes an LRP5/6 chaperone essential for specification of mouse embryonic polarity[END_REF]. Inhibition of WNT signalling downstream regulator β-catenin is also implicated in disruption of cell allocation to the DE in vivo [START_REF] Lickert | Formation of Multiple Hearts in Mice following Deletion of β-catenin in the Embryonic Endoderm[END_REF]. The role of WNT in endoderm allocation is seemingly dose-dependent, with in vitro human ESC modelling demonstrating low WNT doses are conducive for endoderm differentiation, whilst high doses induce mesodermal fate [START_REF] Jiang | Generation of pancreatic progenitors from human pluripotent stem cells by small molecules[END_REF].

Nodal signalling in vivo is essential for the generation of DE (Grapin-Botton & Constam, 2007;[START_REF] Schier | Nodal signaling in vertebrate development[END_REF][START_REF] Stainier | A glimpse into the molecular entrails of endoderm formation[END_REF][START_REF] Zorn | Molecular basis of vertebrate endoderm development[END_REF]. However, DE differentiation of stem cells in vitro has utilised another TGF family member, Activin A, as a substitute for Nodal in driving endoderm differentiation. This is despite embryological studies revealing that mRNA encoding all three subunits of Activin are present in the ICM of the E3.5 mouse, and then restricted to the trophectoderm and E11.5 liver, yet not in the primitive streak of the gastrulating mouse embryo [START_REF] Albano | Activins are expressed in preimplantation mouse embryos and in ES and EC cells and are regulated on their differentiation[END_REF]. Knockout of both Activin beta-chains leads to complete loss of Activin function, craniofacial defects and early postnatal lethality [START_REF] Kuehn | Nodal signaling uses activin and transforming growth factor-β receptor-regulated Smads[END_REF][START_REF] Lau | Activin βC and βE genes are not essential for mouse liver growth, differentiation, and regeneration[END_REF], demonstrating that Activins are expressed during development but may play no role in gastrulation and endoderm formation. Furthermore, Activins seem incapable of regulating Nodal antagonist Lefty (C. [START_REF] Chen | Two modes by which Lefty proteins inhibit nodal signaling[END_REF]). Lefty1 -/-mice fail to regulate primitive streak marker Brachyury, resulting in the formation of multiple primitive streaks and failure of germ layer formation [START_REF] Perea-Gomez | Nodal antagonists in the anterior visceral endoderm prevent the formation of multiple primitive streaks[END_REF].

Whilst the extensive role of Nodal signalling has been examined in vivo, the question of whether Activin A and Nodal act similarly to promote endoderm differentiation remains open. A previous study has shown that, in directing the differentiation of ESCs to pancreatic islet cells, induction by Activin A or Nodal produced functionally disparate endoderm precursor cells (A. E. [START_REF] Chen | Functional evaluation of ES cell-derived endodermal populations reveals differences between Nodal and Activin A-guided differentiation[END_REF]. While cells derived from stem cells cultured with either factor expressed the typical DE markers, FOXA2 and SOX17, and were indistinguishable on the basis of transcriptome, Activin A-derived cells, in contrast to the Nodal-derived cells, could not contribute to endoderm cells in primitive gut tube of the chimeric embryo, nor could they differentiate into pancreatic cells in vitro efficiently.

Another example of the disparate action of Nodal and Activin A was found during the induction of endoderm differentiation of isogenic epiblast stem cells (EpiSCs) derived from the epiblast of postimplantation mouse embryo [START_REF] Kojima | The transcriptional and functional properties of mouse epiblast stem cells resemble the anterior primitive streak[END_REF]. When these EpiSCs are differentiated in serumsupplemented medium, they display different capacity to generate DE-like cells and progenitors of hepatocytes, cholangiocytes and pancreatic islet cells. EpiSCs with enhanced endoderm propensitycharacterised by an early onset of Mixl1 expression -respond to both Activin A and Nodal induction to generate endoderm derivatives efficiently, while those with poor propensity fail to respond to either factor and those with suboptimal propensity for endoderm differentiation respond to Activin A more robustly than Nodal [START_REF] Kaufman-Francis | Differential response of epiblast stem cells to Nodal and Activin signalling: a paradigm of early endoderm development in the embryo[END_REF].

In the present study, the molecular activity underpinning the differential response to Activin A and Nodal was examined during the first 4 days of induced in vitro differentiation of EpiSCs with suboptimal endoderm propensity. The findings demonstrate that Activin treatment promotes an endodermal outcome whilst Nodal is incapable of driving endoderm. Assessment of the underlying TGFβ pathway reveal that Nodal is unable to activate downstream TGF/SMAD transduction and WNT signalling in the EpiSCs in a manner required for endoderm differentiation in vivo. This cell line-specific response highlights the importance of optimising the use of Activin A in place of Nodal when directing endoderm differentiation of pluripotent stem cells.

RESULTS

Activin A and Nodal elicit specific SMADs transduction

In the gastrulating embryo, Nodal in the anterior primitive streak (APS) signals through the TGFβ pathway. To study the TGFβ response in vitro, an APS-counterpart EpiSC line (FS09), was established from the embryo of the mouse harbouring the Foxa2-Venus and Sox17-mCherry reporter [START_REF] Burtscher | Foxa2-venus fusion reporter mouse line allows live-cell analysis of endoderm-derived organ formation[END_REF]. To elucidate whether differences in response are related to activation of the TGFβ signalling pathway, FS09 cells were treated with three factors of the TGFβ superfamily; Activin A, Nodal and BMP4. EpiSC cells require low levels of Activin A (20ng/mL) supplementation to maintain pluripotency, with TGFβ pathway inhibition (by small molecule SB431542) resulting in loss of pluripotency markers [START_REF] Chenoweth | Isolation and maintenance of mouse epiblast stem cells[END_REF][START_REF] Vallier | Early cell fate decisions of human embryonic stem cells and mouse epiblast stem cells are controlled by the same signalling pathways[END_REF]. Therefore, to observe the impact of TGFβ treatment in driving cells away from the maintained pluripotent state, factors were added at three concentrations above the maintenance condition (50, 100 and 200 ng/mL). Treated cells were collected after 48 hours of in vitro 2D culture, alongside untreated cells in control conditions (culture medium without factors), to evaluate the status of the signalling pathway after long term treatment.

To examine TGFβ pathway response to Activin A, Nodal and BMP4, we probed for the phosphorylated forms of the downstream activators of both subfamilies. pSMAD2/3 levels increased with the concentration of Activin A, whereas Nodal induction peaked at 50-100 ng/mL and decreased with higher concentration, to a level comparable to that of BMP4 treated and untreated cells (Figure 1A and B, Figure S1A andB). Untreated and BMP4 treated EpiSCs maintained a high level of pSMAD1/5/8 while both Nodal and Activin A lowered the pSMAD1/5/8 level, with Activin A displaying the strongest effect (Figure 1A andB). [START_REF] Faial | Brachyury and SMAD signalling collaboratively orchestrate distinct mesoderm and endoderm gene regulatory networks in differentiating human embryonic stem cells[END_REF] demonstrated that SMAD2/3 induced expression of Eomes resulted in decreased Brachyury expression, and SMAD1/5/8 induction produced the inverse result. Accordingly, in qPCR analysis of samples shown in Fig. 1A , Eomes expression (relative to control EpiSCs collected 48hrs post seeding) was significantly upregulated by 200ng/mL Activin-A treatment when compared to the corresponding BMP4 treatment (Figure 1C). Conversely, Brachyury expression was significantly increased with BMP4 treatment at 200ng/mL when compared to Activin treatment (Figure 1D). Nodal treatment failed to follow the trend; comparing Activin A and BMP4 treatments, Eomes response was significantly lower at 100ng/mL and 200ng/mL than corresponding Activin A treatments. However, Nodal treatment did not inversely drive Brachyury expression, with Nodal treatment resulting in lower expression than BMP4 treatment at 100ng/mL and 200ng/mL (Figure 1C andD). qPCR analysis demonstrated that supplementation of media with Nodal or Activin A promoted increased expression of endodermal marker Sox17 above the control baseline (of cells cultured in factor-free EpiSC medium), while BMP4 treated cells did not show Sox17 upregulation (Figure 1E). The strongest induction was achieved with Activin A at 200ng/mL (Figure 1E).

As both SMAD2/3 and SMAD1/5/8 are regulated by shared TGFβ pathway components (e.g., SMAD4), pathway activation is suggested to be competitive. Treatment of FS09 EpiSCs with Activin A, Nodal and BMP4 revealed a trend of negative correlation between pSMAD2/3 and pSMAD1/5/8 western blot band strength (r= -0.622, p = 0.0034; Pearson correlation), with BMP4 treatment being the more active in driving phosphorylation of pSMAD1/5/8, and Activin A driving phosphorylation of pSMAD2/3 (Figure 1F). These findings demonstrate Activin A is more potent at activating pSMAD2/3 than Nodal, while both appear involved in dampening the activation of pSMAD1/5/8.

EpiSCs follow different cell trajectories under Nodal and Activin A induction

To evaluate the ability Activin and Nodal to drive cells from a pluripotent state into lineage specification, five EpiSC lines were differentiated towards DE using a directed embryoid body (EBs) protocol (see methods). Samples were collected at Day 2 and Day 4 of differentiation, as well as at Day 0, immediately prior to factor addition (control), and assessed for gene expression using highthroughput qPCR (Biomark -see Methods).

Cell lines CAV2, LMS3, PS03, PS04 (see [START_REF] Kojima | The transcriptional and functional properties of mouse epiblast stem cells resemble the anterior primitive streak[END_REF]) and FS09 (this study) were assessed via Principal Component Analysis (PCA). Two independent analyses were performed with a different set of primers targeting genes of early embryonic development (Supplementary Table 1). This screen revealed EpiSC lines followed divergent lineage trajectories over a 4-day period of in vitro differentiation. Globally, Nodal treatment induced a larger transcriptomic difference than Activin A, accounting for a higher Principal Component (PC) variance, when compared to control cells (Figure 2A and S2A). This result is in accordance with previous findings [START_REF] Kaufman-Francis | Differential response of epiblast stem cells to Nodal and Activin signalling: a paradigm of early endoderm development in the embryo[END_REF] where Activin and Nodal drove differentiation of the above cell lines along different trajectories. After 4 days of differentiation, Activin A induced cells displayed higher expression of pluripotency markers (Pou5f1, Nanog and Fgf5), primitive streak markers (Mixl1 and T) and early epithelial to mesenchymal transition (EMT) markers (Cdh1, Twist1). On the other hand, Nodal treatment upregulated genes associated with advanced EMT (Cdh2, Vim) (Figure 2B, 2C, 2D and S2B). In addition, Nodal was responsible for triggering a higher expression of SMAD genes toward the end of differentiation when compared to Activin. Activin treated cells display a profile that is closer to the control cells at Day 0 and to a primitive streak stage, as suggested by the maintained expression of Mixl1 and T (Figure 2C andD). Despite Nodal inducing a gene expression profile suggestive of more advanced differentiation, endoderm marker Sox17 expression was not consistent across analysis, indicating a sub-optimal endoderm differentiation protocol using Nodal (Figure 2C andD).

Overall, our analysis demonstrated that Nodal induced EpiSC differentiation resulted in a gene expression profile divergent to that of Activin treatment. Analysis suggests Activin treated cells may remain more primitive streak-like when compared to Nodal treated cells that displayed a molecular profile suggestive of more advanced differentiation.

Signalling activities elicited by Activin A and Nodal diverge during endoderm differentiation

To assess the ability of factors to effectively induce DE differentiation, the presence of Foxa2-Venus::Sox17-mCherry positive cells in FS09-derived EBs was assessed. EBs were cultured in factorfree serum replacement medium with supplementation of Activin A or Nodal and assessed by fluorescence microscopy (Figure 3A) and FACS analysis (Figure 3B) over 4 days of differentiation. Activin A treated EBs displayed peak preponderance of Foxa2+ and dual Foxa2+/Sox17+ cells at day 2, with 25% of the cells in EBs being Foxa2+, and about 3% both Foxa2+ and Sox17+ (Figure 3C). A small yet detectable fraction of Sox17+ cells were present with Activin A treatment at days 1-3 (Figure 3B). In contrast to the outcome of 2D culture, Nodal-treated EBs did not produce Foxa2+, Sox17+ or dual Foxa2+/Sox17+ cells. In the control EB condition (culture medium without factors), less than 5% of cells expressed FoxA2-Venus by day 4 of differentiation, suggesting that control condition was sufficient for minimal undirected differentiation towards the endoderm.

To examine the role of TGF signalling in driving unique differentiation outcomes after Activin or Nodal treatment (both at 100ng/mL), the expression of pSMAD2 and pSMAD1/5/8 was assessed. Samples of FS09 cells were collected over four days of EB differentiation for western blot analysis (Figure 4A, Figure S1B). Quantification of the band intensity relative to vinculin control (n=2) showed that Activin A upregulated pSMAD2 expression, reaching an elevated plateau level by Day 2(Figure 4B). Nodal has little effect on pSMAD2 expression, and the level did not differ from Day 0 expression or from the control (Figure 4B). Both Activin A and Nodal downregulated pSMAD1/5/8 activity from Day 1 of differentiation and the activity remained low over days 2-4 (Figure 4B). pSMAD1/5/8 remained expressed in untreated cells during differentiation, indicating endogenous BMP signalling was active in the FS09 EpiSCs. That Nodal and Activin A treatments shut down pSMAD1/5/8 suggested that the primary action of these factors may be to counteract BMP activity. Together, the results indicate Activin A enhanced pSMAD2 function more effectively than Nodal, however both factors are active in repressing BMP-pSMAD1/5/8 signalling activity.

We next examined if response to Nodal signalling during differentiation was modulated by Cripto, a co-receptor required for Nodal binding to ALK4/ActRII receptors [START_REF] Yan | Dual roles of Cripto as a ligand and coreceptor in the nodal signaling pathway[END_REF][START_REF] Yeo | Nodal signals to Smads through Cripto-dependent and Criptoindependent mechanisms[END_REF]. We analysed Cripto expression during differentiation with Activin A or Nodal via western blot, with an antibody specific to both the glycosylated (membrane bound, 36kDa) and unmodified (soluble, 18kDa) protein (Figure 4C, Figure S1C). Activin-A treated EpiSCs expressed membrane-anchored Cripto on Day 1-3 of differentiation, with levels progressively decreasing to an undetectable level. However, Activin A treatment increased levels of soluble Cripto across the timeline. Membrane bound Cripto was present at a low but steady level in Nodal treated EpiSCs across this timeline, maintaining presence across all days unlike the control or Activin treated conditions. Nodal treatment did not result in detection of soluble Cripto. As Cripto is upregulated in a positive feedback loop that is initiated by Nodal and transduced by SMAD2 phosphorylation, the weak activation of SMAD2 in the Nodal-treated cells may account for the lack of Cripto upregulation in the time course of differentiation.

Nodal and Activin directed differentiation uniquely induce WNT signalling

Analysis of TGF pathway activity and transcriptome of differentiating EpiSCs revealed that Nodal did not induce endoderm differentiation in vitro as would be expected during differentiation in vivo. As WNT signalling activity is a requisite element of the molecular cascade driving endoderm differentiation, we next examined the activity of the WNT/-catenin pathway by tracking the phosphorylated/active -catenin in the differentiating EpiSCs. Treatment with Activin A was accompanied by the upregulation of active -catenin at day 2-3. Nodal treatment, in contrast, enhanced expression of active -catenin at later time points, day 3-4 (Figure 5A, Figure S1D). The untreated cells displayed enhanced -catenin separately on day 1 and 3. Expression of genes associated with the canonical WNT signalling pathway were assessed. Expression of the signalling agonists, Wnt3 and Wnt3a was elevated following Activin A and Nodal treatment. However, while the expression of both Wnt3 and Wnt3a was maintained in the Nodal treated culture, Wnt3a expression declined in the Activin treated culture after the Day 1 peak (Figure 5B). Furthermore, the expression of signalling antagonists, Cerberus-1 (Cer1), Goosecoid (Gsc) and Dickkopf 1 (Dkk1), were enhanced by Activin A, but not by Nodal (Figure 5B). These results may suggest that Activin A has elicited antagonistic activity to modulate the level of WNT signalling activity, whereas Nodal did not elicit the antagonistic activity to effectively regulate the pathway activity across the 4-day timeline of endoderm differentiation.

Discussion

We set out to decrypt the differences in the response to signalling activity elicited by two TGF growth factors, Nodal and Activin A. Previous work that has comparatively explored these two ligands revealed differences in response, yet there are gaps in knowledge that this study has further explored. A. E. [START_REF] Chen | Functional evaluation of ES cell-derived endodermal populations reveals differences between Nodal and Activin A-guided differentiation[END_REF], in aiming to explore the functional capacity of late endoderm derived from Activin or Nodal treatment, did not focus on the DE derived after 7 days in vitro. Rather, to achieve their DE population, differing concentrations of Activin A and Nodal (100ng/mL and 1000ng/mL respectively) were used, and the mixed population was sorted for DE cells prior to functional evaluation. To assess the ability of Activin A and Nodal to induce endoderm differentiation, we examined the transition from pluripotent culture, comparing Activin A and Nodal at matched concentrations. Analysis of 5 EpiSC lines revealed directed differentiation outcomes were divergent between Activin and Nodal treated cells. Activin A treatment results in a transcriptomic profile suggestive of a primitive streak outcome, with maintained Mixl1 and Brachyury expression. On the other hand, genes associated with advanced EMT were upregulated after four days of Nodal treatment. Together, these results suggest improved ability for Nodal to prime for or induce differentiation.

We utilised the FS09 EpiSC line that harbours the reporter for Foxa2 and Sox17 expression, an entry point to explore the mechanisms underpinning the different outcomes of TGF signalling after demonstrating that they behave similarly to previously published cells [START_REF] Kojima | The transcriptional and functional properties of mouse epiblast stem cells resemble the anterior primitive streak[END_REF]. Nodal does not induce the differentiation of Foxa2+ and Sox17+ cells, while Activin A drives the differentiation of 25% of cells to Foxa2-expressing cells. We demonstrated that Activin A triggered phosphorylation of SMAD2 but low level of phosphorylation of SMAD1, while Nodal seems to achieve an intermediate level of phosphorylation for both. Nodal-mediated phosphorylation of SMAD1/2 is intermediate between Activin A and BMP4. Nodal treatment responded in steady but weak expression of membrane-anchored, co-receptor Cripto across the differentiation timeline. An inability for Nodal to upregulate Cripto expression in an in vitro context may partially account for failure to induce endoderm differentiation. Activin A induced differentiation was not impeded by decreasing levels of Cripto across the timeline. Activin treatment alone did induce expression of soluble Cripto, a factor reported to be crucial to avoid neural fate in Activin induced differentiation in ESCs [START_REF] Parisi | Nodal-Dependent Cripto Signaling Promotes Cardiomyogenesis and Redirects the Neural Fate of Embryonic Stem Cells[END_REF].

Canonical WNT signalling proteins (e.g., Wnt1, Wnt3, Wnt3a, Wnt8) lead to cellular response through stabilisation of transduction protein β-catenin, via inhibition of the β-catenin-destruction protein complex [START_REF] Koyanagi | Non-canonical Wnt signaling enhances differentiation of human circulating progenitor cells to cardiomyogenic cells[END_REF][START_REF] Zeng | The mouse Fused locus encodes Axin, an inhibitor of the Wnt signaling pathway that regulates embryonic axis formation[END_REF]. The active form of β-catenin translocates to the nucleus and has direct gene regulatory effects [START_REF] Molenaar | XTcf-3 transcription factor mediates β-catenininduced axis formation in Xenopus embryos[END_REF]. In the context of WNT signalling, active -Catenin expression peaked at Day 2 of differentiation following Activin treatment while Nodal produced a slowly increasing but sustained activation. Treatment with both factors was accompanied by enhanced expression of Wnt3 and Wnt3a, but pathway inhibitors Cer1, Dkk1 and Gsc were activated only with Activin A treatment. WNT signalling antagonists can either directly inhibit WNT proteins from interacting with their receptors (e.g. Cer1), interact and maintain the β-catenindestruction protein complex (e.g. Dkk1), or act as a transcriptional repressor of Wnt genes (e.g. Gsc) [START_REF] Katoh | CER1 is a common target of WNT and NODAL signaling pathways in human embryonic stem cells[END_REF][START_REF] Niida | DKK1, a negative regulator of Wnt signaling, is a target of the β-catenin/TCF pathway[END_REF][START_REF] Osteil | Generation of genome-edited mouse epiblast stem cells via a detour through ES cell-chimeras[END_REF]. An endodermal, rather than mesodermal, outcome in vivo is closely tied to WNT pathway regulation [START_REF] Perea-Gomez | Nodal antagonists in the anterior visceral endoderm prevent the formation of multiple primitive streaks[END_REF], and in vitro methodologies fail to induce endoderm rather than mesoderm when increased WNT signalling is present [START_REF] Jiang | Generation of pancreatic progenitors from human pluripotent stem cells by small molecules[END_REF]. Whilst our results indicate that Nodal treatment may mimic more closely the in vivo condition in progressively raising WNT activity, a failure to induce WNT pathway modulators within the 4-day differentiation timeline may factor into the inability to induce DE markers FoxA2 and Sox17 in the FS09 cell line. This information in combination with results showing weak activation of the SMAD2/3 pathway, and equally weak inactivation of the competing SMAD1/5/8 pathway, suggest Nodal in an in vitro context may be unable to provide signalling cues sufficient for generating DE in differentiation methodologies commonly utilised by researchers. On the other hand, Activin A seemingly delivers unphysiologically strong signalling cues that direct the cells towards the desired fate. Data from A. E. [START_REF] Chen | Functional evaluation of ES cell-derived endodermal populations reveals differences between Nodal and Activin A-guided differentiation[END_REF] and Kaufman et. al (2014), demonstrate that DE derived from Nodal treatment, rather than Activin treatment, produces more functionally competent DE derivatives. In this light, it is important to consider that the strong signalling cues seen in our results after Activin treatment may not be conducive to further endodermal applications.

Our study highlighted the importance of understanding the downstream activities of Activin A and Nodal signalling in directing endoderm differentiation of primed-state epiblast stem cells. While Nodal is not regularly used for endoderm differentiation of pluripotent stem cells, it may be the more effective factor to direct endoderm differentiation in cells of different endoderm propensity for modelling lineage differentiation and therapeutic application.

Methods

Cell Culture

LSM3, PS04, PS03 and CAV2 were previously established by our lab [START_REF] Kojima | The transcriptional and functional properties of mouse epiblast stem cells resemble the anterior primitive streak[END_REF] . FS09 was established following previously described protocol [START_REF] Osteil | Generation of genome-edited mouse epiblast stem cells via a detour through ES cell-chimeras[END_REF] from Foxa2-Venus::Sox17-mCherry ESC lines [START_REF] Burtscher | Foxa2-venus fusion reporter mouse line allows live-cell analysis of endoderm-derived organ formation[END_REF]. All cell lines were generated from 129 mice.

Epiblast stem cells (EpiSCs) were maintained in medium comprising of Knockout-DMEM (Gibco, Cat. No. 10829-018) and 20% knock-out serum (Gibco, Cat. No. 10828-028), supplemented with 1% nonessential amino-acid supplement, 1% Glutamax (Gibco, Cat. No. 35050061) and 0.1uM 2-Mercaptoethanol (Sigma-Aldrich, Cat. No. M6250), 10ng/mL recombinant Human basic fibroblast growth factor (bFGF, R&D Systems, Cat. No. 233-FB 025/CF) and 20ng/mL recombinant human/mouse/rat Activin-A protein (carrier free) (R&D Systems, Cat. No. 338-AC-050/CF). Cells were passaged after reaching 70% confluency onto plates coated with 0.1% gelatin and previously seeded with irradiated mouse embryonic fibroblasts (MEFs) at a density of 16,000 cells/cm 2 .

To passage EpiSCs, 2mg/mL Collagenase IV (R&D Systems, Cat. No. 338-AC-050/CF) was added to the culture and incubated for 10 minutes at 37°C. Dissociated colonies were collected following a brief centrifugation and dissociated into single cell suspension using TrypLE Select (Gibco, Cat. No. 12563-011) 2min at room temperature (RT). EpiSCs were seeded at a density of 17,000 cells/cm 2 in the aforementioned medium supplemented with 10uM Y-27632 ROCK inhibitor (TOCRIS, Cat. No. 1254) and maintained at 37 o C with 5% CO2. ROCK inhibitor was removed after 24h.

In vitro differentiation

2D differentiation: EpiSCs were seeded onto a MEF coated 6 well plate. After 24 hours, media was supplemented separately with Activin A, Nodal (carrier free) (R&D Systems, Cat. No. 1315-ND-025) and Recombinant Human BMP-4(Gibco, Cat. No. PHC9534) at three concentrations of 50ng/mL, 100ng/mL and 200ng/mL. Cells were incubated for 48 hours with growth factor replenished with a media change after 24 hours. The cultures were treated with collagenase IV to sample the differentiating EpiSCs from the underlying MEF for analysis.

3D differentiation: EpiSCs were passaged at 60% confluency and resuspended in EB medium: RPMI 1640 media (ThermoFisher Scientific, Cat. No. 21870-076) and Knock-Out Serum (20%) supplemented with 1% non-essential amino-acid supplement, 1% Glutamax and 0.1uM 2-Mercaptoethanol. Single cells were seeded at 2 x10 6 cells per well with 10uM Y-27632 ROCK inhibitor (TOCRIS, Cat. No. 1254) in a 24 well AggreWell plate (StemCell Technologies,Cat. No. 34415) and centrifuged at 1000RPM for 3 minutes. After 24 hours, aggregates were lifted from the Aggrewell plate by gentle pipetting. Unaggregated cells and debris were removed by passing the well contents through a 40m cell strainer (StemCell Technologies, Cat. No. 27305). Aggregates retained in the filter were then transferred into a 60mm culture dishes containing 4mL of EB media supplemented with either 100ng/mL Activin A or 100ng/mL Recombinant Mouse Nodal Protein (carrier free) (R&D Systems, Cat. No. 1315-ND-025).

Imaging

Samples were imaged on an Olympus IX70 Inverted Microscope with a Lumenera Infinity 3-1 camera and Infinity Analyze software (Release 5). Cells were imaged on each day of their timeline immediately prior to media change and sample collection. FS09cells were exposed for 200ms with Olympus U-RFL-T mercury lamp. Image processing was performed using ImageJ (Fiji; imagej.nih.gov/ij). To artificially colour and stack images captured with fluorescence, the native ImageJ function Image/Color/Merge Channels was used.

Flow Cytometry

Embryoid bodies were dissociated into a single-cell suspension using TrypLE Select (Gibco, Cat. No. 12563-011), centrifuged at 1000RPM for 5 minutes and washed with Dulbecco's Phosphate Buffered Saline (Gibco, Cat. No. 14190-144). Samples were fixed in 4% paraformaldehyde at 37 o C for 30 mins. Samples were analysed using the BD LSRII flow cytometry. A 455nm laser with a 530/30 bandpass filter was used to detect Foxa2:Venus Green and a 561nm laser with a 610/20 bandpass filter was used to detect Sox17:mCherry fluorescence in the FS09 reporter cell line. The collected data was analysed using FlowJo Software (version 10.5.3, Oregon, USA).

Western Blot

Sample preparation

Cells were resuspended in 50mL of RIPA buffer consisting of 1% Ipegal CA-630, 0.5% sodium deoxycholate, 0.1% SDS, 1mM DTT, 1x Complete Protease Inhibitor Cocktail (Sigma Aldrich, Cat. No. 11836145001), 1x PhosSTOP EASYPack (Roche, Cat. No. 04906845001) Phosphatase Inhibitor, 1.5% Triton X-100 in Dulbecco's Phosphate Buffered Saline and incubated at 4 o C for 45 minutes. Samples were centrifuged for 10 mins at 13000RPM at 4 o C and the supernatant containing soluble proteins was collected. Samples were made to a concentration of 0.5mg/mL and 5x SDS loading dye (5% mercaptoethanol, 0.02% Bromophenol Blue, 30% Glycerol, 10% Sodium dodecyl sulfate, 250mM pH 6.8 TrisCl) was added to the samples. Samples were heat inactivated at 90 o C for 10 mins.

Protein separation and transfer

A Novex Nu-PAGE Pre-cast 4-12% gradient Bis-Tris 12-well gel (Life Technologies, Cat. No. NP0322BOX) was loaded in an XCell SureLock Novex Mini-gel electrophoresis chamber (Life Technologies, Cat. No. EI0001). 1x Running buffer was made from 20x stock (50 mM MES, 50 mM Tris-Base, 0.1% SDS, 1 mM EDTA) with MilliQ-Ultrapure Water and added to the electrophoresis chamber. 15uL of protein sample was run against 9mL Novex Sharp Pre-stained Protein Standard (Invitrogen, Cat. No. LC5800) for 60 mins at 200V. Gels were transferred to Whatman Optitran BA-S 83 0.2 µm Nitrocellulose membranes (Sigma-Aldrich, Cat. No. 10439380) Transfer was run in a Mini Trans-Blot Electrophoretic Cell (BioRad Cat. No.1703930) in 1x Towbin Transfer buffer made from a 5x stock (50mM Tris-base, 200mM glycine) with 10% methanol for 60 mins at 90V at 4°C. Membranes were washed in 1x PBST made from 10x PBS stock (PBS tablets, MP Biomedicals, Cat. No. 2810305) and 0.001% Tween20 .

Membranes were blocked in blocking agent as applicable for antibody for 60 mins before the primary antibodies were applied (1:1000 dilution) overnight whilst rocking at 4°C; Phospho-Smad2 monoclonal antibody (Ser465/467) (Cell Signaling Technology Cat. No. 3108), Phospho-Smad1 (Ser463/465)/ Smad5 (Ser463/465)/ Smad9 (Ser465/467) monoclonal antibody (Cell Signaling Technoloy Cat. No. 13820), Cripto polyclonal antibody (Merck Cat. No. 05-665), Anti-Active β-Catenin (anti-ABC) monoclonal antibody , Vinculin monoclonal antibody (Sigma-Aldrich (Cat. No. MFCD00162905). The following day, membranes were washed with PBST and secondary antibodies were applied for 60 mins at RT. Membranes were revealed using SuperSignal West Pico PLUS Chemiluminescent Substrate (ThermoFisher, Cat. No. 34580) as per manufacturer's instructions. Membranes were imaged with Fujifilm LAS4000 Luminescence Imager.

Western Blot Analysis

ImageJ (Fiji imagej.nih.gov/ij) was used to analyse western blot band intensity. Rectangular selection tool was used to select an area containing both sample band and control band for western blot image. The function Analyse>Gels>Plot Lanes was used to generate histograms representing relative density of area content. Area under peaks, corresponding to band density, was selected with the wand tool, and the function Analyse>Gels>Label Peaks was used to calculate area under the curve. A normalised percentage was calculated as (selected peak area)/(total size of all selected peaks) to normalise for background variation. Correlation between pSMAD1/5/8 and pSMAD2 band intensity was determined using Pearson correlation and two-tail probability was used to calculate the significance of the correlation coefficient.

Microfluidic quantitative PCR

RNA extraction and preparation

Snap frozen cell pellets had total RNA extracted using ISOLATE II RNA mini kit (Bioline, Cat. No. BIO-52073) following manufacturer's instructions. RNA concentration was determined using Nanodrop ND-1000 Spectrophotometer (ThermoFisher Scientific). Sample RNA was diluted to a concentration of 200ng/uL. Fluidigm cDNA Preparation Master Mix (Fluidigm, Cat. No. 100-642-B1) was used for reverse transcription as per manufacturer's instructions then was reverse transcribed to cDNA using reagents from the RT2 Microfluidics qPCR Reagent System kit (Qiagen, 330431), as per the manufacturer's protocol. cDNA preamplification cDNA was pre-amplified using the RT2 PreAMP Pathway Primer Mix. For amplification of Cell Lineage marker genes in Figure S2, the following commercial sets were used: (Qiagen, PAMM-508Z and PAMM-081Y), TGFb (Qiagen, PAMM-235Z) and EMT target genes (Qiagen, PAMM-090Z) (Gene lists in Supplementary Table S2). Custom made oligos were used for analysis of the FS09 and PS04 line (Supplementary Table 1) seen in Figure 2B. Microfluidic qPCR was performed on the sets of preamplified cDNA using above primer sets. Primers were pooled, with a final concentration of 100uM. Primers were applied to prepared samples and Fluidigm PreAmp MasterMix (Fluidigm, Cat. No. 1005580) was added to samples. The following pre-amplification steps were performed: Hold; 95°C/2min, 10 cycles of 95°C/15 sec, 60°C/4 mins. Remaining primers were removed with Exonuclease 1 (20U/mL), in an Exo1 reaction buffer (ThermoFisher Scientific, Cat. No. EN0851).

Microfluidic qPCR

The prepared samples were analysed on the Biomark HD system (Fluidigm) for microfluidic quantitative qPCR. Pre-amplified sample were prepared as follows: 2.5 µL of 2× SsoFast EvaGreen Supermix with low ROX (Biorad,, 0.25 µL 25× DNA Binding Dye (Fluidigm, Cat. No. 100-7609) and 2.25 µL of diluted sample were prepared. Primers were prepared in the following mix: 2.5 µL 2× Assay Loading Reagent (Fluidigm, 100-7611), 1× DNA Suspension Buffer (TEKnova, Cat. No. T0221), 0.25 µL of combined forward and reverse primers. Samples and primers were combined and loaded into a 96.96 Dynamic Array IFC plate (Fluidigm,) and run on the Biomark System. See supplemental data for primer sequences.

Data analysis

Each set of primers resulted in a different dataset that was first analysed independently. Raw data were extracted using the Fluidigm Real-Time PCRAnalysis Software and subsequent analysis was performed in R software. Ct values flagged as undetermined or greater than the threshold (Ct>24) were set to NA (missing values). Samples with a measurement for only one housekeeping gene or samples with measurements for <30 genes were excluded from further analysis. Genes where over 30 samples had a measurement of NA were also excluded from further analysis. Delta Ct (dCt) was calculated by subtracting the average expression of five housekeeping genes from the sample gene expression. Then, once dCt was generated, all the data were pooled together, and normalised to eliminate the batch effect across the primer sets and the various IFC plates. Samples were carried out across the experimental procedures to ensure efficient normalisation. Then, PCA and DEGs analysis were performed using R. The different sets of primers did not allow for merging the data as the commercial sequences are unknown therefore primer efficiency is not similar. Statistical analysis were performed using rstatix. Raw files and codes for the analysis are available on https://github.com/PierreOsteil/NodalVSActivinEpiSC

Figure legends Figure 1 .

 legends1 Figure legends Figure 1. SMAD pathway response to Activin A and Nodal treatment. (A) FS09 EpiSCs treated with Activin A, Nodal or BMP4 for 48 hours at concentrations of 50ng/mL, 100ng/mL and 200ng/mL. Untreated EpiSCs (no factor supplementation) as the control. Western blot analysis used antibodies for pSMAD2 and pSMAD1/5/8. (B) Quantification of western blot results (average band intensity) normalized against vinculin control. Cell samples assessed for expression of (C) Eomes (D) Brachyury and (E) Sox17 by qPCR analysis. Fold change in log scale. Significance level determined by student's ttest. (F) Relative band intensity between pSMAD2 and pSMAD1/5/8 (blot results in Figure 1B). r: Pearson correlation coefficient. Rep: replicate. AU: arbitrary unit. Significance level determined by one-way ANOVA. Significance level: *p<0.5. **p<0.01. ***p<0.001.

Figure 2 .

 2 Figure 2. Differentiation trajectories of EpiSCs treated by Activin A and Nodal. (A) PCA analysis with variable correlation plot of microfluidic qPCR on 2 EpiSC lines (PS04 and FS09) collected at Day 0, 2 and 4 of directed EB differentiation cultured in Activin or Nodal (100ng/mL) supplemented media, using custom-made oligos set. (B) Genes contribution to principal component axis 1 and 2. The longer the vector the more a particular gene is contributing to the axes. (C,D) Heatmap and hierarchical clustering of differentially expressed genes at Day 4 of differentiation in EpiSC lines treated with Activin or Nodal.

Figure 3 .

 3 Figure 3. Detection of definitive endoderm precursors in FS09 epiblast stem cells differentiated in Activin A and Nodal-supplemented media. (A) Fluorescence visualization of FoxA2:Venus/Sox17:mCherry expressing cells in EBs in Activin, Nodal or no-factor control in a 4-day timeline. Fluorescence image is overlaid on brightfield (BF) image. (B) Temporal profile of changes in the population of Foxa2:Venus-positive, Sox17-mCherry-positive and dual positive cells in the embryoid bodies quantified by flow cytometry. (C) Representative BF images and FoxA2:Venus/Sox17:mCherry fluorescent images of the three populations of reporter-expressing cells by flow cytometry at D2 of differentiation. Quantification of population fluorescence represented as contour map with 610nm excitation capturing mCherry and 530nm FoxA2. EB: Embryoid bodies. BF: Brightfield.

Figure 4 .

 4 Figure 4. TGF/SMAD signalling activity in FS09 epiblast stem cells differentiated in Activin A and Nodal-supplemented media. Profiles of pSMAD expression over the 4 days of differentiation visualised by (A) western blot analysis and quantified by band intensity measurement in (B). pSMAD2 and pSMAD1/5/8 normalized against vinculin control. Asterisk colour denotes level of significance. (C) Temporal profile of expression of membrane anchored and soluble Cripto over 4 days of differentiation. Vinculin: reference control.

Figure 5 .

 5 Figure 5. WNT signalling activity in FS09 epiblast stem cells differentiated in Activin A and Nodalsupplemented media. (A) Profile of active -catenin expression in the 4 days of differentiation, visualised by western blot analysis. Vinculin, reference control. (B) Temporal profile of the expression of WNT signalling factors and WNT-responsive genes over 4 days of differentiation. Data normalised on Day 0 level of expression.

  Figure 4

Acknowledgements

Flow cytometry experiments were performed at the Westmead Scientific Platforms, which are supported by the Westmead Research Hub, the Cancer Institute New South Wales, the National Health and Medical Research Council of Australia (NHMRC) and the Ian Potter Foundation. This work was supported by National Health and Medical Research Council of Australia (Grant 632776), the Australian Research Council (DP 160103651) and Mr James Fairfax. PO was supported by Sir Norman Gregg Research Fellowship and PPLT by NHMRC Research Fellowship (1110751).

Conflict of Interest Statement

The authors declare no conflicts of interest Supplementary Figures Supplementary Table S1. Primer list and sequences for in-house Biomark plate (used to generate data in Figure 2). S2. Gene lists for amplification of Cell Lineage marker genes in Figure S2.

Supplementary Table

Column header for the following commercial sets: Lineage markers (Qiagen, PAMM-508Z and PAMM-081Y), TGFb (Qiagen, PAMM-235Z) and EMT target genes (Qiagen, PAMM-090Z).

Supplementary Figure S1. Western blot images for; (A) EpiSCs treated with 50, 100 and 200 ng/mL. Antibody for (i) pSMAD2 or (ii) pSMAD1/5/8 is detected at ~60kDa. MagicMark XP Standard ladder was run with samples. (B) EpiSCs were treated for 4 days alongside a sample at Day 0 (D0). Antibody for (i) pSMAD2 or (ii) pSMAD1/5/8 is detected at ~60kDa. Novex Sharp pre-stained ladder was run against samples and marked with pencil on membrane. (C) Antibody for Cripto detects membrane anchored Cripto at 36kDa and soluble Cripto at 18kDa. Novex Sharp pre-stained ladder was run against samples and marked with pencil on membrane. (D) Antibody detects Active B-Catenin at ~110Da. Novex Sharp pre-stained ladder was run against samples and marked with pencil on membrane. Abbreviations; Activin A (A), Nodal (N), BMP4 (B), Control (C), Untreated (U).

Supplementary Figure S2. (A)

PCA analysis with variable correlation plot of microfluidic qPCR on 4 EpiSC lines (PS04, PS03, CAV2 and LSM3) collected at Day 0, 2 and 4 of directed differentiation cultured in Activin or Nodal supplemented media, using the commercial set of oligos. (B) Genes contribution to principal component axis 1 and 2. The longer the vector the more a particular gene is contributing to the axes. (C, D) Heatmap and hierarchical clustering all genes for all days of differentiation in EpiSC lines treated with Activin or Nodal or untreated (Control).

Treatment

Activin Nodal

Cell Line

Treatment


S Dérozier

P Nicolas

U Mäder

C Guérin

Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations

  painter of landscape Along the River During the Qingming Festival (清明上河圖) by Zhang Zeduan (張擇端, fl. early 12th century) Genoscapist: a painter of genomic landscape Source : https://genoscapist.migale.inrae.fr/ Source : https://en.wikipedia.org/wiki/Landscape_painting of microbiology collaborators non-aggregated transcriptomic data on large datasets (allowing the discovery of non-canonical biological mechanisms) navigation based on expression correlation between genes Existing tools (JBrowse, IGV, Panoptes, ...) do not allow to have all these features in a single browser 1 Background -Needs & existing situation Source: Wang P. et al. (2021) DOI: 10.1038/s41438-021-00529-8 Locally developed tools -MuGeN 1 (community-proven usability) -FLAGdb ++,2 (genomic data storage and graphic representation) MuGeN-based static web versions used by the B. subtilis 3 and S. aureus community 4 Objectives web-based interface gathering previous experiences deployment on already published expression datasets for both organisms customisable views with sample selection and colouring, sharable views among collaborators high resolution representation and exportation 1 Background -Ambition 3 Nicolas, P. et al. (2012). DOI: 10.1126/sciences.1206848 4 Mäder, U. et al. (2016). DOI: 10.1371/journal.pgen.1005962 1 Hoebeke M. et al. (2003). DOI: 10.1093/bioinformatics/btg101 2 Dérozier S. et al. (2011). DOI: 10.1186/1746-4811-7lossless format for publication exactly same image for view and download easy image editing high resolution PNG export also available Share by URL https://genoscapist.migale.inrae.fr/seb/viewer/custom SVG glyphs -management of data tracks Improving data management -loading data on the fly from online workflow or user data -aggregator portal for inter-project visualization by reference custom SVG glyphs -management of data tracks Improving data management -loading data on the fly from online workflow or user data -aggregator portal for inter-project visualization by reference genome

Journées Ouvertes en Biologie, Informatique et Mathématiques 7 juillet 2021

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Web interface -View options 3 Web interface -Profile customisation (selection) Acknowledgements Ulrike Mäder Cyprien Guérin Pierre Nicolas Elena Bidnenko Etienne Dervyn Matthieu Jules Anne-Gaëlle Planson CoNoCo project