Mamoudou Sangare

Soumya Banerjee

Paul Muhlethaler
email: paul.muhlethaler@inria.fr

Samia Bouzefrane
email: samia.bouzefrane@lecnam.net

Predicting transmission success with Machine-Learning and Support Vector Machine in VANETs

In this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a succcessful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.

I. INTRODUCTION

Recently, great progress in wireless transmission technology has paved the way to large networks with massive transmission patterns. Such networks can be Wireless Sensor Networks (WSNs) or Vehicular Ad hoc NETworks (VANETs). VANETs are considered to be one of the main tools to help reduce traffic accidents and fatalities. In these networks, vehicles exchange pakets between themselves but also can sent packets to RoadSide Units (RSUs) or receive packets from them. VANETs can produce a huge volume of data which can be exchanged and aggregated. VANETs are thus conducive to the use of Machine-Learning techniques, which aim at various goals. Machine-learning is used for vehicular networks in the following areas:

• Positioning : The information received or sent by the vehicles in Vehicular Ad-hoc NETworks can be used to establish their positions. Such a situation is ideal for the application of the machine-learning approach [START_REF] Ke | Support Vector Regression Based Indoor Location in IEEE 802[END_REF], [START_REF] Nguyen | Improving Poor GPS Area Localization for Intelligent Vehicles[END_REF]. In vehicles, there are many sources of information such as GPS, map analysis, radar, lidar, etc. A huge volume of data can be collected, analyzed and shared by the vehicles and the RoadSide Units (RSUs); • Automatic Incident Detection (AID) can be performed by Machine-learning algorithms. The Support Vector Machine technique has been used in recent studies, such as [START_REF] Chen | Automatic incident detection algorithm based on support vector machine[END_REF], [START_REF] Chen | A SVMbased Approach for VANET-based Automatic Incident Detection[END_REF]. Older studies such as [START_REF] Khan | Statistical and neural classifiers to detect traffic operational problems on urban arterials[END_REF], [START_REF] Lee | Fuzzy-logicbased incident detection for signalized diamond interchanges[END_REF] use Neural Networks and Fuzzy logic. A Wavelet scheme is used for AID in [START_REF] Adeli | Fuzzy Wavelet RBFNN model for freeway incindent detection[END_REF]. examples of such a use are given in [START_REF] Lai | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks[END_REF], [START_REF] Zhao | A svm based routing scheme in vanets[END_REF], [START_REF] Slavik | Applying machine learning to the design of multi-hop broadcast protocols for vanet[END_REF]; • Security in VANETs : Machine-learning has been proposed to improve security in VANETs. The collection of packets sent in VANETs can be used to find compromised vehicles which do not perform suitable actions such as relaying when it is appropriate or sending forged messages over the network. We find such a use of machine-learning in [START_REF] Grover | Machine Learning Approach for Multiple Misbehavior Detection in VANET[END_REF], [START_REF] Ghaleb | An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications[END_REF], [START_REF] Zhang | Distributed privacy-preserving collaborative intrusion detection systems for vanets[END_REF],

In the first areas the machine-learning schemes offer other services in addition to those that help the functioning of the VANETs, whereas in the last areas the machine-learning techniques are used solely to help the functioning of the VANETs.

In this paper we study how machine-learning can be used to predict the transmission performances within the VANETs. More specifically we aim at computing the probability of the successful reception of a transmission between a vehicle and a Roadside Unit located at a given and known position and with a given transmission rate 1 . This is very important since the performance of the transmission is a key parameter for the safety applications which use transmissions of packets; tuning the transmission of safety messages is crucial to optimize their efficiency. In Europe, ETSI standards define two safety messages for VANETs: Car Awareness Messages (CAMs) and Decentralized Emergency Notification Messages (DENMs). DENMs are sent in broadcast and generally relayed (in multihop mode) to warn vehicles of hazards. Each vehicle sends CAMs to neighboring vehicles; for a given vehicle, these messages contain its position (obtained by GPS) and speed. RoadSide Units can easily compute the statistics of the transmission i.e. for a given location and a given transmission rate, the probability of success. This probability can be computed by averaging the number of successful receptions. These statics can be used by a Machine learning scheme which can produce a model used the RSU and also by the the vehicles themselves around the RSU.

Since it is very difficult to obtain real data in VANETs, for a first step we use an analytical model to compute the reception probability in a CSMA network and to build our database.

The main mechanism for access in a VANET is the carrier Sense Multiple Access scheme (CSMA). The main idea is that before transmitting a packet a node (in our case a vehicle) senses the channel to determine whether there is already a transmission on the channel. We model the location of the vehicles as an homogeneous Poisson Point Process which eases the computation and has been proved a good assumption.

To build the model of the simultaneously transmitting vehicles, we use the Matern selection process [START_REF] Stoyan | Stochastic geometry and its applications[END_REF] which was first used in [START_REF] Muhlethaler | Throughput optimization in multihop csma mobile ad hoc networks[END_REF] to evaluate the pattern of simultaneous transmissions in CSMA. The model of [START_REF] Muhlethaler | Throughput optimization in multihop csma mobile ad hoc networks[END_REF] was improved by [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF], [START_REF] Nadjib Achir | Optimisation of spatial csma using a simple stochastic geometry model for 1d and 2d networks[END_REF], which is the model that is used and extended in this paper.

Our analytical model allows us to build a database which provides for a given position the probability of successful reception. We then use the Support Vector Machine (SVM) scheme to build a model. Finally we use this model to predict the probability of successful transmission given the position of the vehicle. SVM has been selected as machine learning since it is a widespread technique but other techniques would have be possible as discussed in the conclusion. The remainder of this paper is organized as follows. Section II describes the model we propose to study CSMA and develops the corresponding analytical model. Section III is a brief description of the Support Vector Machine technique.

The prediction results obtained with SVM are reported in Section IV. Finally Section V concludes the paper.

II. SYSTEM MODEL FOR THE VANET PERFORMANCE

A. Network nodes

The nodes are randomly deployed according to a Poisson Point Process Φ. We denote by λ the intensity of the process. In this paper we consider a 2D infinite plan, S = R2 or a 1D infinite line, S = R. The 2D model is for Mobile Ad-hoc NETworks (MANETs) or Wireless Sensor Networks (WSNs). The 1D model is more relevant to Vehicular Ad-hoc NETworks (VANETs).

B. Propagation law, fading and capture model

We suppose that the signal received in a transmission is the result of a random fading F and a power-law in the distance decay 1/r β where β is the decay factor and is generally between 3 and 6. In our study, the fading will be Rayleigh i.e exponentially distributed with parameter µ and thus is of mean 1/µ. Thus the signal received when the transmitter and the receiver are at distance r from each other is F/l(r) 2 with l(r) = r β . We use the well-accepted SIR 3 (Signal over Interference Ratio) with a capture threshold T .

m i , X i m k , X k.
random mark of a point in the PPP

m n ,X n m o ,X o m p ,X p m l , X l m q ,X q m j ,X j.

C. Model for CSMA

Using the model developed in [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF], we adopt a Matern selection process to mimic the CSMA selection process. The points X i in Φ receive a random mark m i . We also call F i,j the fading for the transmission between X i and X j . The idea of the Matern selection is to select the points X i with the smallest random marks m i in their neighborhood. To define the neighborhood of a point X i we need to introduce the carrier sense threshold P cs which is the power threshold under which the channel is considered as busy. We define

V(X i ) = {X j ∈ X i F i,j /l(|X i -X j |) > P cs } the neighborhood of X i . X i will be selected in the Matern selection process if and only if ∀X j ∈ V(X i ) m i < m j .
In other words, this means that X i has the smallest mark m i in its neighborhood. The Matern selection is illustrated in Figure II.1. Node i has the smallest mark m i within its neighborhood. Although node q does in fact have a smaller mark, it is not within node i's neighborhood. We should point out that, for the sake of simplicity, here we have not taken into account any Rayleigh fading (F ≡ 1) and thus the neighborhood of node i is a disc. The technique based on marks used by the Matern selection process results in an over-elimination of nodes. When a node is eliminated by a node with a smaller mark, the node which has the smallest back-off in its neighborhood can start transmitting. The nodes which have been eliminated should not further eliminate other nodes. But this over-elimination can occur, as shown in Figure II.1. Node o is eliminated by node i, but node o eliminates node p in the Matern selection process, whereas in a CSMA system, node o is correctly eliminated by node i, but, being eliminated, node o can not eliminate another node. We do not take this case into account in our model. We note the medium access indicator of node

X i e i = 1I(∀X j ∈ V(X i ) m i < m j )
Proposition II.1. The mean number of neighbors of a node is:

N = λ S P {F P cs l(|x|)}dx.
In a 2D network we have :

N = 2πλΓ(2/β) β(P cs µ) 2/β .
In a 1D network we have :

N = 2λΓ(1/β) β(P cs µ) 1/β .
This result is very simple. Let F 0 j be the fading between the node at the origin X i and node X j This is just the application of Slivnyak's theorem and Campbell's formula, see [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF], [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF]

N = E 0 Xj ∈φ 1I(F 0 j l(|X j -X i |) P cs = λ S P {F P cs l(|x|)}dx
A straightforward computation provides the explicit value of N in the 1D and 2D cases.

Proposition II.2. The probability p that a given node X 0 transmits i.e. e 0 = 1 is:

p = E 0 [e 0 ] = 1 -e -N N .
Proof. The proof is obtained by computing the probability that a given node X 0 at the origin with a mark m = t is allowed to transmit. The result is then obtained by deconditioning on t. The details of the proof can be found in [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF].

Thus p measures the probability of transmission in a CSMA network. If p is close to 1 this means that the carrier sense does not restrain transmissions. In contrast, if p is small, this means that the carrier sense imposes a severe restriction on transmissions.

Proposition II.3. The probability that X 0 transmits given that there is another node

X j ∈ Φ at distance r is p r with p r = p -e -Pcsµl(r) 1 -e -N N 2 - e -N N
Proof. The proof is the same as that of Proposition II.2.

Proposition II.4. Let us suppose that X 1 and X 2 are two points in Φ such that |X 1 -X 2 | = r. We suppose that node X 2 is retained by the selection process. The probability that X 1 is also retained is:

h(r) = 2 b(r)-N ( 1-e -N N -1-e -b(r) b(r) )(1 -e -Pcsµl(r) ) 1-e -N N -e -Pcsµl(r) 1-e -N N 2 -e -N N with b(r) = 2N -λ S e -Pcsµ(l(|x|)+l(|r-x|) dx.
In a 2D network, we have:

b(r) = 2N-λ ∞ 0 2π 0 e -Pcsµ(l(τ )+l( √ τ 2 +r 2 -2rτ cos(θ))) dτ dθ.
In a 1D network, we have:

b(r) = 2N -λ ∞ -∞ e -Pcsµ(l(τ )+l(|r-τ |)) dτ
Proof. The proof can be found in [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF] Proposition II.5. Given the transmission of a packet, we denote by p c (r, P cs ) the probability of successfully receiving this packet at distance r in a CSMA system (modeled by a Matern selection process with a carrier sense threshold P cs ) and with a capture threshold T . We have:

p c (r, T, P cs ) exp -λ S h(|x|) 1 + l(|x-r|) T l(r) dx
In a 2D network, we have:

p c (r, T, P cs ) exp -λ ∞ 0 2π 0 τ h(τ ) 1 + l( √ τ 2 +r 2 -2rτ cos(θ)) T l(r)
dτ dθ

In a 1D network, we have:

p c (r, T, P cs ) exp -λ ∞ -∞ h(τ ) 1 + l(|r-τ |) T l(r) dτ
Proof. The idea of the proof is to consider a transmitter at the origin and to compute the probability of successful reception by a receiver at distance r. To do so, we condition by the presence of another transmitting node at distance τ . According to proposition II.4, the density of such nodes is λh(τ ). We approximate the interference by the interference of a Poisson Process of density λh(τ ). The result is obtained by integrating on τ . The details of the proof can be found in [START_REF] Baccelli | Stochastic Geometry and Wireless Networks[END_REF].

It is easy to add a thermal noise W to the model. The expression of p c (r, P cs ) must then be multiplied by L W (µT l(r)) where L W (.) is the Laplace Transform of the noise.

III. THE SUPPORT VECTOR MACHINE TECHNIQUE

The idea is to use the Support Vector Machine technique to compute the reception probability of p i versus x i = (r i , T i ).

In the first step we assume that we know the p i and the related value x i (thus we know (p i ) 1 i N and (r i , T i ) 1 i N ) and we have to predict the reception probability using other values:

(p i ) 1 i N knowing (r i , T i ) 1 i N .
We assume that

p i = w T φ(x i ) + b (III.1)
where w and b are two unknown vectors and φ(x) an unknown function of a vector x.

To solve these equations, we introduce the following convex optimization problem:

minimize 1 2 ||w|| 2 subject to - w T φ(x i ) + b . (III.2)
This problem assumes that the function given in III.1 can approximate the set of points that is given i.e (p i , T i ) 1 i N with an accuracy of . Sometimes this is not possible and some errors must be accepted. In this case, slack variables, which allow us to cope with impossible constraints, are introduced. This relaxation procedure uses a cost function. The convex problem then becomes :

minimize 1 2 ||w|| 2 + C N i=1 (ξ i + ξ * i ) subject to --ξ * i w T φ(x i ) + b + ξ i with ξ * i , ξ i > 0 (III.
3) This problem can be solved by using Lagrange multipliers. The problem becomes:

L : = 1 2 ||w|| 2 + C N i=1 (ξ i + ξ * i ) - N i=1 (ν i ξ i + ν * i ξ * i ) - N i=1 α i ( + ξ i -p i + w T φ(x i ) + b) - N i=1 α * i ( + ξ * i + p i -w T φ(x i ) -b)
where L is the Lagrangian and ν i , ν * i , ξ * i , ξ * i are the Lagrangian multipliers which are thus positive i.e ν i , ν * i , ξ * i , ξ * i > 0

We know that the minimum of L is attained when the partial derivatives are zero, thus:

∂L/∂b = N i=1 (α i -α * i ) = 0 ∂L/∂w = w - N i=1 (α i -α * i )φ(x i ) = 0 ∂L/∂ξ ( * ) i = C -α ( * ) i -ν ( * ) i = 0
The substitution of these equations in the Lagrangian leads to the following problem:

maximize - 1 2 N i,j=1 (α i -α * i )(α j -α * j )φ(x i ) T φ(x j ) - N i=1 (α i + α * i ) + N i=1 y i (α i -α * i ) subject to N i,j=1 (α i -α * i ) = 0 and α i , α * i ∈ [0, C].
Thus it follows that:

w = N i=1 (α i -α * i )φ(x i )
and

p i (x) = N i=1 (α i -α * i )φ(x i ) T φ(x) + b
This formula is called the Support Vector expansion. The complexity of the function representation only depends on the dimensionality of the input space. The next step of the analysis uses the Karush-Kuhn-Tucker (KKT) conditions. These conditions imply that at the solution the product between the constraints and the dual variable must vanish. In other words, we have

α i ( + ξ i -p i + w T φ(x i ) + b) = 0 (III.4) α * i ( + ξ * i + p i -w T φ(x i ) -b) = 0 and (C -α i )ξ i = 0 (C -α * i )ξ * i = 0
We can deduce that only the samples which do not satisfy the constraint of III.3 have α ( * ) i = C. Moreover, since the two values of the right part of III.4 can not be simultaneously 0 then we have α i α * i = 0. Thus after some observations we have :

max(-+ p i -w T φ(x i )|α i < C or α * i > 0) b min(-+ p i -w T φ(x i )|α i < C or α * i > 0)
This part is adapted from [START_REF] Grover | Machine Learning Approach for Multiple Misbehavior Detection in VANET[END_REF] a tutorial by Smola.

IV. NUMERICAL RESULTS

In this part we use the results of Section II to build the database with which we train the SVM model as defined in Section III.

For the radio model we use a Rayleigh fading with µ = 10 and a density of vehicles λ = 0.02 vehicles per meter. We assume that β = 4 and we use the 1D model, which is more suitable for VANETs than the 2D model. The database is built as follows: r varies from 10m to 250m by steps of 10m and we vary T from 1 to 10 by step of 0.2. For each of these couples (r, T ) we use the analytical model to compute the probability p of successful reception.

We also assume that P cs = 4.5e -10 which is approximately the value that optimizes the density of successful transmission for r = 50m and for T = 10.

A. Results with no errors in the database

The database is built as described above. Initially we do not include any errors in the measurement of the probability of successful transmission given by the analytical model. For the Support Vector Machine algorithm we use the libsvm program in regression mode with the option: '-s 3 -c 10 -p 0.00000001 -e 0.000000001' to build the SVM model. Thus the kernel used is an exponential kernel. In Figures IV.1 and IV.2, we present the probability of successful reception versus T for respectively r = 75m and r = 125m. The results compare the prediction of the SVM algorithm with the results of the analytical model. We observe a perfect matching between the two approaches. Thus we can foresee that SVM (with an exponential kernel) is very suitable to predict the probability of successful transmission in a VANET using RSUs and a CSMA-based access scheme.

B. Results with errors in the database

We suppose that the database includes errors in the measurements. We assume that the values in the database are the real values multiplied by (1 + N (0, 0.05)) where N (0, 0.05) is the normal random variable of mean 0 and variance 0.05. Here again the prediction by the SVM model is very good. Even with noise measurements, SVM remains very good at predicting the transmission probability in VANETs using an access protocol based on CSMA. In Figures IV.3 and IV.4, we present the probability of successful reception versus T for respectively r = 75m and r = 125m with these assumptions and we can compare the prediction of the SVM model with the direct analytical model.

C. Optimal data rate with SVM

Here, the idea is to use the prediction of SVM to predict the transmission probability. Then it is possible for a vehicle to compute the expected throughput : W p c (r, T, P cs ) versus the capture threshold used T and to choose the best transmission rate W corresponding to a given value of T . We assume a throughput of W = 6M bits/s with T = 1 , a throughput of W = 9M bits/s with T = 4.65 and a throughput of W = 12M bits/s with T = 7. These values of T are compatible with the Shannon law W = k log 2 (1 + T ). In Figure IV.5 we have shown the optimal data-rate algorithm which optimizes the expected throughput. We observe that up to 154m the best data-rate is 12M bits/s and then we have to use the lowest data rate of 6M bits/s.

V. CONCLUSION

In this paper we use SVM to predict the probability of successful transmissions in a VANET using a CSMA access scheme such as IEEE 802.11p. The prediction of the SVM technique is excellent when we use a data base built with an analytical model. We observe that even with errors the prediction of the SVM technique very good. The SVM technique can be used to build a dynamic rate control algorithm which optimizes the VANET average throughput. Although this initial study is very encouraging further investigations such as introducing more errors, changing the fading law, using real figures for the database etc. should be carried out to confirm our first results. Moreover other machine learning techniques such as Random Forest, K Nearest Neighbor (KNN), etc. could be used to optimize the transmission in VANETs. We think that similar good results would be obtained. This study and a careful comparison of these techniques is left as future work.

eliminationFig. II. 1 .

 1 Fig. II.1. Matern CSMA selection process and an example of overelimination.

Fig. IV. 1 .

 1 Fig. IV.1. Probability of successful reception versus T (x= 75m , µ = 10, β = 4).

  Fig. IV.2. Probability of successful reception versus T (x= 125m , µ = 10, β = 4).

  Fig. IV.3. Probability of successful reception versus T with error in the database (x= 75m , µ = 10, β = 4).

Fig. IV. 4 .

 4 Fig. IV.4. Probability of successful reception versus T with error in the database (x= 125m , µ = 10, β = 4).

Fig. IV. 5 .

 5 Fig. IV.5. Optimal data rate versus distance vehicle-RSU, µ = 10, β = 4).

This can be used also to compute the probability of the successful reception of a transmission between two vehicles.

The power received P = P 0 F l(r) and we set P 0 = 1

We omit the thermal noise but it could be easily added, as is explained below.


Estelle Lecluze

Bernard Jégou

Antoine D Rolland

Frédéric Chalmel
email: frederic.chalmel@inserm.fr

New transcriptomic tools to understand testis development and functions

Keywords: Testis, development, spermatogenesis, germ cells, RNA-sequencing, transcriptomics Highlights RNA-sequencing applied to testis biology ceRNAs: competing endogenous RNA 1 germ cells lncRNA: Long noncoding RNA miRNA: micro RNA spermatogonia Spt: spermatids Spz: spermatozoa SSCs: spermatogonial stem cells UHTS: ultra-high-throughput sequencing

The testis plays a central role in the male reproductive system -secreting several hormones including male steroids and producing male gametes. A complex and coordinated molecular program is required for the proper differentiation of testicular cell types and maintenance of their functions in adulthood. The testicular transcriptome displays the highest levels of complexity and specificity across all tissues in a wide range of species. Many studies have used high-throughput sequencing technologies to define the molecular dynamics and regulatory networks in the testis as well as to identify novel genes or gene isoforms expressed in this organ. This review intends to highlight the complementarity of these transcriptomic studies and to show how the use of different sequencing protocols contribute to improve our global understanding of testicular biology.

Introduction

The transcriptome can be defined as the entire set of transcripts present in a given biological sample.

For a long time, the central dogma in molecular biology assumed that one gene is transcribed into one messenger RNA (mRNA) which in turn is translated into one protein that fulfills various structural, biological and/or regulatory functions within the cell. However, it is now clear that the transcriptome is a far more complex machinery, composed by several RNA classes performing a myriad of functions. About two thirds of human genes contain more than one alternatively spliced exon [START_REF] Johnson | Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays[END_REF] and are thus able to produce numerous RNA and protein isoforms. A large part of the transcriptome is also composed of RNAs that do not encode proteins -noncoding RNAs (ncRNAs). These are involved in a wide variety of biological processes (for review, see: [START_REF] Cech | The noncoding RNA revolution -Trashing old rules to forge new ones[END_REF], including testis differentiation/development [START_REF] Rastetter | The role of non-coding RNAs in male sex determination and differentiation[END_REF][START_REF] Taylor | Long non-coding RNA regulation of reproduction and development[END_REF] and spermatogenesis [START_REF] Luk | Long noncoding RNAs in spermatogenesis: insights from recent high-throughput transcriptome studies[END_REF]. Long noncoding RNAs (lncRNAs) constitute the first class of noncoding transcripts, longer than 200bp, and are involved in many regulatory mechanisms, such as transcriptional, post-transcriptional and direct protein activity regulation [START_REF] Wang | Molecular Mechanisms of Long Noncoding RNAs[END_REF]. Small noncoding RNAs (sncRNAs) refer to transcripts shorter than 200bp that can be further divided into micro RNAs (miRNAs), small interfering RNAs (siRNAs) or Piwi interacting RNAs (piRNAs), most of which regulate gene expression at a transcriptional and/or post-transcriptional level (for review, see : [START_REF] Luo | Small non-coding RNAs and their associated proteins in spermatogenesis[END_REF]. Capturing all types of RNA species is necessary to fully understand the transcriptional network at play within a given cell.

Owing to the intrinsic structural differences between mRNAs, lncRNAs and sncRNAs, transcriptomic studies typically focus on a single type of RNA. These intrinsic differences also warrant specific extraction methods, sequencing protocols and distinctive analytical strategies.

The first transcriptomic technology, called Expressed Sequenced Tag (EST), was based on the Sanger method and consisted of partial sequencing of cDNAs cloned into bacterial plasmids. The serial analysis of gene expression (SAGE) [START_REF] Velculescu | Serial analysis of gene expression[END_REF] was an improved version of the EST technique that allowed increasing the sequencing throughput as well as quantifying the corresponding transcripts. Both EST and SAGE technics already introduced the notion of transcript assembly (tags concatenated into contigs) prior to association of corresponding genes in a reference genome. While these methodologies paved the way to decipher transcriptomes, they remained resource, time and money consuming. The technology that allowed transcriptomics to truly become a high-throughput discipline was DNA microarrays. These consist of solid surfaces on which DNA fragments, called probes, are spotted or synthetized in situ [START_REF] Heller | DNA Microarray Technology: Devices, Systems, and Applications[END_REF][START_REF] Nelson | Microarrays have arrived: gene expression tool matures[END_REF][START_REF] Pozhitkov | Oligonucleotide microarrays: widely applied poorly understood[END_REF][START_REF] Schena | Quantitative monitoring of gene expression patterns with a complementary DNA microarray[END_REF]. The complementary sequence between probes and transcripts (or corresponding cDNAs) allows their specific hybridization, while radioactivity or fluorescence intensity emitted following hybridization is used for quantification purposes. The design of probes is critical and requires prior knowledge of genes and transcripts. Aside from the whole-genome tiling arrays designed to interrogate an entire genome [START_REF] Mockler | Applications of DNA tiling arrays for whole-genome analysis[END_REF], this technology does not allow the characterization of new transcriptional events and, ultimately, the discovery of new genes. Nonspecific hybridization can also result in biased transcript quantification [START_REF] Pozhitkov | Oligonucleotide microarrays: widely applied poorly understood[END_REF]. Even though the number of PubMed records using the term "microarray" is declining since 2015, most probably because of the democratization of high-throughput RNA-sequencing (RNA-seq), the overall fast assessment of gene expression at a reasonable price explains why microarrays remain a widely-used technology.

Over the last decade, ultra-high-throughput sequencing (UHTS) technologies have revolutionized transcriptomics [START_REF] Morozova | Applications of New Sequencing Technologies for Transcriptome Analysis[END_REF][START_REF] Wang | RNA-Seq: a revolutionary tool for transcriptomics[END_REF], and all fields under the umbrella of genomics.

Briefly, total RNAs or a subset of these are reverse-transcribed, amplified by PCR and fragmented.

Subsequently, millions of short DNA fragments are sequenced in parallel, leading to generation of many sequences called reads. The mapping of these reads onto a reference genome (genome-based assembly) or of reads to each other (de novo assembly) is then required to reconstruct the initial transcripts. Finally, the expression level is assessed by counting reads associated to each transcript or gene (Figure 1). Such quantification methods have been shown to be accurate and well-correlated to other technologies such as microarrays, and to eventually outperform these in terms of accuracy for low-abundance transcripts (Chen et al., 2017a;[START_REF] Izadi | A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets[END_REF][START_REF] Wang | The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance[END_REF]. Since the emergence of RNA-seq, the accuracy and sensitivity of related methodologies have greatly improved. In addition, these techniques have diversified to respond to scientists' specific biological questions [START_REF] Head | Library construction for next-generation sequencing: overviews and challenges[END_REF]. For example, the sequencing depth can be improved by selecting RNAs of specific interest, such as polyA-RNAs, or by depleting undesired ones (e.g. rRNAs). Depending on the study's focus, the sequencing protocol can also be optimized. Single-end sequencing is usually sufficient for gene expression analysis, while paired-end sequencing is highly recommended for the purpose of transcript assembly -an absolute pre-requisite to the identification of new isoforms or novel genes [START_REF] Conesa | A survey of best practices for RNAseq data analysis[END_REF]. Preserving strand information is also crucial to determine the direction in which a given locus is transcribed, allowing a better gene prediction in non-model organisms and the discovery of antisense lncRNAs. Over the past years, many bioinformatic tools have also been developed to improve all steps of UHTS analyses, including mapping of reads to a reference genome, transcript reconstruction and quantification, and differential expression analysis. Rather than describing the best practices in experimental design [START_REF] Hardwick | Reference standards for next-generation sequencing[END_REF], RNA-seq protocol [START_REF] Conesa | A survey of best practices for RNAseq data analysis[END_REF][START_REF] Han | Advanced Applications of RNA Sequencing and Challenges[END_REF][START_REF] Hrdlickova | RNA-Seq methods for transcriptome analysis[END_REF] or differential expression analysis [START_REF] Fang | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments[END_REF][START_REF] Huang | Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software[END_REF][START_REF] Kvam | A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data[END_REF][START_REF] Wu | Experimental Design and Power Calculation for RNA-seq Experiments[END_REF] , this review intends to illustrate the wide range of biological questions that can be answered, thanks to these sequencing methods, using the testis as a complex organ model.

The establishment and maintenance of testicular functions, i.e. the secretion of several hormones including androgens and the continuous production of male gametes, require a permanent communication network between several cell types. The testicular transcriptome therefore represents the sum of transcripts expressed in distinct cell populations, present in various proportions within the testis, including Leydig cells (that produce androgens), peritubular cells (that surround seminiferous tubules), Sertoli cells (that support germ cells) and germ cells (GCs) at various differentiation stages (mitotic spermatogonia, meiotic spermatocytes and haploid spermatids). Despite its extreme cellular complexity, a commonly-used strategy to study the testis consists of using the whole organ at different ages: fetal or postnatal. The expression changes highlighted with such approaches can be used to identify: i) expression dynamics within a given cell population, and/or ii) preferential expression in cell types whose proportions fluctuate during development. Additionally, many techniques allow the isolation of enriched populations of each testicular cell type (Figure 2). These two different strategies have recently been combined with the UHTS methods, notably RNA-seq, to gain insights into testis biology.

Tissue profiling analyses and first evidences of a testicular specificity

RNA-seq has been used in a wide range of species to perform tissue profiling analyses, i.e. studies in which the transcriptomes of various tissues or organs, including the testis, are analyzed and compared. A pioneer study using RNA-seq was performed on several tissues in humans and rodents [START_REF] Ramsköld | An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data[END_REF]. This study demonstrated that the most complex tissues in terms of number of expressed genes and RNA classes were the brain, kidney and testis. With the aim of describing the evolutionary dynamics of the mammalian transcriptomics, Brawand and colleagues investigated six organs from several species representing mammalian lineages and avians [START_REF] Brawand | The evolution of gene expression levels in mammalian organs[END_REF]. This analysis pointed towards the testis being the most rapidly evolving organ with regards to gene expression, thereby confirming previous phenotypic observations [START_REF] Harcourt | Testis weight, body weight and breeding system in primates[END_REF]. UHTS has also been combined with proteomics analysis in order to define the specific proteome of several organs [START_REF] Djureinovic | The human testis specific proteome defined by transcriptomics and[END_REF]. The authors used RNA-seq as a clue to determine the protein-coding genes specifically expressed in all the tissues analyzed and concluded that the testis was by far the organ with the highest number of tissue-specific genes.

Additionally, a landmark RNA-seq study comparing transcriptomes of 24 tissues and cell types in Humans was performed to gain insight into the role of lncRNAs in testis specificity [START_REF] Cabili | Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses[END_REF]. The authors found that lncRNAs are transcribed in a highly tissue-specific manner and that a third of these are predominantly expressed in the testis. These results were further confirmed by a tissue profiling analysis across 11 species, including mammals and tetrapods [START_REF] Necsulea | The evolution of lncRNA repertoires and expression patterns in tetrapods[END_REF], which also showed that the high divergence rate of genes expressed within the testis does not exclusively apply to protein-coding genes, but is also inclusive of lncRNAs. This testis-specific expression of lncRNAs was further investigated by Zhang and collaborators, who performed RNA-seq on the whole testis and mature sperm.

They compared their results with six other mouse tissues by integrating available data from NONCODE database, and they found that almost 7000 sequenced lncRNAs were exclusively expressed in the testis or sperm. Unlike other tissues, the average level of lncRNAs expression appeared higher than that of mRNAs in mature sperm. These RNA-seq based studies have confirmed previous findings from microarray experiments (for reviews, see: [START_REF] Calvel | Testicular postgenomics: targeting the regulation of spermatogenesis[END_REF][START_REF] Chalmel | Linking transcriptomics and proteomics in spermatogenesis[END_REF][START_REF] Zhu | Transcriptome research on spermatogenic molecular drive in mammals[END_REF] and have provided additional insight into testis-specificity of lncRNAs. The high tissue-specificity of the adult testis is mostly due to the germline expression program. During differentiation (i.e. meiosis and spermiogenesis), male GCs undergo unique biological processes that require molecular factors expressed exclusively in the testis.

Additionally, strong evolutionary constraints appear to act on the male gonad, shaping its genes with exaggerated traits, at both transcripts' sequences and expression levels.

Gonad development and somatic cell differentiation

Following sexual differentiation, the bipotent gonad differentiates to either a testis or an ovary. In the testis, this involves the differentiation of Sertoli and Leydig cells, the formation of testis cords as well as the early establishment of a relatively prominent vasculature. These developmental processes occur during the entire embryonic period and continue after birth. Many microarray studies have investigated these critical processes in various species and paved the way for a better understanding of the underlying molecular mechanisms [START_REF] Beverdam | Expression profiling of purified mouse gonadal somatic cells during the critical time window of sex determination reveals novel candidate genes for human sexual dysgenesis syndromes[END_REF][START_REF] Bouma | Transcriptional profile of mouse pre-granulosa and Sertoli cells isolated from early-differentiated fetal gonads[END_REF][START_REF] Combes | Expression and Functional Analysis of Dkk1 during Early Gonadal Development[END_REF]del Valle et al., 2017;[START_REF] Houmard | Global Gene Expression in the Human Fetal Testis and Ovary[END_REF][START_REF] Jameson | Temporal transcriptional profiling of somatic and germ cells reveals biased lineage priming of sexual fate in the fetal mouse gonad[END_REF][START_REF] Munger | Fine Time Course Expression Analysis Identifies Cascades of Activation and Repression and Maps a Putative Regulator of Mammalian Sex Determination[END_REF][START_REF] Small | Profiling Gene Expression During the Differentiation and Development of the Murine Embryonic[END_REF]. To date, only a few studies performed in non-mammalian species, have analyzed this process at the whole organ level with the use of RNA-seq.

For instance, the transcriptomes of chicken ovaries and testes at two developmental time-points surrounding sex differentiation (4.5 and 6 dpc) were investigated using a poly(A)-enrichment protocol [START_REF] Ayers | Identification of candidate gonadal sex differentiation genes in the chicken embryo using RNA-seq[END_REF]. Additionally, the transcriptional dynamics throughout gonadal development were studied in Portunus trituberculatus by sequencing whole testis and ovaries at various stages of development [START_REF] Meng | De novo Transcriptome Analysis of Portunus trituberculatus Ovary and Testis by RNA-Seq: Identification of Genes Involved in Gonadal Development[END_REF]. The authors undertook de novo transcript assembly using the Trinity tool [START_REF] O'neil | Assessing De Novo transcriptome assembly metrics for consistency and utility[END_REF] and used protein databases (Nr, Pfam, Swiss-Prot) as well as Blast2go and WEGO softwares for annotation purposes. Owing to the non-reliance of RNA seq on genome annotation, it offered a great potential of discovery in non-model species studies.

Till date, only a single study has focused on somatic cell differentiation within the fetal mammalian testis using RNA-seq [START_REF] Mcclelland | Purification and Transcriptomic Analysis of Mouse Fetal Leydig Cells Reveals Candidate Genes for Specification of Gonadal Steroidogenic Cells1[END_REF]. The authors used FACS to isolate sf1-eGFP cells from mouse testes at different stages. By comparing Sertoli cells (high-GFP cells), Leydig cells (low-GFP cells) and GFP-negative cells, they were able to identify 61 genes, previously unknown to be involved in fetal Leydig cells differentiation, prior to the onset of steroidogenic genes. In this context, it is also worth mentioning the RNA-seq analysis of Sertoli cells isolated from Sox9-EGFP mouse testes at different postnatal ages [START_REF] Zimmermann | Research Resource: The Dynamic Transcriptional Profile of Sertoli Cells During the Progression of Spermatogenesis[END_REF]. This study also integrated data from purified GCs [START_REF] Soumillon | Cellular Source and Mechanisms of High Transcriptome Complexity in the Mammalian Testis[END_REF] to filter out transcripts arising from potential residual GCs. By doing so, the authors successfully described the expression dynamics of known transcripts as well as of newly discovered isoforms and unannotated transcripts (29 novel unknown intronic or intergenic transcripts) in Sertoli cells as they differentiate from immature proliferating cells to mature quiescent cells (in charge of sustaining spermatogenesis).

Primordial germ cells and gonocytes

Studies on sex differentiation and testicular somatic cells using RNA-seq are less numerous than microarray-based studies (see above). However, there have been many studies investigating primordial germ cells (PGCs) and/or gonocytes by means of UHTS over the recent years. These cells indeed undergo a deep epigenetic reprograming involving removal of cytosine methylation from imprinted genes and restoration of totipotency [START_REF] Hajkova | Genome-wide reprogramming in the mouse germ line entails the base excision repair pathway[END_REF][START_REF] Popp | Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency[END_REF][START_REF] Seki | Extensive and orderly reprogramming of genome-wide chromatin modifications associated with specification and early development of germ cells in mice[END_REF] (reviewed in this issue). In this context, many studies have characterized the chromatin status of immature GCs from Oct4-GFP mice [START_REF] Hammoud | Transcription and imprinting dynamics in developing postnatal male germline stem cells[END_REF][START_REF] Kubo | DNA methylation and gene expression dynamics during spermatogonial stem cell differentiation in the early postnatal mouse testis[END_REF][START_REF] Lesch | A set of genes critical to development is epigenetically poised in mouse germ cells from fetal stages through completion of meiosis[END_REF][START_REF] Seisenberger | The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells[END_REF] by combining RNA-seq together with epigenomics technologies (for review, see : [START_REF] Mensaert | Next-generation technologies and data analytical approaches for epigenomics[END_REF], including bisulfite sequencing (BS-seq), (hydroxyl-)methylated DNA immunoprecipitation followed by sequencing ((h)MeDIP-seq) [START_REF] Jacinto | Methyl-DNA immunoprecipitation (MeDIP): hunting down the DNA methylome[END_REF][START_REF] Jin | Genomic mapping of 5-hydroxymethylcytosine in the human brain[END_REF]; chromatin immunoprecipitation followed by sequencing (ChIP-seq) [START_REF] Park | ChIP-seq: advantages and challenges of a maturing technology[END_REF], or nucleosome occupancy and DNA methylation profiling followed by sequencing (NOMe-seq) [START_REF] Kelly | Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules[END_REF]. A first study compared gene expression and DNA methylation (using BS-seq) and showed that global erasure of methylation does not lead to promiscuous transcription, including retrotransposons, suggestive of other transcriptional repression mechanisms in these cells [START_REF] Seisenberger | The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells[END_REF]. A second study correlated transcriptional activity with poised chromatin domains, i.e. which harbor both activation (H3K4me3) and repression (H3K27me3) histone marks, using ChIP-seq and highlighted a set of essential developmental regulators which poised chromatin state was conserved from fetal life (in PGCs) to adulthood (in meiotic and postmeiotic germ cells) [START_REF] Lesch | A set of genes critical to development is epigenetically poised in mouse germ cells from fetal stages through completion of meiosis[END_REF]. The authors further proposed that the flexible transcriptional state of these genes from fertilization onwards is essential to regenerate totipotent cells from differentiated germ cells. Finally, one study combined BS-seq and RNA-seq to compare mouse gonocytes (16.5dpc), prospermatogonia (0.5 dpp), undifferentiated (KIT-) and differentiating (KIT+) spermatogonia (7.5 dpp), allowing to correlate methylation status together with were changes in expression of important genes [START_REF] Kubo | DNA methylation and gene expression dynamics during spermatogonial stem cell differentiation in the early postnatal mouse testis[END_REF].

In humans, the in vitro differentiation of the PGC like cells (hPGCLCs) from embryonic stem cells (hESCs) could represent a great mean for the study of PGCs but such models also require a better understanding of fetal GCs. For instance, a first transcriptional landscape of purified gonocytes (cKIT+) from human fetal testes and ovaries, between 8 and 20 gestational weeks (GW), was generated to evaluate and compare five protocols for in vitro generation of hPGCLCs, all of which proved unsatisfactory [START_REF] Gkountela | The ontogeny of cKIT+ human primordial germ cells proves to be a resource for human germ line reprogramming, imprint erasure and in vitro differentiation[END_REF]. In 2015, four independent studies investigated human PGCs and gonocytes between 4 and 19 GW using similar isolation procedures [START_REF] Gkountela | DNA demethylation dynamics in the human prenatal germline[END_REF][START_REF] Guo | The transcriptome and DNA methylome landscapes of human primordial germ cells[END_REF]Irie et al., 2015;[START_REF] Tang | A Unique Gene Regulatory Network Resets the Human Germline Epigenome for Development[END_REF]. Combination of RNA-seq and BS-seq produced comprehensive datasets involving transcriptional and single-base-resolution methylome dynamics during GC development. Except for SINE-variable number of tandem repeats-Alu elements (SVAs) [START_REF] Tang | A Unique Gene Regulatory Network Resets the Human Germline Epigenome for Development[END_REF], global DNA demethylation was not found to correlate with gene upregulation, notably for transposons, implicating the presence of another regulatory process [START_REF] Gkountela | DNA demethylation dynamics in the human prenatal germline[END_REF][START_REF] Tang | A Unique Gene Regulatory Network Resets the Human Germline Epigenome for Development[END_REF]. Another study combined RNA-seq and NOMe-seq with previously published ChIP-seq data (H. Guo et al., 2017), showing that that promoters' accessibility is strongly correlated to the corresponding gene expression level. The SVAs elements also specifically displayed an open state in fetal GCs. In terms of transcriptional and epigenomic dynamics during GC development, two studies have successfully highlighted similarities as well as significant differences between human and mouse (H. Guo et al., 2017;[START_REF] Tang | A Unique Gene Regulatory Network Resets the Human Germline Epigenome for Development[END_REF].

Other labs also developed protocols for the in vitro differentiation of hPGCLCs from hESCs or human induced pluripotent stem cells (hISPCs). A first group evaluated such in vitro differentiation protocol by comparing hPGCLCs to hPGCs, cultured hESCs, pre-induced cells, the seminoma-derived Tcam-2 cell line as well as somatic cells using RNA-seq [START_REF] Irie | SOX17 is a critical specifier of human primordial germ cell fate[END_REF]. They first showed a significant correlation between the transcriptomes of hPGCLCs, hPGCs and Tcam-2 cells. They also identified hPGCLCs as potential "differentiating" hPGCs as they do not express the typical late germ cell markers DAZL, VASA and MAEL, further demonstrating that hPGCLCs constitute an interesting model to investigate the mechanisms of human germ line establishment. Most importantly, they identified the requirement of SOX17 for specification of human PGCs [START_REF] Irie | SOX17 is a critical specifier of human primordial germ cell fate[END_REF]. A second group compared the transcriptome of hPGCLCs with other previously published RNA-seq data and evidenced significant differences in terms of gene expression program and epigenetic reprogramming between human and mouse PGCLCs as well as regarding the respective timing of specification and reprogramming between these species [START_REF] Von Meyenn | Comparative Principles of DNA Methylation Reprogramming during Human and Mouse In Vitro Primordial Germ Cell Specification[END_REF].

Spermatogonial stem cells

Spermatogonia (Spg) constitute a heterogeneous population of male GCs that include spermatogonial stem cells (SSC), proliferative progenitor Spg and differentiating Spg committed to spermatogenesis. In mouse, undifferentiated Spg consist of type A single (As), paired (Apr) and aligned (Aal4-16) Spg while differentiating Spg consist of A1-4 Spg, intermediate Spg and type B Spg. Whether only As Spg encompass the pool of SSC is still a matter of debate (for review, see [START_REF] Lord | A revised Asingle model to explain stem cell dynamics in the mouse male germline[END_REF]. In humans, there are only three types of Spg: undifferentiated Adark and Apale Spg, which are thought to be reserve and active SSC, respectively, and type B Spg which are differentiating progenitors [START_REF] Boitani | Spermatogonial cells: mouse, monkey and man comparison[END_REF]).

An extensive characterization (RNA-seq, smallRNA-seq, ChIP-seq and BS-Seq) of mouse GCs at different stages, including self-renewing Spg (THY1+), differentiating Spg (KIT+), spermatocytes (Spc), spermatids (Spt) and mature sperm allowed for the unraveling of key pathways regulating the balance between self-renewal and differentiation of SSCs [START_REF] Hammoud | Chromatin and Transcription Transitions of Mammalian Adult Germline Stem Cells and Spermatogenesis[END_REF]. This study also showed epigenetic specificities concerning these cells, which includes the poising of key transcription factors as well as the activity of DNA-methylated promoters during gametogenesis. By including additional markers (THY1, KIT, OCT4, ID4, and GFRa1), the same group further characterized the transcriptome and epigenome of adult mice SSCs [START_REF] Hammoud | Transcription and imprinting dynamics in developing postnatal male germline stem cells[END_REF] and proposed a classification of spermatogonial subpopulations, i.e. epithelial-like Spg (THY1+; highOCT4, ID4, and GFRa1), mesenchymal-like Spg (THY1+; moderate OCT4 and ID4; high mesenchymal markers), and Spg committed to gametogenesis (high KIT+), that differ in terms of methylation status and imprinting. The recent identification of ID4 as being preferentially-expressed in a subset of mouse As Spg and playing an important role in the maintenance of SSC pool [START_REF] Oatley | Inhibitor of DNA Binding 4 Is Expressed Selectively by Single Spermatogonia in the Male Germline and Regulates the Self-Renewal of Spermatogonial Stem Cells in Mice1[END_REF], allowed further characterization of the SSC transcriptome [START_REF] Chan | Functional and molecular features of the Id4+ germline stem cell population in mouse testes[END_REF][START_REF] Helsel | ID4 levels dictate the stem cell state in mouse spermatogonia[END_REF][START_REF] Hermann | Transcriptional and translational heterogeneity among neonatal mouse spermatogonia[END_REF][START_REF] Mutoji | TSPAN8 Expression Distinguishes Spermatogonial Stem Cells in the Prepubertal Mouse Testis[END_REF]. Taking advantage of previously-published transcriptomic datasets [START_REF] Hammoud | Transcription and imprinting dynamics in developing postnatal male germline stem cells[END_REF][START_REF] Mutoji | TSPAN8 Expression Distinguishes Spermatogonial Stem Cells in the Prepubertal Mouse Testis[END_REF], a list of 123 genes representing the core expression program of SSC was identified [START_REF] Helsel | ID4 levels dictate the stem cell state in mouse spermatogonia[END_REF].

A great work has been recently conducted, using SSEA4 as a marker of human SSCs, and KIT as a marker of Spg committed into gametogenesis (J. Guo et al., 2017). The authors performed whole-genome bisulfite sequencing (WGBS), Assay for Transposase-Accessible Chromatin with highthroughput sequencing (ATAC-seq) and RNA-seq on these two purified cell populations, aiming to describe the complete DNA methylation, chromatin and transcriptional states which may explain their stem-ness differences, and focused their analysis on pluripotency factors. Single-cell RNA-seq technologies were also used to profile the transition between SSEA4+ and KIT+ cells. The authors described four distinct cellular states, which highlight more precisely the differentiation pathway of human SSCs. This work represents a great example of the complementarity and power of different high-throughput technologies (J. Guo et al., 2017).

A number of studies have also been conducted in order to characterize the repertoire of ncRNAs, including sncRNAs, expressed in immature male GCs. The pioneer work in this field compared mouse SSC (THY1+) to somatic cells (THY1-) and identified a set of preferentially-expressed miRNAs in selfrenewing SSC [START_REF] Niu | MicroRNA-21 regulates the self-renewal of mouse spermatogonial stem cells[END_REF]. The use of unbiased sequencing also confirmed that piRNAs represent the preponderant class of smallRNA in differentiating mouse GCs, i.e. Spc and Spt, whereas, other cell types such as ESCs, SSCs, Sertoli cells, and mesenchymal stem cells preferentially express miRNAs [START_REF] Tan | miRNA signature in mouse spermatogonial stem cells revealed by high-throughput sequencing[END_REF]. Recently, RNA-seq was also used to characterize not only classical mRNAs and lncRNAs but also circular RNAs (circRNAs) expressed in SSCs and ovarian germline stem cells (X. Li et al., 2017).

circRNA is a novel class of ncRNA which specificity lies in its covalent bond linking the 3′ and 5′ ends generated by backsplicing (for review, see: [START_REF] Huang | The emerging role of circular RNAs in transcriptome regulation[END_REF]. Thanks to correlation analysis, a competing endogenous RNA (ceRNAs) network, comprising lncRNA-miRNA-circRNA-mRNA, identified competitive RNAs -capable of binding and inhibiting the regulatory activities of other RNAs.

Gene expression program at play during spermatogenesis

While at the first glance, working with a whole organ might seem irrelevant for studying GC differentiation, it has been successfully demonstrated that in several mammalian species (including rodents), the first wave of spermatogenesis is synchronous in all seminiferous tubules [START_REF] Bellvé | Spermatogenic cells of the prepuberal mouse. Isolation and morphological characterization[END_REF][START_REF] Oakberg | Duration of spermatogenesis in the mouse[END_REF][START_REF] Oakberg | Duration of spermatogenesis in the mouse and timing of stages of the cycle of the seminiferous epithelium[END_REF]. Therefore, following the evolution of the testicular transcriptome at representative post-natal ages allows extrapolation of the gene expression program of each GC population (Figure 2). This hypothesis is based on the assumption that observed transcriptional variations are due to germline differentiation. This strategy has the critical advantage of conserving the testis's integrity by preserving cell junctions and avoiding transcriptome modifications inherent to cell isolation procedures. In 2013, Gong and collaborators were among the first to profile the transcriptome of the developing mouse testis at three postnatal stages (infant, 6 dpp; juvenile, 4 weeks after birth; and, adult, 10 weeks after birth) using UHTS and a polyA-enrichment protocol [START_REF] Gong | Transcriptome profiling of the developing postnatal mouse testis using next-generation sequencing[END_REF]. At the same time, Laiho and collaborators published about the transcriptome evolution during the first wave of spermatogenesis using postnatal mouse testes at 7, 14, 17, 21 and 28 dpp [START_REF] Laiho | Transcriptome profiling of the murine testis during the first wave of spermatogenesis[END_REF]. Instead of using a classical polyA-enriched approach, the authors used a rRNA depletion strategy to characterize the transcriptional dynamics of a wide range of protein-coding and noncoding transcripts. They observed for the first time that lncRNAs accumulate during meiotic and postmeiotic stages. Kaessmann's lab was among the first to determine the cellular source of the testicular transcriptome complexity during mouse spermatogenesis [START_REF] Soumillon | Cellular Source and Mechanisms of High Transcriptome Complexity in the Mammalian Testis[END_REF] using RNA-seq on five enriched populations of testicular cells including Sertoli cells, type A Spg, pachytene Spc, round Spt and spermatozoa (Spz). Their results confirmed that meiotic and postmeiotic GCs have the highest transcriptome complexity (considering protein-coding genes, lncRNAs, pseudogenes or transposable elements), in comparison to testicular somatic cells (Sertoli cells) or other organs. At the same time, Gan and colleagues also published a landmark paper describing global 5-hydroxymethylcytosine distribution in GCs and its impact on the mouse germline transcriptional landscape [START_REF] Gan | Dynamics of 5-hydroxymethylcytosine during mouse spermatogenesis[END_REF]. The authors sequenced the transcriptome of seven isolated cell populations (primitive SpgA, SpgA, SpgB, preleptotene Spc, pachytene Spc, round Spt and elongated Spt) and found that the highest transcriptional transitions during spermatogenesis occurred between preleptotene Spc and pachytene Spc, and also between pachytene Spc to round Spt. The following year, Chalmel and collaborators used RNA-seq to study the rat testicular noncoding expression program and to discover novel genes [START_REF] Chalmel | High-Resolution Profiling of Novel Transcribed Regions During Rat Spermatogenesis[END_REF]. Four distinct testicular cell populations (Sertoli cells, Spg, Spc and Spt) were investigated using a rRNA depletion strategy. This allowed them to discover 1419 novel genes, called TUTs for testis-expressed unannotated transcripts, most of which exhibited characteristic genomic features of lncRNAs. This study confirmed that lncRNAs and TUTs accumulate in the meiotic and postmeiotic GCs [START_REF] Laiho | Transcriptome profiling of the murine testis during the first wave of spermatogenesis[END_REF][START_REF] Soumillon | Cellular Source and Mechanisms of High Transcriptome Complexity in the Mammalian Testis[END_REF] and identified a distinct class of meiosis-related lncRNAs exhibiting exons twice as long as those of other transcripts. By using a ''proteomics informed by transcriptomics'' (PIT) strategy combining RNA sequencing data with shotgun proteomics analyses, the authors further identified 44 novel proteincoding transcripts initially thought to be lncRNAs or TUTs [START_REF] Chocu | Forty-Four Novel Protein-Coding Loci Discovered Using a Proteomics Informed by Transcriptomics (PIT) Approach in Rat Male Germ Cells[END_REF][START_REF] Evans | De novo derivation of proteomes from transcriptomes for transcript and protein identification[END_REF].

Recently, Da Cruz and colleagues took advantage of improved FACS-based methods [START_REF] Da Cruz | Transcriptome analysis of highly purified mouse spermatogenic cell populations: gene expression signatures switch from meiotic-to postmeiotic-related processes at pachytene stage[END_REF] to isolate leptotene/zygotene Spc, pachytene Spc, secondary Spc, and round Spt and subsequently performed RNA-seq analysis. Their meiosis-centric study revealed a large number of genes showing a burst of expression at the leptotene/zygotene stage. The authors also confirmed that the transcription of some spermiogenesis-related genes is initiated as early as pachytene Spc. They also found that some Xlinked genes are overexpressed during meiosis, thus escaping the meiotic sex chromosome inactivation (MSCI) [START_REF] Ichijima | Sex chromosome inactivation in germ cells: emerging roles of DNA damage response pathways[END_REF][START_REF] Van Der Heijden | The X and Y chromosome in meiosis: how and why they keep silent[END_REF]. Another RNA-seq study on five enriched mouse GCs populations focused on lncRNAs and also identified candidate genes that escape MSCI [START_REF] Wichman | Dynamic expression of long noncoding RNAs reveals their potential roles in spermatogenesis and fertility[END_REF]. Moreover, another study analyzed the differential expression of not only mRNAs, lncRNAs but also included circRNAs in mouse primitive SpgA, preleptotene Spc, pachytene Spc and rSpt [START_REF] Lin | Expression dynamics, relationships, and transcriptional regulations of diverse transcripts in mouse spermatogenic cells[END_REF]. Interestingly, the authors minimized the limitations of their study by taking advantage of several previous transcriptomic studies to define the set of testis-specific genes expressed in their samples [START_REF] Brawand | The evolution of gene expression levels in mammalian organs[END_REF][START_REF] Cabili | Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses[END_REF], assess the conservation of the spermatogenic expressed lncRNAs [START_REF] Necsulea | The evolution of lncRNA repertoires and expression patterns in tetrapods[END_REF], compare the circRNAs production across mouse tissues [START_REF] Rybak-Wolf | Circular RNAs in the Mammalian Brain Are Highly Abundant, Conserved, and Dynamically Expressed[END_REF] and investigate the expression of potential precursors of piRNAs [START_REF] Li | An ancient transcription factor initiates the burst of piRNA production during early meiosis in mouse testes[END_REF].

In humans, microarray experiments have been conducted to decipher the specific transcriptome of testicular cell populations and to identify key genes involved in spermatogenesis by using biopsies from patients with distinct infertility phenotypes [START_REF] Chalmel | Global human tissue profiling and protein network analysis reveals distinct levels of transcriptional germline-specificity and identifies target genes for male infertility[END_REF][START_REF] Ellis | Coordinated transcriptional regulation patterns associated with infertility phenotypes in men[END_REF][START_REF] Fox | Feasibility of global gene expression analysis in testicular biopsies from infertile men[END_REF][START_REF] Gatta | Testis transcriptome analysis in male infertility: new insight on the pathogenesis of oligoazoospermia in cases with and without AZFc microdeletion[END_REF][START_REF] Nguyen | Gene expression alterations in cryptorchid males using spermatozoal microarray analysis[END_REF][START_REF] Okada | Genome-Wide Expression of Azoospermia Testes Demonstrates a Specific Profile and Implicates ART3 in Genetic Susceptibility[END_REF][START_REF] Spiess | Cross-platform gene expression signature of human spermatogenic failure reveals inflammatory-like response[END_REF][START_REF] Von Kopylow | Screening for biomarkers of spermatogonia within the human testis: a whole genome approach[END_REF].

Similar approached were also used in rodents with GC-deficient models [START_REF] Ellis | Modulation of the mouse testis transcriptome during postnatal development and in selected models of male infertility[END_REF], and chemical induced infertility [START_REF] Orwig | Genes Involved in Post-Transcriptional Regulation Are Overrepresented in Stem/Progenitor Spermatogonia of Cryptorchid Mouse Testes[END_REF][START_REF] Rockett | Development of a 950-gene DNA array for examining gene expression patterns in mouse testis[END_REF]. Recently, studies have relied on testicular samples from patients who underwent orchiectomy [START_REF] Djureinovic | The human testis specific proteome defined by transcriptomics and[END_REF]. Zhu and colleagues used testicular biopsies of patients with obstructive azoospermia (i.e. with normal spermatogenesis) and combined FACS and MACS methods to obtain purified Spg, Spc and Spt [START_REF] Zhu | Dynamics of the Transcriptome during Human Spermatogenesis: Predicting the Potential Key Genes Regulating Male Gametes Generation[END_REF]. The RNA-seq analysis allowed them to describe stage specific expression of known protein-coding and lncRNAs, as well as to focus their analysis on potential key transcriptional regulators of each GC stage such as HOXs, JUN, SP1, and TCF3. Recently, Jégou and collaborators published a RNA-seq analysis on five testicular cell populations including Leydig cells, Sertoli cells, peritubular cells, Spc and Spt [START_REF] Jégou | Meiotic Genes Are Enriched in Regions of Reduced Archaic Ancestry[END_REF]. By classifying differentially expressed genes into three broad co-expression clusters associated with somatic, meiotic or postmeiotic cells, the authors found that genes expressed during meiosis are significantly depleted in archaic hominin alleles [START_REF] Sankararaman | The Combined Landscape of Denisovan and Neanderthal Ancestry in Present-Day Humans[END_REF][START_REF] Sankararaman | The genomic landscape of Neanderthal ancestry in present-day humans[END_REF]. Jan and collaborators used a microdissection approach to obtain enriched cell populations from adult human testis including A dark and A pale Spg, leptotene/zygotene Spc, early pachytene Spc, late pachytene Spc and round Spt [START_REF] Jan | Unraveling transcriptome dynamics in human spermatogenesis[END_REF]. The dynamic changes of transcriptome have been characterized during human germline differentiation and enlightened that the Spg express transcripts, at protein level, which are required in later stages of spermatogenesis. The authors also compared their sequencing results with previously published datasets, in order to assess the efficiency of their cell isolation protocol [START_REF] Zhu | Dynamics of the Transcriptome during Human Spermatogenesis: Predicting the Potential Key Genes Regulating Male Gametes Generation[END_REF], and to define the conserved expression program between human ad mouse spermatogenesis in GCs populations [START_REF] Da Cruz | Transcriptome analysis of highly purified mouse spermatogenic cell populations: gene expression signatures switch from meiotic-to postmeiotic-related processes at pachytene stage[END_REF].

Splicing factors and regulatory RNA at play during male germ cell differentiation

RNA-binding proteins and splicing factors play a critical role during male gamete development (for reviews, see [START_REF] Bettegowda | Transcription and post-transcriptional regulation of spermatogenesis[END_REF][START_REF] Elliott | The role of potential splicing factors including RBMY, RBMX, hnRNPG-T and STAR proteins in spermatogenesis[END_REF][START_REF] Idler | Control of messenger RNA fate by RNA-binding proteins: an emphasis on mammalian spermatogenesis[END_REF][START_REF] Paronetto | Role of RNA-binding proteins in mammalian spermatogenesis[END_REF][START_REF] Venables | Alternative splicing in the testes[END_REF][START_REF] Walker | RNA processing and the control of spermatogenesis[END_REF]. Several studies also took great advantage of RNA-sequencing to study splicing events occurring during spermatogenesis and particularly in the meiosis phase. Schmid and collaborators compared the transcriptome between whole mouse testis before GCs enter meiosis (6dpp) and at the end of the process (21dpp) [START_REF] Schmid | The splicing landscape is globally reprogrammed during male meiosis[END_REF]. They used a dedicated pipeline, MISO [START_REF] Katz | Analysis and design of RNA sequencing experiments for identifying isoform regulation[END_REF], to identify differentially regulated isoforms or exons across samples, revealing switches in splicing patterns during the meiotic process. They pointed out several splicing factors that may play a key role in the evolution of meiotic protein isoforms, which can be possibly crucial for the germline mitotic-meiotic transition. Margolin and colleagues investigated whole testes at eight developmental stages of between 6 and 38 dpp [START_REF] Margolin | Integrated transcriptome analysis of mouse spermatogenesis[END_REF]. They identified more than 13000 novel isoforms, including 159 predicted transcripts mapped in intergenic regions, further demonstrating that alternative spliced isoforms are abundant through spermatogenesis. They found transcripts mapping on unknown splice junctions for which an open reading frame was maintained. In 2016, the dynamics of the transcriptional landscape governing testis maturation (20 dpp, 75 dpp, 270 dpp) has also been characterized in two closely related species, Meishan and Duroc boars [START_REF] Ding | Histological and transcriptome analyses of testes from Duroc and Meishan boars[END_REF]. Using Sus scrofa as the reference genome to map reads, the authors enlightened a set of potential critical genes for the onset of spermatogenesis. They used a dedicated strategy based on the SOAPsplice tool, allowing them to identify alternative splicing events [START_REF] Huang | SOAPsplice: Genome-Wide ab initio Detection of Splice Junctions from RNA-Seq Data[END_REF]. Importantly, transcriptomic differences in such closely related species illustrates the high divergence of transcription within the testis across mammalian species. Alternative splicing has also been explored in enriched mouse populations of Spc and Spt, to decipher more precisely the splicing changes occurring through transmeiotic differentiation of GC [START_REF] Naro | An Orchestrated Intron Retention Program in Meiosis Controls Timely Usage of Transcripts during Germ Cell Differentiation[END_REF]. Following a polyA-enrichment protocol, the authors used the FAST-DB splicing annotation tool and identified intron retention (IRT) as being the most represented pattern. They showed that IRTs are highly stable transcripts that accumulate in the meiotic nuclei and are enriched in genes involved in mature gamete functions (i.e. Spt development and sperm-egg recognition). The authors therefore suggested that IRT allows storage and stabilization of key transcripts after the transcriptional burst in meiotic cells until their translation in transcriptionally-silenced post-meiotic cells.

Besides the widely described mRNAs and lncRNAs, sncRNAs are involved in many expression regulation pathways and can be divided in different classes according to their genomic features and sequence modifications. Specific RNA-seq protocols had to be developed to allow smallRNA purification, library preparation and sequencing, as well as mapping and annotation of corresponding reads. The role of sncRNAs in testis maturation and spermatogenesis have been recently reviewed (for reviews, see Luo et al., 2016;[START_REF] Meikar | Chromatoid body and small RNAs in male germ cells[END_REF][START_REF] Yadav | Small RNAs in spermatogenesis[END_REF], and will not be extensively described here.

Briefly, sncRNA profiling and differential expression analyses were performed on testes at different developmental stages [START_REF] Gebert | piRNAs from Pig Testis Provide Evidence for a Conserved Role of the Piwi Pathway in Post-Transcriptional Gene Regulation in Mammals[END_REF][START_REF] Li | Genome-wide differential expression of genes and small RNAs in testis of two different porcine breeds and at two different ages[END_REF][START_REF] Lian | A comparative profile of the microRNA transcriptome in immature and mature porcine testes using Solexa deep sequencing[END_REF][START_REF] Liu | Discovery of potential piRNAs from next generation sequences of the sexually mature porcine testes[END_REF], by comparing the testis with other tissues [START_REF] Luo | microRNA profiling in three main stages during porcine spermatogenesis[END_REF], including the ovary [START_REF] Kowalczykiewicz | Characterization of Sus scrofa small non-coding RNAs present in both female and male gonads[END_REF][START_REF] Li | Repertoire of porcine microRNAs in adult ovary and testis by deep sequencing[END_REF]. Several works used whole mouse testes at different ages to study the specific biogenesis of piRNAs during spermatogenesis [START_REF] Beyret | piRNA biogenesis during adult spermatogenesis in mice is independent of the ping-pong mechanism[END_REF][START_REF] Li | An ancient transcription factor initiates the burst of piRNA production during early meiosis in mouse testes[END_REF]. In humans, the small noncoding transcriptome has also been characterized in the adult testis [START_REF] Ha | A comprehensive analysis of piRNAs from adult human testis and their relationship with genes and mobile elements[END_REF][START_REF] Yang | MicroRNA and piRNA profiles in normal human testis detected by next generation sequencing[END_REF] and several other studies have been performed on purified GCs to understand the implication of such sncRNAs during spermatogenesis [START_REF] Gan | piRNA profiling during specific stages of mouse spermatogenesis[END_REF][START_REF] García-López | Diversity and functional convergence of small noncoding RNAs in male germ cell differentiation and fertilization[END_REF][START_REF] Goh | piRNA-directed cleavage of meiotic transcripts regulates spermatogenesis[END_REF][START_REF] Vourekas | Mili and Miwi target RNA repertoire reveals piRNA biogenesis and function of Miwi in spermiogenesis[END_REF][START_REF] Zhang | MIWI and piRNA-mediated cleavage of messenger RNAs in mouse testes[END_REF].

The Noora Kotaya's laboratory is particularly known in the field of reproductive biology for its work on the chromatoid body (CB). The CB is a GC-specific organelle composed of RNAs and RNA-binding proteins which appears in the cytoplasm of GCs during the transition between meiotic and post-meiotic phase of spermatogenesis [START_REF] Kotaja | The chromatoid body: a germ-cell-specific RNA-processing centre[END_REF][START_REF] Meikar | Chromatoid body and small RNAs in male germ cells[END_REF][START_REF] Parvinen | The chromatoid body in spermatogenesis[END_REF][START_REF] Peruquetti | Perspectives on mammalian chromatoid body research[END_REF]. Meikar and collaborators isolated CBs from juvenile mouse testes at 22 and 26 dpp using several steps of cell lysis, filtration and immunoprecipitation [START_REF] Meikar | An atlas of chromatoid body components[END_REF]. They combined sncRNA-seq and RNA-seq to compare purified CB to round Spt and identified sncRNAs, lncRNAs and mRNAs that accumulate within this organelle. This analysis showed that lncRNAs identified as genomic clusters generating piRNAs, accumulate in the CB, suggesting their important role in piRNAs biogenesis.

However, a proteomic analysis failed at identifying the core primary piRNA processing components within the CB, therefore challenging the role of this organelle in the biogenesis of piRNAs.

Resources

While microarrays remain useful and accurate tools for measuring expression levels, RNA-seq provides a more complete transcriptomic solution by allowing transcript isoform determination and eventually new gene discovery, on top of being more accurate when it comes to quantification (Chen et al., 2017b;[START_REF] Izadi | A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets[END_REF][START_REF] Malone | Microarrays, deep sequencing and the true measure of the transcriptome[END_REF][START_REF] Wang | The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance[END_REF]. With the continuous decrease of sequencing costs, the number of RNA-seq studies therefore drastically increased over recent years, and the corresponding datasets now present a great potential for reanalysis or integration within one's own study. Similar to microarrays, the MIAME compliance [START_REF] Brazma | Minimum information about a microarray experiment (MIAME)-toward standards for microarray data[END_REF] applies to UHTS data and generalist databases such as the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) [START_REF] Barrett | NCBI GEO: archive for functional genomics data setsupdate[END_REF], or the ArrayExpress (Kolesnikov et al., 2015, https://www.ebi.ac.uk/arrayexpress/) resources, also serve as repositories of high-throughput sequencing data. Most of the datasets arising from studies described in this review are therefore publicly available in these public repositories (see Supplemental Table ). Despite that several groups have already taken advantage of these available resources, differences in sequencing protocols, such as transcript type and size selection, strand-specificity, length or pairing of reads, may somehow prevent full integration of, or direct comparison between different datasets.

Despite the wide use of UHTS, the handling of such data requires specific skills and is both time and resource consuming. Dedicated databases have therefore started to emerge in order to extract, summarize and/or visualize the corresponding information, including the field of reproductive biology. For instance, the ReproGenomics Viewer (RGV) is a useful toolbox dedicated to genomic data for the reproductive science community [START_REF] Darde | The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community[END_REF]. It consists of a genome browser that allows the visualization of selected datasets from the literature in the genomic context of their respective species. Moreover, crossspecies comparisons are now possible, allowing simultaneous visualization of several types of data (e.g. RNA-seq, ChIP-seq) performed in different species. Currently, studies implemented in RGV mainly focus on testis biology and spermatogenesis, but this tool is likely to be regularly updated in order to expand its focus to other areas of reproductive biology. As described earlier in this review, lncRNAs are expressed in a highly tissue-specific manner and especially in male GCs. GermlncRNA constitutes a dedicated catalogue of known and novel lncRNAs [START_REF] Luk | GermlncRNA: a unique catalogue of long non-coding RNAs and associated regulations in male germ cell development[END_REF], by integrating several published transcriptomic studies covering three GC populations, i.e. type A Spg, pachytene Spc and round Spt [START_REF] Gan | Dynamics of 5-hydroxymethylcytosine during mouse spermatogenesis[END_REF][START_REF] Lee | Revealing the transcriptome landscape of mouse spermatogonial cells by tiling microarray[END_REF][START_REF] Lee | GermSAGE: a comprehensive SAGE database for transcript discovery on male germ cell development[END_REF][START_REF] Soumillon | Cellular Source and Mechanisms of High Transcriptome Complexity in the Mammalian Testis[END_REF][START_REF] Sun | Long non-coding RNA expression profiling of mouse testis during postnatal development[END_REF]. Last but not the least, Spermbase represents a great resource dedicated to sperm transcriptome in four mammalians species, i.e. human, rat, mouse and rabbit [START_REF] Schuster | SpermBase: A Database for Sperm-Borne RNA Contents[END_REF]. Using a standardized sperm RNA isolation and sequencing protocol, this work allows a comparative RNA-seq analysis, covering long as well as small RNAs, and identifying the conserved counterpart of the sperm transcriptome across mammalian species.

New technologies and perspectives

The application fields of UHTS technologies have expanded at an amazing pace since its emergence, enabling investigation of RNA as well as DNA, both quantitatively and qualitatively, at the sequence, structural, and conformation levels. In parallel, the accuracy and throughput of sequencing methods are also improving to overcome technical limitations. For instance, most current UHTS approaches involve amplification steps by PCR and rely on the sequencing of relatively short sequences in order to maintain reasonable sequencing error rates. The future of UHTS particularly relies on the use of PCR-free protocols, to prevent amplification biases, and on increasing the length of reads, to avoid or, at least, reduce and make easier assembly steps for both transcripts and chromosomes/genomes [START_REF] Chu | Innovations and challenges in detecting long read overlaps: an evaluation of the state-of-the-art[END_REF].

In this context, it is worth mentioning the emergence of third generation sequencing technologies which allow the PCR-free or even the direct sequencing of single RNA molecules and generate long reads of up to several kilobases [START_REF] Garalde | Highly parallel direct RNA sequencing on an array of nanopores[END_REF][START_REF] Laver | Assessing the performance of the Oxford Nanopore Technologies MinION[END_REF][START_REF] Lu | Oxford Nanopore MinION Sequencing and Genome Assembly[END_REF][START_REF] Rhoads | PacBio Sequencing and Its Applications[END_REF] (Figure 1). Such technologies remain very expensive and the sequence error rates are still very high (up to 10%), but they are likely to improve on both aspects in the near future. Very importantly, and also quite unexpectedly, both technologies are able to capture information on bases' modifications such as methylation during the sequencing process.

The outcome of transcriptomic studies also greatly depends on the biological material. While whole organs preserve cell-cell interactions and reduce gene expression alterations due to extensive sample handling, they prevent access to discrete cell populations as well as to low-copy transcripts, and they do not allow to ascertain their cellular origins. On the other hand, cell enrichment protocols provide cell-type information and improve sensitivity, but potential contaminations and/or changes in expression cannot be ruled out. Moreover, such a bulk approach, even with enriched cell populations, is likely to mask differences between distinct subpopulations. Over the past years, single cell approaches have been developed and adapted to RNA sequencing (Figures 1 and2). Single-cell RNA-seq (scRNA-seq) includes cell isolation and library preparation with molecular barcodes. Thanks to microfluidic [START_REF] Klein | Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells[END_REF][START_REF] Zilionis | Single-cell barcoding and sequencing using droplet microfluidics[END_REF], droplet-based [START_REF] Macosko | Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets[END_REF] or FACS-based methods [START_REF] Jaitin | Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types[END_REF], scRNA-seq is able to capture the transcriptome of a very large number of cells. These high-throughput technologies [START_REF] Ziegenhain | Comparative Analysis of Single-Cell RNA Sequencing Methods[END_REF] have proved very efficient for inferring and describing the temporal dynamic of cell differentiation, or the spatial description of different cell populations within an organ [START_REF] Kalisky | A brief review of single-cell transcriptomic technologies[END_REF][START_REF] Macaulay | Single-Cell Multiomics: Multiple Measurements from Single Cells[END_REF][START_REF] Tanay | Scaling single-cell genomics from phenomenology to mechanism[END_REF][START_REF] Wagner | Revealing the vectors of cellular identity with single-cell genomics[END_REF]. In the field of reproductive biology, several single-cell transcriptomic studies have already been performed, on human PGCs/gonocytes and fetal somatic cells [START_REF] Guo | The transcriptome and DNA methylome landscapes of human primordial germ cells[END_REF][START_REF] Li | Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions[END_REF] as well as on mouse fetal somatic cells [START_REF] Stévant | Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing[END_REF] (reviewed in this issue). scRNAseq is a promising technology that represents a great potential of discovery, but as with all other technologies, some experimental limitations are still in place. For instance, the dissociation of complex tissues into single-cell suspension can be the first limiting step. Single nucleus RNAseq (snuc-RNAseq) has initially been developed to overcome this issue in the brain [START_REF] Habib | Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons[END_REF][START_REF] Lake | Neuronal subtypes and diversity revealed by singlenucleus RNA sequencing of the human brain[END_REF], and then combined with microfluidic technology, leading to DroNc-seq [START_REF] Habib | Massively parallel single-nucleus RNA-seq with DroNc-seq[END_REF]. Furthermore, the different available technologies still show relatively low sensitivity, allowing to detect medium to highly expressed transcripts exclusively.

Therefore, for now at least, these methods cannot replace classical RNA-seq approaches in terms of quantification accuracy and completeness.

The future of these single cell high-throughput technologies is evolving [START_REF] Yuan | Challenges and emerging directions in single-cell analysis[END_REF], and many perspectives remain to be explored, such as in situ transcriptomic analysis [START_REF] Coskun | Dense transcript profiling in single cells by image correlation decoding[END_REF][START_REF] Satija | Spatial reconstruction of single-cell gene expression data[END_REF], live imaging transcriptomic analysis [START_REF] Skylaki | Challenges in long-term imaging and quantification of single-cell dynamics[END_REF], lineage tracing [START_REF] Mckenna | Wholeorganism lineage tracing by combinatorial and cumulative genome editing[END_REF][START_REF] Perli | Continuous genetic recording with self-targeting CRISPR-Cas in human cells[END_REF][START_REF] Schmidt | Quantitative Analysis of Synthetic Cell Lineage Tracing Using Nuclease Barcoding[END_REF] or single-cell multi-omics [START_REF] Cheow | Single-cell multimodal profiling reveals cellular epigenetic heterogeneity[END_REF][START_REF] Genshaft | Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction[END_REF][START_REF] Hou | Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas[END_REF]. The counting of UMIs, rather than measuring read density, then allows more precise quantification, by correcting for eventual PCR biases. Such protocols were designed for differential gene expression analysis and are particularly well-suited for single-cell RNA-seq analyses as well as low-input samples. (c) Currently, third-generation sequencing technologies are revolutionizing many fields of genomics, including transcriptomics. These emerging technologies indeed differ from previous one since they allow the direct sequencing of RNAs (i.e. with neither reverse transcription nor PCR amplification) and the generation of long-reads (i.e. sequences of several tens of kilobases in length). Consequently, they make it possible to sequence full length transcripts, therefore avoiding the tedious and equivocal step of transcript assembly. The relatively low throughput (in terms of number of sequences delivered per run) at a still relatively high cost, however, do not make yet such technologies an appropriate solution for quantification purposes.

Figures and Legends

Figure

  Figure 1. High-throughput sequencing technologies supporting transcriptomic analyses (2-column

1 .

 1 Figure 1. High-throughput sequencing technologies supporting transcriptomic analyses (2-column fitting, colors should be used). (a) Until recently, most sequencing technologies have relied on the generation of relatively short reads (i.e. sequences of few tens to hundreds of nucleotides in length). When applied to the sequencing of RNA, following reverse transcription (RT) of either poly-A enriched or of ribosomal RNA (rRNA)-depleted transcripts, the bioinformatic analysis then consists in mapping reads to a reference sequence (typically the genome if available) prior to transcript assembly (based on reads spanning exonic junctions) and/or quantification (based on density of reads mapping each individual transcript/gene). (b) Alternatively, modified library construction protocols involve the barcoding of each cDNA by a Unique Molecular Index (UMI) during reverse transcription, followed by sequencing of the 3' extremity of the resulting cDNA.

Figure 2 .

 2 Figure 2. Strategies to study gene expression during testis development and functions (2-column fitting, colors should be used). Several strategies are typically used to investigate the transcriptomic profiles of testicular cells, notably during germ cell development. (a) These include the use of whole testes at various developmental stages during the first wave of spermatogenesis in various species. In such approaches, the specific or preferential expression program of a given cell type is inferred from the changes in expression observed between two stages during which this cell type appears. (b) Based on the same rationale, the use of testicular biopsies from patients with spermatogenesis arrested at distinct developmental stages has been commonly used in humans. (c) Alternatively, many isolation procedures enable to obtain enriched cell

Acknowledgements

We thank Dr. Julien Dagher and Dr. Sadia Khan for critical reading of the manuscript. We also thank the 5

Research Institute for Environmental and Occupational Health (Irset), the National Institute of Health and Medical Research (Inserm), the University of Rennes 1 and the French School of Public Health (EHESP) for supporting this work.

Funding: This work was supported by the French National Institute of Health and Medical Research 10 (Inserm), the University of Rennes 1 and the French School of Public Health (EHESP).