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Int J Biol Sci 2023; 19(7):2167-2197. doi:10.7150/ijbs.82191 This issue Cite

Review

Application of single-cell RNA sequencing on human testicular samples: a comprehensive review

Fan Dong^1,2, Ping Ping^1,2, Yi Ma^1,2 , Xiang-Feng Chen^1,2,3

1. Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
2. Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China.
3. Shanghai Human Sperm Bank, Shanghai, China.

✉ Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: allanbaconcom. Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: my_julycom More

Abstract

So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.

Keywords: single-cell RNA sequencing, human testis, male gonad, spermatogenesis, bioinformatic analysis

Background

Human testis is the core organ of the male reproductive system [1], and acts as the cornerstone of male fertility since it is the place of spermatogenesis [2]. On the one hand, the testis is lionized by developmental biologists because it's the start of sex determination [3] and the kindergarten of male germline cells [4]. Meanwhile, human testis is also the priority of andrologists because testicular failure is the leading cause of male infertility [4]. Therefore, scientists have delved into both normal [5, 6] and abnormal testes [7-9], and detected mechanisms behind them especially those related to male infertility [10, 11]. Although numerous approaches have been used to learn about human testis including cell experiments [12], animal experiments [13] or even new methods like testicular organoid [14], most of the above-mentioned methodologies are indirect. It is no doubt that the most ideal model to study human testes is the testis itself. Therefore, human testicular biological sample-based studies could be important sources of knowledge of changes in both normal and impaired testes.

As early as the 1910s, human sample-based studies had started to give direct evidence of testicular diseases [15]. Yet until the 1970s, most of those studies were restricted to anatomy and morphology/histology [16-19] perhaps because at that time nonspecific histological staining was the most popular way to study human testes. Then with the development of some more specific methods such as immunohistochemistry staining (IHC), researches on the human testis reached protein levels [20-22]. A huge breakthrough in this field was the application of microarray and high-throughput RNA-seq on human testicular samples [23-26], which gave scientists a transcriptional view of both the normal testis's development and the pathogenesis of spermatogenic dysfunctions [27, 28]. Nevertheless, both microarray and traditional RNA-seq are based on bulk tissue, for which the testicular sample is considered as a whole, so we could only obtain the mainstream of transcriptional changes in the sample but ignore the heterogeneity of different cells. To solve this problem, Tang et al. introduced single-cell RNA sequencing (scRNA-seq) [29], which could detect the transcriptome at a single-cell resolution. The technology was created on blastomere and oocytes and soon applied to human testicular cells [30] and then to unsorted testicular tissues [31]. So far, there have been plenty of studies using human testicular scRNA-seq data and several reviews partially or roughly touched upon this topic (human testicular scRNA-seq analysis) [32-39]. Nevertheless, some of these reviews didn't fully focus on “human testis” and/or “scRNA-seq” (for example, some included much more about female gonad or animal testis [32, 34, 40] and some focused much on techniques other than scRNA-seq [33, 37, 38]), which led to limited paragraphs and blurry outline on this topic. Other reviews were almost restricted to germ cells [35, 36] or after-birth gonads [39], which gave an incomplete depiction of this topic. Most importantly, none of these articles was with systematic literature searching strategies, which might result in a biased interpretation. Using systematic literature searching, this comprehensive review plans to fully summarize the new findings offered by scRNA-seq analyses of human testicular biological samples, concerning both testicular cells and unsorted testicular tissues, both prenatal and postnatal male gonads, both underage and adult testes, both young and aged testes, both normal spermatogenic and impaired spermatogenic patients, and both original sequencing data and reusage of published data. We hope this review will give scientists detailed and all-round information on testicular development, spermatogenesis as well as testicular pathogenesis in the single-cell resolution, and humbly suggest the future directions in this field.

Methods

Search methods

The searches were conducted on 29 Aug 2022. First, a systematic search in PubMed database was conducted. Based on the fact that this review mainly focused on two aspects, including “scRNA” and “human testicular samples”, we used a matched searching strategy to link these two aspects. In detail, for the former aspect, the terms “single-cell” (term A) or “scRNA” (term B) were selected. And each of these two terms was combined with one of the following testis-related (the latter aspect-related) search terms, including “testis” (term 1), “spermatogenesis” (term 2), “Klinefelter Syndrome” (term 3), “azoospermia” (term 4), “Y chromosome microdeletion” (term 5), “cryptozoospermia” (term 6), “oligozoospermia” (term 7), “asthenospermia” (term 8), “teratospermia” (term 9), “orchitis” (term 10), “cryptorchidism” (term 11) and “male and gonad” (term 12). In order to minimize the chance of omitting related studies especially preprint studies, we do an extra search on GEO dataset (https://www.ncbi.nlm.nih.gov/gds) using four combination of advanced search terms including “((testis) AND scRNA) AND Homo sapiens [Organism]”, “((testis) AND single-cell) AND Homo sapiens[Organism]”, “(((male) AND gonad) AND scRNA) AND Homo sapiens[Organism]” and “(((male) AND gonad) AND single-cell) AND Homo sapiens[Organism]”, which also referred to the data related to both focused aspects. For filtering, the “series” of Entry type and “Expression profiling by high throughput sequencing” of Study type were also chosen. For each identified GEO series, its related articles were obtained from GEO or bioRxiv (https://www.biorxiv.org/) if there was no published article at the searching time.

Selection criteria

The inclusion criteria were (1) studies with single-cell RNA sequencing applied on human testicular biological samples (including testicular tissues, cells and prenatal male gonads) and/or (2) studies with re-analyses of published/public scRNA-seq data of human testicular biological samples (not including pure literature review of published articles).The exclusion criteria were (1) articles not in English; (2) articles published before 2009 (the year when scRNA-seq technology was created [41]); (3) non-research articles (such as pure review/case report/guideline/protocol/technical note/correction/comment/chapter of book/letter) without scRNA-seq analysis on human testis; (4) full-length articles not available; (5) tumor studies without scRNA-seq data analyzing or not relevant to human testis; (6) studies based on sequencing methods other than scRNA-seq (e.g. single-cell small RNA-seq or spatial transcriptomics sequencing); (7) pure animal or botanic studies; (8) human-related studies but with no scRNA-seq analysis on human testicular samples.

Results

Application of scRNA-seq on human testicular samples: an overview

The workflow of literature searching and selections was in Figure 1. The searches returned about 1300 records (609 after de-duplication). By further filtering, 81 full-length articles were enrolled into the final analysis. There were 33 articles containing original scRNA-seq data (at least from one donor) on human testicular samples, while the rest 48 articles were based on the re-analysis of previously published data (no matter whether the data was previously published by the same team). Since the year of 2015, Guo et al. for the first time do scRNA-seq on human fetal testicular cells [30], followed by adult testicular cells sequenced by two studies in 2017[42, 43]. Interestingly, although those two adult studies were from different teams, both of them focused on spermatogonia of one accord. From 2018, numerous studies have done scRNA-seq on human testicular samples regarding both prenatal and postnatal, both normal and abnormal testes. Table 1 summarized articles that have originally published scRNA-seq data on human testicular samples. We could conclude that most of the studies were conducted by scientists from the USA and China. As for library techniques, most of the studies used the 10×Genomics platform (https://www.10xgenomics.com/) to carry out their works. While other studies chose techniques/plateforms such as BD Rhapsody [44], (modified) smart-seq2 [45-47], STRT-seq [48], Microwell-seq [49], Drop-seq [50], inDrop [51], Slingleron GEXSCOPE^® [52, 53] and some earlier platform such as Fluidigm C1 [54] and Tang method [29, 41]. In some studies [55, 56], two or more methods simultaneously appeared for cell capturing. So far, no comparative study has focused on which technique is more suitable for scRNA-seq of human testicular samples. Nevertheless, some reviews and comparative studies based on scRNA-seq of other tissues have gradually revealed that the technological differences of scRNA-seq, including both the wet-lab parts (e.g., cell capturing/sequencing/barcoding) [44, 57-59] and the dry-lab parts (e.g., data-processing/batch-effect correction/dimension reduction/quality control) [60-64] might have profound impact on the final results. Especially, unavoidable technological batch effects will be generated using data generated from different sequencing platforms [65]. Although the data will go through batch effects correction before downstream analysis [60], we still recommend that researchers should choose one suitable techniques and consistent data-analyzing methods to do their testicular scRNA-seq works. If you need to do integrated analyses and have to combine your own scRNA-seq data with public scRNA-seq data, we suggest that you should choose the public data that used the same cell-capturing techniques as your own data, to minimize the non-biological batch effects.

In terms of sample types, one-third of the studies choose prenatal male gonads (the youngest sample was from the gonad from a 4-week male embryo). For adult testes (the oldest sample was from a 76-year-old male for now), most of the studies included samples with complete/full/normal spermatogenesis. But the sources of these normal spermatogenic samples varied. Some collected deceased healthy male testicular samples [31] to serve as the normal groups, which were ideal controls. For many institutes, obstructive azoospermia (OA) samples with full spermatogenesis confirmed by histopathological diagnosis, including congenital bilateral absence of the vas deferens [66], post-vasectomy [67], and physical obstruction of the vas deferens [66] were considered as workarounds for normal/control groups. It is noteworthy that in the original manuscripts of Hermann et al. [68], Tan et al. [69] and Sohni et al [67], some of the adult testicular biopsies were obtained from patients receiving vasectomy reversal, and the samples in Xia et al.'s work [70] were from testicular sperm extraction (TESE) surgeries of patients with obstructive infertility (they were also post-vasectomy patients after confirming with the first author). Interestingly, none of these patients was clearly claimed as OA patients, although they all had obvious obstructive etiology. Frankly speaking, these post-vasectomy patients were extremely unlikely to have sperm in ejaculate (only if some rare situations happened such as duplication of vas deferens or natural recanalization [71]). But in order to be strict and consistent, in the current review, all patients with obstructive etiology (no matter they were diagnosed with obstructive azoospermia or not) were also unitedly abbreviated as obstruction (OB). Besides, testes removed from patients receiving transsexual operation might also be an option for sample collection [43, 72]. Additionally, normal testicular samples at age 17 were collected by both Zhao's and Guo's studies, but were assigned to different groups (adult for Guo's and underage for Zhao's studies) [31, 55], maybe due to the different definition of adult (biological adult versus legal adult).

 Figure 1 

Workflow of the literature searching and study selection

Till now, the number of scRNA-seq testicular samples in each study has been limited, maybe due to the high price of scRNA-seq commercial service and limited sample donors. To partially solve this dilemma, some studies chose to do combined analyses of their own scRNA-seq data and public scRNA data or bulk RNA-seq data. And most of the studies in Table 1 did extra experiments (such as histological experiments, animal or cell experiments) to validate the findings from scRNA-seq analyses. Additionally, some studies were conducted on both testes and ovaries [22, 73], or on testes from both human and animals [68], in order to do comparisons between genders or among species. Garcia-Alonso et al did integrated analyses by the combination of testicular scRNA-seq with spatial transcriptomics [74], which is a novel direction. Nevertheless, so far there has been no guideline for scRNA-seq studies on the human testis. By reviewing all related studies, we summarized a standard guideline(workflow) of testicular scRNA-seq-based studies (Figure 2). We believe that with the wider application of scRNA-seq techniques, there will be more scRNA-seq-based studies of testicular samples published, with more comprehensive analyzing methods used.

 Figure 2 

Standard guideline/workflow of single-cell RNA sequencing studies on human testicular biological samples. Some of the elements used in this figure were downloaded from Icons of Microsoft PowerPoint 2021(Microsoft, USA). Note: scRNA-seq, single-cell RNA sequencing; FACS, fluorescence activated cell sorting; MACs, magnetic activated cell sorting; DEGs, differentially expressed genes; HE, hematoxylin-eosin; IHC, immunohistochemistry; IF, immunofluorescence.

Cell identifications by scRNA-seq on human testicular samples

The fundament of scRNA-seq analysis is cell identification. Apart from a few studies based on isolated cells by fluorescence activated cell sorting (FACS) or magnetic activated cell sorting (MACS), the majority of studies in Table 1 were based on testicular tissues. For adult testes, the representatively identified cell types in scRNA-seq analyses were summarized in Table 2. ScRNA-seq-identified testicular cells could be roughly divided into three classifications, including spermatogenic cells (germ cells), non-immune somatic cells and immune cells [66]. The widely used and some newly identified markers for cell identification were also summarized (Table 2). Importantly, because of the different study purposes and different sample selections, the types and subtypes of testicular cells in different studies were slightly variant. For germ cells, spermatogonia (SPG), spermatocyte (SPC) and spermatid could be broadly identified in a full-spermatogenic sample if the focus of the study is not spermatogenic cells [55]. Nevertheless, a more detailed classification of these three germ cells could be done based on stage-specific markers. Here we recommended a moderate classification of germ cells used by Nie et al., in which SPG could be further divided into spermatogonial stem cell (SSC) (also called undifferentiated SPG) and differentiating SPG (diff_SPG), SPC into early primary SPC and late primary SPC, spermatid into round and elongating/elongated spermatid [75]. One can adjust the subdivision of each germ cell type according to your research focus. For example, if you were not interested in the development of SPC, you could easily keep SPC as an unclassified type but classifying SPG and spermatid into more detailed subtypes [56]. If the whole spermatogenesis is your attention, you might choose a more aggressive strategy for germ cell classification [66, 76].

Apart from germ cells, it is well known that somatic cells also played important roles in spermatogenesis. Such somatic cells could be further divided into non-immune somatic cells and testicular immune cells. For non-immune somatic cells, Sertoli cells (SC) were one of the most important cell types in spermatogenesis, as they could not only form blood-testis barrier but also directly support germ cell survival [55]. And Leydig cells (LC) could affect spermatogenesis by producing testosterone [75]. Although the roles of testicular immune cells in spermatogenesis have not been fully revealed, more evidence indicated that they could interact with germ cells and take part in spermatogenesis [77].

The rules of cell identification in germ cells also worked when annotating somatic cells. For example, although the rest studies all keep testicular macrophages as a whole, Mahyari et al. chose to further divided them into M1 and M2 macrophages [78]. Nevertheless, the above-mentioned all classifications were “well-known” classification of adult testicular cells. One important function of scRNA-seq is to identify novel subtypes of cells. For example, traditionally SC were considered as one type of cells, but Zhao et al. and Guo et al. found three subtypes of SC in the human testis [55, 72]. Similar findings also occurred in LC [78]. Owing to the lack of unified understanding, sometimes similar novel subtypes of certain cells were independently reported by different teams with different nomenclatures. For instance, an important subtype of prenatal gonadal cells which could bifurcate into the developmental path of both SC and LC were reported by two different teams with different names [79, 80]. Meanwhile, samples with different pathological status might deeply influence the identified cell types and numbers. A case in point is testicular immune cells. In the microenvironment of testes with spermatogenic dysfunction, an increasing number of immune cells were observed [66]. Additionally, the main type of immune cells in full spermatogenic testes was macrophages, while a large number of mast cells were identified in testes from non-obstructive azoospermia (NOA) patients, especially in Sertoli cell-only syndrome (SCOS) [55, 56].

ScRNA-seq demonstrated the development and changes of testicular cells from all ages

Testicular development from prenatal to postnatal period

1. Overview

Before the year 2015 in which the first prenatal testicular sample was scRNA-seqed, the understanding of the development of different types of testicular cells in prenatal testes was limited to the changes of cell number, morphology and distribution [81]. So far, with over ten articles published their own scRNA-seq data on human prenatal testicular samples, we already have an outline of both germline and somatic development at single-cell transcriptional level.

2. Germline transition

For germline development pre- and postnatally, Guo et al pointed out that prenatal germs cells generally have two main forms of transcriptional profiling, including a form of primordial germ cells (PGC) (expressing PGC markers such as TFAP2C, KIT, NANOG, POUF51 and SOX17) and form of the most naïve SSC (expressing earliest SSC markers such as PIWIL4, EGR4, MSL3 and TSPAN33) [79]. PGC form mainly started at the embryotic time and then gradually stopped meiosis and inhibited pluripotency and at transcriptional level became cells with high similarity to the early status of adult SSC [79]. The latter form (named State f0) [79] strode across human birth and kept a quite similar transcriptional status until the adult time (named State 0 in adult) [31]. During the germline transition from PGC to (f)State 0, pathways related to gonad and stem cell development were downregulated while signaling related to transcription/homeobox were enhanced [79]. Similarly, Wang et al. found a transition from mitotic (mainly existing before 15 weeks) to meiotic arrest (mainly existing after 15 weeks) male fetal germ cells (FGC) [82], which was in line with Guo's [79] and Li's results [73], indicating that the downward status of meiosis (proliferative-quiescent-arrested) was the symbol of male fetal germ cell development. Garcia-Alonso et al. called the latter form of FGC prespermatogonia and also showed the activation of EGR4 in this stage [74]. Interestingly, Wang et al. also reported an extra subtype of FGC called SPARC⁺ POU5F1⁺ FGC, which might keep the ability to migrate locally within the fetal testis [82].

3. Somatic development

As for prenatal somatic cells, most of the researches focused on SC and LC due to the isogenesis of these two cells. The source of testicular somatic cells was embryotic coelomic epithelium (CE) [83]. By re-clustering of scRNA-seqed CE cells, Cheng et al. found two subclusters of CE, of which one was NR0B1⁺STAR⁺NR5A1⁺ but GATA4^-LHX9^- (annotated as adrenogenic CE), while the other was GATA4⁺LHX9⁺HOXA9⁺HOXD9⁺ but NR5A1^- (known as posterior/gonadogenic CE) [74, 80]. The former subcluster will develop into the adrenal gland while the latter goes to the testis. Noteworthily, gonadogenic CE will step into a sort of bipotent somatic progenitors (symbolized by the expression of NR5A1), which had been recognized by several studies [79, 80]. In Wang et al.'s study, a subpopulation of KRT19⁺ somatic cells were identified mainly in 6- to 8-week testes, which also resembled these somatic progenitors [82]. Starting from 7-8 weeks, these somatic progenitors bifurcated into two different developmental paths, one of which is embryonic Sertoli progenitors (marked by SRY, similar to the “early supporting gonadal cells” in Garcia-Alonso et al.'s work [74]) and then became fetal SC [79]. The rest path, on the contrary, led to embryonic/fetal interstitial progenitors (marked by ARX and TCF21, also called Leydig precursor cells by Li et al. [73] and called somatic progenitor by Wang et al. [84]), and finally part of the fetal interstitial progenitors became fetal Leydig cells (marked by HSD3B2 and CYP17A1, also called differentiated LC by Li et al. [73]) [79, 80]. During the development of prenatal interstitial/Leydig lineage, the enriched molecular pathways altered from “extracellular matrix”/ “cell adhesion” to “steroid biosynthesis”. Interestingly, at the perinatal stage, fetal SC successfully passed the birth (with genes related to translation and respiratory chain enhanced, while genes related to the endoplasmic reticulum and steroid biosynthesis reduced) and became postnatal SC including stage_a and b (discussed in more detail below) [55, 79], while fetal Leydig cells disappeared in the postnatal time [79]. In fact, it was CYP17A1-negative fetal interstitial cells that might finally pass the birth and finally become the common progenitors of adult LC and PTM [79]. Regarding EC, although some studies proved the early appearance of EC in prenatal male gonad [73, 84], so far there have been few studies that focused on EC development. Like the previous murine studies [85, 86], scRNA-seq data on human the fetal testis also revealed two distinct types of macrophages in the fetal testis, of which one is named SIGLEC15⁺ fetal testicular macrophages (osteoclast-like) and the other is TREM2⁺ fetal testicular macrophages (microglia-like) [74]. Further study showed that SIGLEC15⁺ macrophages were usually located in the interstitium and could interact with EC/mesenchymal cells and play roles in mesonephric endothelial cell migration, while TREM2⁺ macrophages (located in the testis cord) interacted with fetal SC and FGC and worked as an immune regulator [74]. Unfortunately, the transition from fetal testicular macrophages to postnatal testicular macrophages remained unexplored. Further scRNA-seq-based studies might have the opportunity to reveal the relationship between fetal and infant testicular macrophages.

After-birth testicular maturation from underage to adult

1. Overview

Among all scRNA-seq studies on testicular samples, several studies did original scRNA-seq on underage testicular samples [31, 55, 67, 72, 79, 87]. And in some other studies [78, 88], previously published underage testicular scRNA-seq data were re-analyzed. Those studies pointed out that huge heterogeneity existed in both spermatogenic cells and somatic cells during the maturation of testes after birth.

2. Spermatogenic maturation

For analyzing the after-birth development of spermatogenic cells, pseudotime trajectory analysis was widely used. In fact, pseudotime trajectory analysis on spermatogenic cells could be divided into three distinct types. First, if we only focused on the normal testis of an adult man, spermatogenesis itself, starting from SSCs and ending at mature spermatids, was a continuous process running along a time axis of maturation [89]. Hence, the complete spermatogenesis process could be analyzed using pseudotime trajectory in scRNA-seq data. In 2018, Guo et al. did this task and showed there was only one no-branch pseudotime trajectory in spermatogenesis of healthy male [31], which started with SSCs, passed diff_SPGs, early and late SPCs, then round and elongated spermatids and ended up with testicular sperms (also known as mature spermatids [90]). The second type of pseudotime trajectory analysis on spermatogenic cells is to extract and re-cluster a certain type of germ cells and do pseudotime trajectory analysis on this type of germ cells. For instance, Sohni et al. did pseudotime trajectory analysis on SPGs and found a minor group of SPGs called “transitional cells” which was the linkage between SSCs and diff_SPGs during SPGs differentiation [67]. Besides, Guo et al. did pseudotime analysis on SPGs and reported a five-stage development of SPG, starting from state 0 (markered by PIWIL4, similar to the above-mentioned state f0 in the fetal testis), then state 1 (ID4, GFRA1 expressed), followed by state 2-3 (marked by KIT and MKI67) and finally reaching state 4 (marked by STRA8) [31, 42]. By comparing these 5 stages with the results from Wang et al. [76] and Sohni et al [67]., we could conclude that state 0 and 1 were two types of SSC (0 was more naïve and fetal-like, and resembled the “SSC-1” in Sohni's work, while 1 resembled “SSC-2” in Sohni's work), and state 2-3 were diff_SPG while state 4 were diffed_SPG. Third, pseudotime analysis could also be used to study the maturation of germ cells from underage to adult. In fact, the discovery of state 0 was based on pseudotime analysis on the combination of infant and adult spermatogenic cells, from which they found this special state of SPGs (which was close to the infant germ cell cluster) in adult [31]. Sohni et al. further re-clustered infant germ cell cluster into three subclusters, including primordial germ cells (PGCs)-like cells (PGCLs) (marked by POU5F1 and NANOG), Pre-SPG1 (marked by DOCK8 and SERINC2) and Pre-SPG2 (marked by COL1A2 and TIMP2) [67, 91]. And further pseutotime trajectory showed that the developmental order of fetal/infant/adult naïve germ cells was PGCL→Pre-SPG1→Pre-SPG2→adult SSC subcluster (SSC-1) [67]. During puberty, the spermatogenic status changed obviously with age, and pseutotime trajectory analysis of a series of testicular scRNA-seq data from 1- to 25-years old males showed the dynamic changes of germ cells at different ages [72]. In detail, during the infant and child period (1 and 7 years old), there were only SSC in testes (also known as undifferentiated SPG or SPG state 0 and 1, which all referred to the naïve status of SPG) [72], and this finding was further validated by scRNA-seq data on testes from 2- and 5- years old males [55]. At year 11, both diff_SPG and SPC appeared, while spermatid appeared at 13 years old samples [72]. Zhao et al. further showed that diff_SPG had already existed in the 8-year-old testis but SPC was still absent at that time [55]. The first maturity of spermatogenesis was observed at 14 years old, by which time the proportion of each type of spermatogenic cells was almost the same as adult testes [72]. In terms of molecular signaling, Activin pathway was downregulated in SSC but was obviously upregulated during spermatogonia differentiation [72]. In a word, scRNA-seq together with pseutotime trajectory analysis is very powerful in analyzing the after-birth maturation of spermatogenic cells, especially in the identification of new cell subclusters and finding new stages of cells.

3. Somatic maturation

For somatic cells, the development of Sertoli cells has been fully analyzed by scRNA-seq analysis. By integratedly analyzing testicular scRNA-seq data of five young adult with normal spermatogenesis (age from 23 to 31 years), three children (age 2, 5 and 8 years) as well as two adolescents (age 11 and 17 years), Zhao et al. revealed that the normal maturation of testicular Sertoli cells consisted of three stages (termed as stage_a, b and c) [55]. In detail, stage_a (marked by JUM) was the majority of Sertoli cells at infant time (including the neonatal testis) and gradually decreased with the age increasing. While stage_c (marked by DEFB119) appeared at the time of puberty (after 11 years old) and became the dominant type of Sertoli cells in adult people. Besides, they also found that the Wnt signaling pathway should be the key of regulating Sertoli cell maturation [55]. Such findings are outstanding because it focused on the normal development of postnatal somatic cells and revealed not only the phenotype development of Sertoli cells but their related molecular mechanisms as well. Similarly, Guo et al. also found two immature stages of SC, named immature 1# (marked by PDPN) and 2# (with higher expression of TOMM7 and ATP5E), which finally merged into mature SC after 11 years old [72]. By comparing the differentially expressed genes (DEGs) of stage_a/b SC (from Zhao's study) versus those of immature 1#/2# SC (from Guo's study), we concluded that stage_a was similar to immature 1# while stage_b was closer to immature 2#. A huge difference between these two studies is that Guo's study showed 1# and 2# as two independent paths of immature SC, which will merge into mature SC after 11 years old. Zhao's study, however, showed a potential sequence of stage_a, b and c, (from a to be to c) by both analyzing the proportion of these three cells at different ages and analyzing the functions of these three cells. In detail, they found that before the appearance of mature SC, the ratio of SC a to SC b decreased with age, suggesting there might be a transition from a to b [55]. But was this change of ratio caused by “transition from a to b” or by “proliferation of b/death of a”? Enrichment analysis and cycle-specific gene analysis further showed that stage a SC were the most proliferative and had more features as progenitors, which supported the hypothesis that stage a, b and c were three consecutive development stage of SC [55]. Since Zhao's study was more comprehensive in terms of SC, hereinafter we chose the nomenclature of SC from Zhao's study [55].

Moreover, Mahyari et al. found that in adult human testes, the maturation of Leydig cells also consisted of three types including progenitor LCs (PLCs), immature LCs (ILCs) and mature LCs (MLCs) [78]. And pseudotime analysis confirmed that the maturation of LCs started from PLCs, followed by ILCs and ended at MLCs [78]. However, in underage testes, the LCs seemed to be a unique type which was totally separated from the above-mentioned three adult LC types [78]. In line with that finding, using pseudotime analysis on both infant and adult LCs, another study also found that infant LCs and adult LCs appeared at two different ends of the trajectory, indicating that the progress of LCs from underage to adult might be a discontinuous alteration [67]. Further, Guo et al. for the first time showed that the so-called “infant or child LCs” might actually be the common progenitors of LC and PTM [72], which will finally develop into both LC and PTM during puberty [72]. As for PTMs, although the developmental trend was similar to LCs (because they were homologous), pseudotime analysis of scRNA-seq data identified a special subtype of adult PTMs which was clustered together with neonatal PTMs and showed a neonatal-like gene expression characteristic [67]. For the molecular signaling involved in the maturation of LC and PTM, Guo et al. found that the common progenitors expressed high level of genes associated to “transcription” signaling, and the enriched pathways went to cytoskeleton/ cell adhesion signaling (if the progenitor differentiated into PTM) or secretion signaling (when it differentiated into LC) [72]. So far, the development and maturation of other types of somatic cells, especially testicular ECs and immune cells, have not been fully revealed. Future scRNA-seq studies should pay special attention to the differences of these rare somatic cells between underage and adult human testes.

Based on the published studies, we can conclude that during the maturation of after-birth testes, huge differences existed in both somatic cells and germ cells between underage and adult testis in terms of cell subtypes, expressed markers and regulating mechanisms.

Testicular senescence from adult to aged males

1. Overview

Unlike the deep studies on differences between underage and adult spermatogenesis, few scRNA-seq researches focused on the changes in aging testes. Until last May, Nie et al. published the world's first testicular scRNA-seq study based on the comparison between young and old adult men [75]. In this study, by integrated analysis of 4 young adults (age 17-22 years) and 8 older adults (age 62-76 years), this team found huge age-related changes in both spermatogenic cells and somatic cells.

2. Germ cell senescence

In terms of germ cells, they found that spermatogenic cells were affected by age in an inconsistent way (some old people still have complete spermatogenesis, while others showed impaired spermatogenesis), and in old males with obviously impaired spermatogenesis, the decreased number of SSC might be one of the causes of age-related spermatogenic dysfunction [75]. Mechanistically, using Cellchat analysis, a powerful function of scRNA-seq data to detect cell-cell communications [92], Nie et al found that Activin and KIT signaling pathways weakened between SCs and SSC/diff_SPGs, which might lead to damaging effects on germ cells [75].

3. Somatic cell senescence

For somatic cells, scRNA-seq analyses revealed pan-somatic cell changes, including inflammation changes of SCs, loss and testosterone decline of LCs, decreased contraction and integrity of PTMs-based tubular walls and the senility of both ECs and Macrophages [75]. Mechanistically, Nie found that for SCs, metabolic signaling was significantly altered in aged males, while in senescent LCs, key components of the Hedgehog pathway were decreased [75].

4. Young infertile testes showing onsets of senescent process

For some infertile males who were still young, Alfano et al. found that their testes showed early onsets of senescent process [88]. Such abnormal senescence was observed in almost all somatic cells of idiopathic germ cell aplasia, and was characterized by the upregulation of proteins of innate immunity, the overexpression of UBA52/NACA and decreased pathways of amino acids metabolism [88]. This phenomenon suggested that the senescence of human testes was not only associated to the exact age of male, but affected by their pathological status as well.

Transitions of SC/LC/PTM in the context of the whole life cycle

So far, there has been limited scRNA-seq study that focused on the all-age process of the origin, development, maturation and senescence of a certain type of testicular cells by analyzing prenatal, infant, children, adolescent, adult and aged testicular samples. Due to the profound studies on the transitions of SC/LC/PTM and the low inconsistency among these studies, we tried to summarize the development and changes of these three main somatic cells in the context of the whole life cycle (from embryotic to aged) in Figure 3, based on the current evidence retrieved from human testicular scRNA-seq studies [55, 67, 72-75, 78-80]. Future studies should try to integrate the transcriptional phenotypes of testicular cells from all periods of the life cycle, and do a coherent study on the other types of testicular cells, especially testicular immune cells, to which less attention was paid.

ScRNA-seq mapped the testicular microenvironment in infertile males but with complete spermatogenesis

Obstructive azoospermia

Obstructive azoospermia was clinically diagnosed by semen tests (no sperm in ejaculates) and the existence of excurrent duct obstruction [93]. According to traditional opinion, OA patients were with rather conserved spermatogenesis [94]. Therefore, in traditional studies, due to the difficulty in obtaining completely healthy and fertile male testicular samples [95, 96], researchers would like to choose OA samples with confirmed normal histopathologic features as the controls [97-100]. In testicular scRNA-seq studies, this workaround was also widely used [55, 66, 76]. Although we could conclude that these OA control samples were with complete or full spermatogenesis because of the histopathologic confirmation, could these OA testes strictly be called as “normal” testes? In the past, due to the limitation of research methods, this question was not fully answered. By the combination of healthy and OA testicular scRNA-seq data, Chen et al. revealed an obvious loss of late spermatids and an increase of early SPCs in OA testes compared with testes from the healthy man [56]. Mechanistically, the changes were found in both germ cells and somatic cells. First, SPCs of OA testes might suffer from the weakening meiotic process and enhanced apoptosis, which eventually leading to the loss of spermatids [56]. As for somatic cells, PTMs might be the matchmaker between obstruction-caused pressure and the damage of spermatogenesis [56]. This study partially solved the unsettled issue and suggested that although the spermatogenesis of OA testes was functionally and pathologically normal, it was not technically normal. Meanwhile, this study only used scRNA-seq data of post-epididymitis and post-vasectomy OA testes. Is there a difference in testicular function among OA patients with various causes (e.g., congenital bilateral absence of the vas deferens and post-vasectomy)? Whether the changes of OA testes found by scRNA-seq are obstructive time-dependent? These questions remain to be answered.

Ejaculation dysfunction

Ejaculation dysfunction contains a wide range of situations in which the normal ejaculation was disordered or even failed [101]. Infertility caused by ejaculatory dysfunction or retrograde ejaculation could also be accompanied by testes with complete/full spermatogenesis. Using testicular scRNA-seq data of a patient with retrograde ejaculation (post-colectomy), Mahyari et al. pointed out that the characteristics of cell score residuals (a statistic reflecting testicular cells' pattern) of the retrograde ejaculation sample and adult control samples were alike, which indicating that the patient with retrograde ejaculation had undisturbed spermatogenic status and could be considered as another type of control samples [78]. Nevertheless, the above-mentioned scRNA-seq study did not especially focus on ejaculation dysfunction, so the evaluation of testicular changes of ejaculation dysfunction was not that comprehensive. Further scRNA-seq studies should take several situations of ejaculation dysfunction, including “total anejaculation”, “situational anejaculation” [102] and “retrograde ejaculation”, into consideration, in order to profoundly reveal the potential alterations caused by ejaculation dysfunction.

 Figure 3 

A model map of the potential developing paths of Sertoli cells/Leydig cells/Peritubular myoid cells in the whole life cycle, together with changing cell functions/pathways and highly expressed markers. The information of this diagram was retrieved from studies listed in Table 1, especially from [55, 67, 72-75, 78-80]. Icons of various cells in this figure were downloaded from Servier Medical Art (https://smart.servier.com/) or redrawn based on the elements of Servier Medical Art. EMT, epithelial-mesenchymal transition; ECM, extracellular matrix.

ScRNA-seq revealed cellular and molecular disorders in patients with spermatogenic dysfunctions

Non-obstructive azoospermia

Spermatogenic dysfunction was characterized by the impairment of intratesticular spermatogenesis, and NOA is the most severe condition of it [103]. NOA was clinically diagnosed with no sperm in ejaculates along with evidence reflecting/causing the impairment of testicular spermatogenesis (e.g., elevated FSH or history of chemotherapy) [104]. Maybe due to the relatively easy availability of NOA testicular samples (a large number of NOA patients need testicular sperm retrieval surgeries [105, 106]), so far there have been seven studies that did their own scRNA-seq on at least one NOA testicular sample (Table 3). It should be noted that in this part (Table 3) we only discussed NOA samples without evidence of chromosome abnormality (NOA patients with chromosome abnormality will be discussed separately). Among these studies, over 70% (five) were conducted by Chinese research teams. Interestingly, due to the heterogeneity of NOA, the pathological status of scRNA-seq NOA samples used in each study varied across studies. Besides, maybe due to the decrease or lack of germ cells in the NOA testis, most of these studies focused on changes in somatic cells, especially SCs and LCs. Notably, most of the scRNA-seq studies did analyses on NOA samples in the following two ways: 1) identifying DEGs (in a certain type of cells) between control and NOA testes, and 2) finding new/major subtypes of a certain cell in NOA. For instance, Zhao et al. found that in iNOA patients, Sertoli cells were basically in stage a and stage b (mentioned above), and they revealed iNOA SCs showed different gene expressions pattern compared with normal testes [55], which were independently confirmed by Wang et al. [76] and Chen et al. [56]. In terms of somatic cells, two main somatic cells of the human testis, SCs and LCs, showed similar status in NOA patients, both were immature but with enhanced proliferative/divisive ability [55, 78], which were in line with the fact that immature somatic cells have proliferative ability [107, 108]. Alfano et al. further found that in SCOS (the severe pathology of NOA), the transcriptional and phenotypic features of LC were more like pre-pubertal LC, indicating the repressed maturation of LC in NOA [88]. As for germ cells in the NOA sample (if it had germ cells), Cst3 mediated autophagy might play an important role in the development of NOA by harming SSC maintenance (the ability of SSC to keep its population by self-renewal [109]), and knockdown of this gene might cause the impairment of SSC which might further lead to spermatogenic dysfunctions [110].

Unfortunately, maybe due to the reduction or absence of germ cells in the NOA testis, most of these studies merely focused on somatic cells, which made the changes of residual spermatogenic cells in NOA patients largely unknown. Future studies could especially select samples with spermatogenic arrest or hypospermatogenesis to do scRNA-seq, which will give a chance to solve this problem.

Cryptozoospermia

In the area of Oligo-astheno-teratozoospermia (OAT), so far there have been few scRNA-seq data reported. This phenomenon is easy to understand because the scRNA-seq data was based on testicular tissue which is usually dissected in sperm retrieval surgeries such as TESE or microdissection TESE (mTESE) [111], and for OAT patients, many of them could use ejaculated sperm to do in vitro fertilization (IVF). Only severe OAT patients might need to undergo testicular sperm retrieval surgeries [112]. Cryptozoospermia, which was characterized by the appearance of isolated sperms in semen under the microscopic examination after centrifuging [106], could be considered as the transitional situation between severe oligozoospermia and azoospermia. The previous study pointed out that for cryptozoospermia patients, surgically retrieved testicular sperm was better than ejaculated sperm when doing intracytoplasmic sperm injection (ICSI) [113], which could also provide more convenience for obtaining testicular samples to do scRNA-seq. Using three cryptozoospermia testicular samples versus three controls (OA samples), Persio et al. profoundly revealed the extensive changes of testicular microenvironment in cryptozoospermia patients [66]. Deeper changes occurred in spermatogenic cells, including the reduction of germ cells such as pachytene spermatocytes and beyond, the changes of transcriptional profiles of all-type spermatogenic cells, an increase of PIWIL4 positive spermatogonia (representing the most naïve germ cells [114], just like the above-mentioned state 0 SPG or SSC-1), and a decrease of A_dark SPGs (reserved SSCs [115]) and UTF1 positive SPGs (a SSC/undifferentiated SPG marker [116]) [66]. More importantly, an enhanced and prolonged expression of EGR4 was noticed in SPGs (also resembled the SSC-1 cells in Sohni's study [67]), which could act as a transcriptional factor and inhibit the expression of UTF1 in part of SPGs from cryptozoospermia patients [66]. Besides, scRNA-seq data also revealed perturbed somatic cells in cryptozoospermia, including an obvious increase of PTMs (including fibrotic PTMs) and macrophages, a deep transcriptional change of PTMs, an elevated ratio of MUSTN1 postitive (a pan-musculoskeletal cell marker [117]) blood vessels and the gathering of CD3 T cells around blood vessels [66]. Besides, two crosstalk networks between SPGs and adjacent microenvironments including “SPGs-FGFR1/3-FGF2-pachytene/diplotene SPCs” and “SPGs-ACKR2-CCL2/3/4/5/3L1/14-EC/immune cells/perivascular cells” were detected in cryptozoospermia rather than normal patients [66].

Trans-females after gender-affirming hormone therapy

The testis removed from patients receiving sexual reassignment surgery was another important tissue source for scRNA-seq studies [43, 72]. Of course, if a physically healthy patient did not receive any medical treatment before transsexual surgery, the removed testis was definitely an ideal sample of the “normal” group. Unfortunately, many transgender individuals received gender-affirming hormone therapy [118], which might profoundly impair the spermatogenesis of their testes [119]. Guo et al. drew the single-cell transcriptional profiles of two trans-females who received long-term (over a year) testosterone antagonist and estradiol treatment before transsexual surgeries [72], and found huge disorders in both germ cells and somatic cells. In terms of germ cells, although they still existed in both donors, the proportions of advanced spermatogenic cells including spermatids, SPC and diff_SPG were extremely low or even absent, and such changes stopped at the level of undifferentiated SPG (or SSC, or state 0 & 1 SPG as mentioned above) [72], indicating that testosterone inhibition might have less impact on SSC itself at the transcriptional level but could stop its differentiation. We found that such status was similar to the testis of prepuberty boys, for whom diff_SPG and more developed germ cells did not appear before 8 years old [55]. As for somatic cells, SC was found to be immature with higher expression of AMH and HES1 in these two patients, and the transcriptional profiles of SC were more like pubertal males (stage_b SC was predominant during this period [55]) rather than adults [72].

ScRNA-seq detected impairment of testicular somatic cells in patients with chromosome abnormalities

Klinefelter syndrome

Klinefelter syndrome (KS) makes up the most common part of male chromosome disorders [120], which is also widely studied by scRNA-seq studies [55, 78, 121]. Importantly, usually the studies on KS patients were considered as a part of works on NOA patients because of the popular azoospermic phenotype [122]. Nevertheless, due to the special karyotype of KS (one or more extra X [123]), the changes and relative mechanisms in KS testes were distinguishable from the above-mentioned iNOA patients [124]. Interestingly, for scRNA-seq data of KS testicular samples with the presence of germ cells, there seemed to be no huge changes at the transcriptional level of KS germ cells compared with normal germ cells [121]. But such a conclusion was drawn indirectly based on the co-clustering of KS germ cells with normal germ cells, rather than differential expression analysis (because there was only one KS sample sequenced with only 39 germ cells identified in Laurentino's study [121]). Unlike the other type of male infertility (in which the scientist could deeply analyze germ cell changes), Laurentino's study was almost the “best” work of KS germ cells because most of the scRNA-seq samples on KS from other teams lacked spermatogenic cells and showed a SCOS or even tubular atrophy pattern [55, 78]. Although previous study pointed out that spermatogenic cells were not entirely lost among all KS patients [125], we found it was hard to accurately get one sample with germ cells to do scRNA-seq. According to our experience, for KS patients, if the sample was coming from a random biopsy, we can't guarantee there were germ cells in it. And if the sample was coming from TESE or mTESE surgery (which we could judge the spermatogenic status by the form of tubules during surgeries), although it was possible for us to find a sample that contains germ cells or even spermatozoa, most of such “good” samples should be used for finding sperms and ICSI (since such good samples were quite rare and precious), so it was unlikely to have an extra “good” sample for scientists to do further studies including scRNA-seq. By combining scRNA-seq with bulk RNA-seq data and IHC staining, Winge et al. found that such germ cell loss in KS patients might start as early as in the fetal testis [126]. And another study found that KIF2C might be a germ-cell-related gene to regulate spermatogenic development in KS testes [127]. Frankly, based on the current evidence from scRNA-seq data, it's too early to answer the question that what was the transcriptional change in the residual germ cells of KS patients.

As for KS somatic cells, scRNA-seq data has offered us abundant findings which we didn't know before. SCs were found to be the most affected somatic cells in KS testes [126]. In detail, KS SCs were almost stage_b SCs and showed altered energy metabolism characteristics such as enhanced expression level of glycolysis and oxidative phosphorylation-related genes but rather low triglyceride metabolism level [55]. Huge transcriptional changes were observed in KS SCs including the upregulation of immune-related genes such as B2M [128] and MIF [129], the decreasing of sex hormone -regulating gene GNRH1 and the increasing expression of X-linked genes [55, 78]. Notably, Mahyari et al. found a special subpopulation of KS SCs which showed lacking expression of XIST, which could partially explain the X-linked genes' enhancement in SCs [78]. The involvement of immune activation in KS testes found by Zhao et al. [55] were also validated in peripheral blood of KS patients [130], indicating the profound impact of immune-related pathogenic factors in the pathogenesis of KS. Similarly, the proportion of immature LCs was also increased in KS LC population and more LCs were found to undergo division/development (with higher expression level of RNA processing/splicing-regulating genes such as CCNL1, DDX17, PNISR and FUS) [78]. Interestingly, when comparing KS-NOA somatic cells with iNOA somatic cells, both shared and heterogenous features were found. For example, the expression of β-catenin protein level was both significantly enhanced in KS-NOA and iNOA SCs, indicating a shared pattern of activated Wnt/β-catenin pathways in both type of NOA compared with normal adults [55]. While SERPINE1, a tissue plasminogen blocker, was highly expressed in KS immature LCs but not in iNOA LCs, indicating the heterogenous pathogenesis of KS-NOA compared to NOA with other etiology [78].

Y chromosome microdeletion

The testicular evidence got from single-cell studies on patients with Y chromosome microdeletion was limited and less than the evidence from bulk studies [131, 132], especially for AZFc microdeletion (the most common form of Y chromosome microdeletion in male infertility [133]). So far, there has been only one scRNA-seq testicular sample with azoospermia factor a (AZFa) deletion reported [55]. Unfortunately, this sample was with SCOS pathology, so we can't gather enough data on germ cells of patients with Y chromosome microdeletion. Pseudotime trajectory analysis in the AZFa testis found its SCs were in the early part of stage_c (a form of mature SCs), together with upregulation of MIF and DEFB119 (two important factors in immunoregulation), suggesting the involvement of immune regulating pathways in the etiology of AZFa SCs [55]. Considering the limited research on this field, we suggest future scRNA-seq studies should focus on the pathogenesis of azoospermia factor b (AZFb) and azoospermia factor c (AZFc) testes (especially those AZFc testes with germ cells), so as to profoundly reveal the special changes in patients with Y chromosome microdeletions.

Re-analysis of published scRNA-seq data on human testicular samples met diversified scientific requests

Overview

With more and more scRNA-seq data on human testicular samples published, so far there have been plenty of studies that chose to re-analyze those published data in various aspects. Table 4 summarized the studies that re-analyzed the previously reported scRNA-seq data on human testicular samples. A special case is Chen et al.'s spatial transcriptomic work on testes [134]. In that Slide-seq-based study, the authors only employed published scRNA-seq data as a reference for cell-type assignment, and as an input for pseudotime value assignment, which (strictly speaking) were not the real “re-analyses” of scRNA-seq data itself. Therefore, we chose to exclude that study from Table 4. Among the reviewed 48 studies in Table 4, the majority of them combined the human testicular scRNA-seq data with other types of scRNA-seq data or bulk RNA-seq/microarray data in order to get integrated results, and most of them chose to experiments such as IHC/immunofluorescence (IF) staining on testicular sections to validate their findings. Hence, in Table 4 we also offered the important public data (other than human testicular scRNA-seq data) that the study used and listed the experiments/clinical studies or sequencing works conducted on their own. Although those studies had various designs and different findings, we still found universalities among these studies and categorized them into several categories. The detailed classifications as well as representative studies were discussed hereinafter, and many of the studies were with multi-categories.

Detecting the transcriptional pattern of interested genes in human testes

As a transcriptome-level technology, the fundamental usage of scRNA-seq was to detect genes' transcriptional patterns such as the mRNA expression level of a certain gene in different types of cells. A typical study was carried out by Yang et al. [135], in which they first found a new variant of KASH5 by Whole Genome Sequencing (WGS) in an NOA patient, but did not know the detailed KASH5 expression pattern in the human testis. So, they chose scRNA-seq data (the data used here were previously reported by the same team) to see the expression of KASH5 and found it to be enriched in SPCs, especially leptotene to pachytene SPCs, and finally validated it by IF staining. This process of “finding target gene” and then “detecting it in human testicular scRNAs-seq data” (sometimes with a third step as “validating genes using experiments”) was quite common for studies with interested genes [136-141]. Meanwhile, the interested genes were not restricted to one gene or several genes, the researchers could detect a gene family (such as Interleukins) or a series of function-related genes (such as inflammatory cytokine receptors) in scRNA-seq data [142], which could give researchers a view on macroscale. Moreover, an updated usage of this process was to not only show the type of cells that a certain gene was expressed in, but also show its transcriptional changes during spermatogenesis [143] or among different pathological groups [127, 144]. A good example came from He et al.'s work [127], in which they identified four interested genes (KIF2C, MRPS2, RPS15 and TSFM), and then based on human scRNA-seq data of both KS testes and normal testes, they showed that KIF2C were downregulated while RPS15 were upregulated in KS testes. Additionally, pseudotime trajectories showed a growing tendency of KIF2C and a downtrend of RPS15 with the maturation of spermatogenic cells [127]. We need to point out that no matter what method the study used and no matter how complicated the study procedure was, the nature and essence of this type of re-analyzing were to show the expression of given genes in human testes, no matter how these interested genes were found out (by other technologies/experiments, by bulk RNA-seq/microarray data, by scRNA-seq data of other organs/species, by literature reviews or even by the authors' own idea).

Indirectly revealing the vulnerability of testes to SARS-CoV-2 infection

Due to the Corona Virus Disease 2019 (COVID-19) pandemic and the high proportion of SARS-CoV-2-related articles in Table 4 (10 out of 48), we especially discussed the usage of human testicular scRNA-seq data here. As for the usage of testicular scRNA-seq data, most of these studies were actually in the form of “detecting the transcriptional pattern of interested genes in human testes” [145-154], since several important proteins, such as ACE2 [155], TMPRSS2 [156], Furin [157] and CD147 [158], were deeply involved in the entry of SARS-CoV-2. So far, the opinions on this topic have been controversial. In some studies, ACE2 was reported to be expressed in both germ cells (especially SSCs&SPGs) and somatic cells, including SCs, LCs as well as myoid cells [145, 147, 149, 154]. And one study showed a higher positive rate of ACE2 in testes from infertile patients (OA and NOA) [148]. Those results indicated the potential vulnerability of human testes to SARS-CoV-2 infection, especially in infertile men. And some studies even called the human testis a “high-risk organ” for SARS-CoV-2 infection [152]. But based on the scRNA-seq-derived evidence that there was no co-expression of ACE2 and TMPRSS2 in the human testis, other two studies gave the opposite view that human testes might not be able to be long-term affected by SARS-CoV-2 infection [146, 151]. From our point of view, those ten scRNA-seq-based studies only gave indirect evidence based on previously published data. By combing original and published scRNA-seq data on human testes, Liu et al. partially overcame the shortcomings of those studies and for the first time showed an expression of ACE2 in primordial germ cells of prenatal testes and a downward trend of ACE2 with the age increasing [159]. More importantly, although ACE2 had the highest expression in SCs, it significantly shrank in SCs of NOA patients [159]. But the results of Liu's study were still based on scRNA-seq of patients without histories of COVID-19, which were also indirect. So far, there has been no report of scRNA-seq data on human testes from COVID-19 recovered patients, maybe due to the huge difficulty of sample obtaining. But with the pandemic continuing, there must be patients who simultaneously suffered from COVID-19 and male infertility and needed testicular biopsy or TESE surgery. Therefore, in the future, researchers can use scRNA-seq to compare testicular samples from COVID-19-recovered patients with those from non-COVID-19 patients, which will offer direct evidence of the impact of SARS-CoV-2 on human testes.

Finding novel genes involved in normal testicular function and male infertility

Testicular scRNA-seq data could not only be used to draw expression patterns of certain genes in the testis, but could directly be used as the sources of such novel genes. The difference between this type of usage and the type “Detecting the transcriptional pattern of interested genes in human testes” is that this type of study directly used testicular scRNA-seq data as a tool to find novel genes, while the other one only used scRNA-seq data as a tool of validation or display. For example, by comparing the DEGs in SPG between OA and NOA scRNA-seq data, Yang et al. reported a novel SPG-specific gene ELAVL2 and found it to be significantly downregulated in SPGs of NOA patients [160]. Further experiments proved this gene as an important factor in regulating SSC maintenance and apoptosis [160]. Like that study, scientists could use testicular scRNA-seq data as a tool to reveal functional genes that may be related to the development of testicular diseases [161-164]. We recommend that this type of usage should be “cell-oriented”, which means the researchers should focus on a certain type of testicular cells and try to dig up novel genes or mechanisms of this type of cells, rather than just comparing the differences between disease and control groups without considering cell heterogeneity (in other words don't use scRNA-seq data to do tasks that bulk RNA-seq can do).

Comparing conserved and species- or sex-specific transcriptional features

Another important type of re-analyses of human testicular scRNA-seq data is to do species or sex comparisons. For species comparison, so far, the spermatogenesis of mouse [165], mouse lemur [165], chicken [166], cynomolgus macaque [167], sheep [168] and the somatic development of pigs [169] has been fully compared with that of human. The similarities of these studies were that they reported both conserved (especially the conservatism of X chromosome inactivation) and species-specific features among different animals and human. A good example would be Tian et al.'s work, in which they compared the spermatogenesis of four different species (human, mouse, sheep and cynomolgus macaque) [168]. In that study, they found the spermatogenesis between sheep and human were partially conserved at the transcriptional level (e.g., similar regulators were expressed in the same stages of spermatogenesis in human and sheep) and further showed the high conservatism of X chromosome inactivation during meiosis among four species [168], which was in line with Lau et al.'s report [167]. On the other hand, Rengaraj et al. reported a large proportion of species-specific genes between human and chickens (24 out of 56 orthologs) during the development of male fetal germ cells [166]. Additionally, the species comparison (usage type 4) could be combined with detecting certain genes' expression patterns (usage type 1). For example, INPP4B was found to be highly expressed in postmeiotic spermatogenic cells in the testis of both human and mice [170], which remained evolutionary conservatism, while SFTPC was expressed in late spermatogenesis of human but were absent in murine spermatogenesis [171], which indicated evolutionary divergence. And the tasks of those two studies were using testicular scRNA-seq data to show the expression of certain genes, but such tasks were done cross-species by combining two types of usage. In this area, the spermatogenesis among mammalian species or some other common animals (such as chicken) has been largely compared, but two subjects remain to be discovered. First, are there any highly conserved genes in spermatogenesis between humans and rare species or other non-mammalian animals (which are evolutionarily far)? Second, what's the conserved mechanism of testicular somatic cells' development among species? We believe scRNA-seq analysis will give the answers in the future. Besides, it's well known that ovary is the female counterpart of the testis, thus scientists tried to compare gonads of two genders. The conservatism of germ cells for both genders was observed during meiotic prophase I, such as the high expression of the same marker genes at each stage of primary SPC/oocytes (TEX19 in leptotene; SPO11 in zygotene; BRDT in pachytene and H1FOO in diplotene) [172]. Wang et al. even identified shared subtypes of germ cells (SPARC⁺ fetal germ cells) in prenatal testes and ovaries [82]. But huge heterogeneity of germ cells was also reported in the expression of X-linked genes, including the high expression of SMS in diplotene oocytes but not SPC and the lack of XIST in adult testes [73, 172]. Both homogeneity and diversity between genders were also reported in somatic cells during scRNA-seq analyses. For example, shared transcriptional expressions of NR5A1/SOX9 in both fetal SC and granulosa cells [73] and shared type of somatic cells (KRT19⁺cells) [82] reflected the homogeneity between genders, while the divergent somatic expression pattern of DMRT1 (high in SC but not granulosa cells) suggested the diversity [73].

Bioinformatic usages including algorithm developing and database/portal establishing

Since developmental biology is one of the most important areas in which scRNA-seq analysis is applied [173], so far there has been a huge accumulation of related raw data deposited online. Therefore, the raw data could then be used to develop or validate scRNA data-related bioinformatic tools, such as bioinformatic algorithms, packages, softwares and databases. Pont et al. developed a method for single-cell based scoring system and visualization of gene set signature, and a scRNA-seq set of normal human spermatogenesis was employed to show the function of this method [174]. Recently, Stow et al. developed another scRNA-seq-based bioinformatic tool called SCIFER to analyze long interspersed element-1 (L1) mRNA level from individual L1 loci in single cells, and further employed human testicular scRNA-seq data to show its function [175]. Theoretically, the role of scRNA-seq data could either be a developer (to build up the tool) or a validator (to confirm the tool works well or to show how to use it), while the current studies seemed to prefer to use testicular scRNA-seq data as a validator. Moreover, for many life scientists (especially clinicians) who would like to use scRNA-seq analysis to validate their findings or interested genes in human testes, it's too hard for them to start from the raw data due to the high demand for coding skills [176]. Therefore, it's more convenient for researchers to use some interact-friendly or “zero-code” online websites/portals. So far there have been several popular interactive online portals containing human testicular scRNA-seq data (listed in Table 5), some of which were testis-specific [31, 72, 78, 177], two were human gonad-based (with female's samples and in vitro differentiated gonadal cells) in terms of scRNA-seq data [79, 178-180], while the rest were based on various organs with testicular scRNA-seq data included. The function of these online portals was basically Uniform Manifold Approximation and Projection (UMAP)/t-distributed stochastic neighbor embedding (t-SNE) visualization of cells and illustration of the expression patterns of certain genes. Some advanced functions such as pseudotime trajectory of testicular cells or enrichment analysis are only supported by few portal [78]. As a result, bioinformatic engineers should pay more attention to the realization of integrated analysis and comprehensive analysis through interact-friendly (non-code) website tools.

Conclusions and future perspectives

The main purpose of the current review is to gather the updated evidence on human testicular samples revealed by scRNA-seq data, in order to gain a “single-cell” view of both normal and pathological development of the testis. So far, there have been a large number of novel findings in terms of normal spermatogenesis, age-related testicular microenvironment's development and changes, testis-related male infertility, as well as new bioinformatic methods suitable for testicular scRNA-seq data analysis. In terms of normal status, the whole development process of spermatogenic cells (from the most naïve primordial germ cells to testicular spermatozoa), together with their transcriptional patterns at each stage were largely revealed (including their similarity and differences among species). The researches on normal somatic cells were basically restricted to SCs, LCs and PTMs, while fewer scRNA-seq-based findings about human testicular macrophages, ECs or other rare cells (e.g., T cells) were reported. Additionally, numerous transcriptional changes and potential mechanisms have been revealed in disease data including spermatogenic dysfunction, OA, ejaculatory disorder and chromosomal abnormalities, giving us insight into the heterogenous pathogenesis of male infertility and providing us with innovative targets of treatments. Last but not least, more and more publicly available testicular scRNA-seq datasets give researchers more opportunities to do their individualized data analysis for different research needs. We believe this review will not only pave the way to the transcriptionally understanding of both normal and abnormal spermatogenesis, but also be a reference for the future usage of scRNA-seq analysis in the human testis.

One of the most prospective research highlights in this area is the combination of testicular scRNA-seq analysis with spatial transcriptomic analysis of the testis. Due to the dissociation of testicular cells in scRNA-seq, it could neither spatially illustrate spermatogenic cells in seminiferous tubules, nor analyze the spatial interaction between somatic and spermatogenic cells [134]. To solve this problem, Chen et al employed the novel technology “spatial transcriptomic analysis” to create spatial atlas that spatially illustrate testicular gene expression at near-single-cell resolution in the human testis [134]. More importantly, Garcia-Alonso et al did integrated analyses of the development of male gonads by combining scRNA-seq and spatial transcriptomics [74], which was more comprehensive and in-depth. We believe that the integration of these two approaches will improve future researches of the human testis and male infertility, allowing for a more complete and nuanced understanding of the functional/dysregulated spermatogenesis in the human testis. So far, there has been a limited number of such integrated studies, leading to a novel “Blue Ocean” of testicular researches.

Although scRNA-seq analysis has proved the existence of immune cells, such as macrophages (both M1 and M2), T cells, mast cells, or even B cells, in testicular microenvironment, the role of these minor cells and their transcriptional patterns in the development of male infertility remain unknown.

Meanwhile, more types of testicular diseases, including cryptorchidism, AZFc deletions, azoospermia after chemo/radiotherapy, as well as teratospermia, should be focused on and will benefit from the further usage of scRNA-seq technologies. Due to the high cost of scRNA-seq, few of the above-reviewed articles were directly out of clinical purpose. With the development and cost reduction of this technology, clinical usages such as diagnosis, classification, prediction of sperm retrieval or the effect of hormone treatment might come true.

Abbreviations

ScRNA-seq: single-cell RNA sequencing; GEO: Gene Expression Omnibus; OA: obstructive azoospermia; TESE: testicular sperm extraction; OB: obstruction; FACS: fluorescence-activated cell sorting; MACS: magnetic activated cell sorting; SPG: spermatogonia; SPC: spermatocyte; SSC: spermatogonial stem cell; diff_SPG: differentiating SPG; diffed_SPG: differentiated SPG; L.SPC: leptotene SPC; Z.SPC: zygotene SPC; P.SPC: pachytene SPC; D.SPC: diplotene SPC; SC: Sertoli cell; LC: Leydig cell; NOA: non-obstructive azoospermia; SCOS: Sertoli cell-only syndrome; PGC: primordial germ cell; FGC: fetal germ cell; CE: coelomic epithelium; PLCs: progenitor LCs; ILCs: immature LCs; MLCs: mature LCs; PGCLs: primordial germ cells-like cells; iNOA: idiopathic NOA; OAT: oligo-astheno-teratozoospermia; mTESE: microdissection TESE; ICSI: intracytoplasmic sperm injection; KS: Klinefelter Syndrome; AZFa: azoospermia factor a; AZFb: azoospermia factor b; AZFc: azoospermia factor c; IHC: immunohistochemistry; IF: immunofluorescence; WGS: whole genome sequencing; COVID-19: corona virus disease 2019; L1: long interspersed element-1; UMAP: Uniform manifold approximation and projection; t-SNE: t-distributed stochastic neighbor embedding; FSH: follicle-stimulating hormone; DEGs: differential expressed genes; HPA: Human Protein Atlas; GTEx: Genotype-Tissue Expression; FANTOM5: Function Annotation of The Mammalian Genome; TCGA: The Cancer Genome Atlas; GEPIA2: Gene Expression Profiling Interactive Analysis; MGI: Mouse Genome Informatics; HE: hematoxylin-eosin; PTM: peritubular myoid cell; wpf: weeks postfertilization; ELISA: enzyme-linked immunosorbent assay; RT-qPCR: reverse transcription real-time polymerase chain reaction; WB: western blotting; VUS: unknown-significance variants; SO: severe oligospermia; FISH: fluorescence in situ hybridization; CCK-8: cell counting kit-8; TUNEL: TdT-mediated dUTP Nick-End Labeling; Co-IP: co-immunoprecipitation; RIP: RNA binding protein immunoprecipitation; DMR: differentially methylated region; PAS: periodic acid-schiff; WGBS: whole-genome bisulfite sequencing; ENA: European Nucleotide Archive; scATAC-seq: single-cell assay for transposase-accessible chromatin sequencing; OSCC: oral squamous cell carcinoma; TFs: transcription factors; hESC: human embryonic stem cells; PGCLC: primordial germ cell-like cells; TGCT: testicular germ cell tumor; ExAC: Exome Aggregation Consortium; CNV: copy number variation; EMT: epithelial-mesenchymal transition; ECM: extracellular matrix.

Acknowledgements

Author contributions

FD, YM and XC conceived and designed the study. FD did the literature researches and wrote the first draft. YM, PP and XC reviewed, rewrote and revised the manuscript. All authors read and approved the final manuscript.

Competing Interests

The authors have declared that no competing interest exists.

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Author contact

Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: allanbaconcom. Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: my_julycom

Received 2022-12-27
Accepted 2023-3-25
Published 2023-4-9

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