Human spermatogonial markers

Accepted Manuscript Human spermatogonial markers

Kathrein von Kopylow, Andrej-Nikolai Spiess PII: DOI: Reference:

S1873-5061(17)30241-6 doi:10.1016/j.scr.2017.11.011 SCR 1097

To appear in:

Stem Cell Research

Received date: Revised date: Accepted date:

1 September 2016 6 November 2017 13 November 2017

Please cite this article as: Kathrein von Kopylow, Andrej-Nikolai Spiess , Human spermatogonial markers. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Scr(2017), doi:10.1016/ j.scr.2017.11.011

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ACCEPTED MANUSCRIPT Human spermatogonial markers Kathrein von Kopylow1,2 & Andrej-Nikolai Spiess1 1

Department of Andrology, University Hospital Hamburg-Eppendorf, Hamburg, Germany

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to whom correspondence should be addressed: [email protected]

ACCEPTED MANUSCRIPT Abstract In this review, we provide an up-to-date compilation of published human spermatogonial markers, with focus on the three nuclear subtypes Adark , Apale and B. In addition, we have extended our recently published list of putative spermatogonial markers with protein expression and RNA-sequencing data from the Human Protein Atlas and supported these by literature evidence. Most importantly, we have put substantial effort in acquiring a comprehensive list of new and potentially interesting markers by refiltering the raw data of 15

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published germ cell expression datasets (four human, eleven rodent) and subsequent building of intersections to acquire a robust, cross-species set of spermatogonia-enriched or

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-specific transcripts.

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Keywords: Spermatogonia, stem cells, gene expression, microarray, RNA sequencing

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1.

Morphology and functions of human spermatogonial subtypes

In the last 20 years (Schnieders et al., 1996; Kang et al., 1996), increasing efforts have been made to elucidate the function of human spermatogonia (SPG) or spermatogonial stem cells (SSC) from the analysis of specifically expressed markers. Results from marker expression studies support and extend interpretations on the physiological role of SPG independent from the established classification into the three distinct phenotypes of nuclear morphology,

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namely Adark , Apale and B. The nucleus of Adark SPG exhibits homogeneous dense, intensively stainable chromatin and

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at least one chromatin-free cavity (chromatin rarefaction zone), whereas the chromatin of Apale SPG is coarser, less dense, less stainable and devoid of the rarefaction zone. Apale and

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B SPG are distinguishable by their typical nucleolar localization (peripheral vs. central) and by the distance of the cells to the tubular basement membrane (close vs. minimal contact). In addition, nuclei of B SPG contain several flakes or granules of heterochromatin (Clermont

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1963; 1966; 1972; Rowley et al., 1971). Adark SPG were classically described as constituting the population of true or ‘reserve’ stem cells. Although active during puberty (Simorangkir et

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al., 2005), they only rarely divide in the adult organism (Clermont, 1972) but proliferate intensively when repopulating the seminiferous

epithelium

after

depletion of the

spermatogonial (spg) pool, e.g. by disease or injury (van Alphen and de Rooij, 1986; van

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Alphen et al., 1988; Nakagawa et al., 2007). In contrast, Apale SPG divide regularly and were thus historically defined as the ‘active’ stem cell pool (Clermont, 1972). This concept was

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questioned by Ehmcke & Schlatt (2006), who considered Apale SPG as rather being amplifying progenitor cells that possess transient self-renewal capacity. Human Apale SPG give rise to one generation of B SPG, which in turn differentiate to later germ cell stages (Clermont and Leblond, 1959). In a non-human primate study it was postulated that the

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different nuclear architecture of Adark and Apale SPG may correlate stronger with cell cycle stages (G0 vs. G1/S/G2/M) than with distinct stem cell functions (Hermann et al., 2009; 2010).

In Table 1, we have compiled 43 published markers whose germ cell expression is described to be exclusive in SPG. Here, immunostaining with SPG-specific markers reveals an unexpected molecular diversity and shows no correlation to the classically described Adark and Apale nuclear morphology (von Kopylow et al., 2012a, b). To date, one exception to the rule with exclusive expression in Adark SPG is the nuclear OCT2/POU2F2 (Octamer-binding protein 2, POU class 2 homeobox 2) protein (Lim et al., 2011). For almost all spg markers of Table 1, it is conspicuous that the same morphological spg cell type always exists in parallel with or without expression of that certain marker. Moreover, only a few markers are expressed in all spg subtypes, but this is not necessarily

ACCEPTED MANUSCRIPT accompanied by an expression of that marker in all cells of that subtype [i.e. SALL4 (Spalt like transcription factor 4), SPOC1 (PHF13, PHD finger protein), EXOSC10 (Exosome component 10); Eildermann et al., 2012; von Kopylow et al., 2012a; b]. Also noteworthy is that almost 30% of previously identified markers are nuclear localized transcription (co)factors, such as NANOG (Nanog homeobox), SOX3 (SRY-box 3), DMRT1 (Doublesex and mab-3 related transcription factor 1) and others, rendering “regulation of transcription” (Gene Ontology Term, p = 0.0022; https://david.ncifcrf.gov/) the spg subtypes’ most prominent

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functional difference. Furthermore, Table 1 depicts some contradictory results from the literature regarding the

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localization of THY1 (Thy-1 cell surface antigen) protein expression in the human testis as well as Fgfr3 (Fibroblast growth factor receptor 3) expression in the murine testis that need to

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be addressed in more detail. While He et al. (2010) identified human THY1 protein only on the basement membrane of a subpopulation of SPG, Izadyar et al. (2011) reported THY1 expression in germ cells located towards the tubular lumen. Finally, Valli et al. (2014) used a

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fraction of ITGA6+ (Integrin subunit alpha 6) / THY1dim [sic.] expressing SPG to efficiently isolate and enrich human SSCs. With respect to Fgfr3 protein expression in the mouse testis,

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two somewhat contradictory studies with immunohistochemical (IHC) results indicated either nuclear Ffgr3 expression in SPG (Willerton et al., 2004; polyclonal rabbit antibody sc-123 from Santa Cruz BT) or showed Fgfr3 immuno-stained Leydig cells and tubular cells, in

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particular spermatids (Lai et al., 2016; employing a not further specified antibody from Sigma, St. Louis, MO, USA).

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While abundant in mouse SPG, the pluripotency-associated isoform 1 of the transcription factor and pluripotency core regulator POU5F1 (POU class 5 homeobox 1; OCT3/4; OCT4), OCT4A (translated from transcript variant 1) is restricted to embryonal pluripotent cells and primordial germ cells in the human (de Jong and Looijenga, 2006). On the protein level,

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OCT4A serves as an established diagnostic marker for testicular germ cell tumors as it lacks expression in human cancer-free adult testis tissue (de Jong et al., 2005). Panagopoulos et al. (2008a, b) could demonstrate the absence of OCT4 transcript variant 1-mRNA in cancerfree adult testis tissue using a sophisticated PCR/restriction digestion assay. OCT4A-RTPCR reactions are prone to deliver false positives either from transcribed pseudogenes (Suo et al., 2005) or from trace amounts of DNA-pseudogene sequences after DNAse treatment (Panagopoulos et al., 2008b). To reliably detect OCT4 by RT-PCR, a cDNA synthesis negative control without reverse transcriptase is mandatory (Wang and Dai, 2010). The primary aim of this review is not to deliver an extensive elaboration on published markers of SPG or putative SSC, as these information are available in more detail from the references in Table 1 and Supplemental Data 1. Instead, we want to provide insight on the

ACCEPTED MANUSCRIPT differences of (putative) spg markers when obtained from either defined spermatogenic arrest/development (von Kopylow et al., 2010, “kop”; Orwig et al., 2008, “orw”; Shima et al., 2004, “shi”) or, as is the case for the majority of studies, from isolated cell populations (Schlecht et al., 2004, “sch”; Namekawa et al., 2006, “nam”; Chalmel et al., 2007, “ch, ch2”; Johnston et al., 2008, “joh”; Fallahi et al., 2010, “fal”; Soumillon et al., 2013, “sou”; Chalmel et al., 2014, “ch3”; Zhu et al., 2016, “zhu”; Griswold et al., 2006, “gris”; Jan et al., 2017, “jan”; Guo et al., 2017, “guo”). In addition, we attempt to confer more robustness to published

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markers by i) evaluating the SPG-specific localization through immunohistochemical (IHC) images of the Human Protein Atlas (HPA; Uhlén et al., 2005; https://www.proteinatlas.org/)

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and ii) creating intersections between the different studies in order to identify markers that are supported by multiple evidence. Although the latter is complicated from the fact that 11 of

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the 15 studies investigated the rodent model – largely because sufficient tissue or cell populations are available and less ethical issues arise – this enables the identification of

2. HPA evaluation of putative spg markers

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conserved, or alternatively, human-specific spg markers.

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To exemplify the first approach (HPA evaluation), we interrogated the microarray-based list of spg markers acquired from differential filtering of Sertoli cell-only (SCO) phenotypic testis (n = 5) with those harboring a homogeneous arrest at the level of SPG (n = 3; von Kopylow

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et al., 2010). The differential list, which was previously filtered using a Benjamini-Hochberg corrected p-value of 0.05 in combination with averaged expression ratios > 3, was updated

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with more recent annotations, and multiple Affymetrix probesets as well as pseudogenes were removed, yielding 116 unique transcripts that are enriched in SPG (Supplemental Data 1). For each of these markers, the HPA’s IHC testis sections were inspected and positive cellular staining noted. The staining was classified independent from the cellular (including

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compartment

exclusive

nucleolus

staining),

summarized

from

multiple

antibodies/patients and split into weak (grey), strong (black) or no (white) signals (SPG, spermatogonia; SPC, spermatocytes; SPT, spermatids; SC, Sertoli cells; IC, interstitial cells; LM, lamina propria). Cross-hatched boxes indicate that the HPA did not provide any IHC images. Furthermore, HPA tissue RNA sequencing (RNAseq) data were interrogated and provided together with germ cell localizations (Supplemental Data 1, “Published”) as stated in previous published studies (Supplemental Data 1, “References”). Several observations can be made from aggregating these data: Firstly, 82% of all markers exhibit spg staining. Secondly, published localizations of marker expression tally extremely well with those visible in the HPA. Thirdly, HPA RNAseq data indicate that all filtered spg markers are expressed in the testis, with 57 of the 116 markers (49%) testis-specific, and a large proportion of these being testis-specific X and non-X chromosome located cancer testis antigens that drive germ

ACCEPTED MANUSCRIPT cell mitosis and meiosis, respectively (Simpson et al., 2005). Finally, there is frequent low costaining of the markers in somatic cells (SC, IC, Lam), possibly stemming from the known unspecific binding of antibodies to Leydig cells, but maybe also from the reasons described in the next section. For the previously unpublished spg markers in Supplemental Data 1, similar observations are made (frequent staining in later germ cell stages, weak signals in interstitial cells, sporadic signals in Sertoli cells or Lamina propria cells). De novo filtering of spg markers

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3.

At this point, we must delve deeper into the various experimental setups of SPG marker

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identification. In principle, three different scenarios of spg marker expression exist: a) the marker is expressed exclusively in SPG,

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b) the marker is expressed in SPG as well as somatic cells,

c) the marker is expressed in SPG but also later germ cell stages. Depending on the experimental setup to identify spg markers, these will belong to any of the

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above three expression types. For instance, in von Kopylow et al. (2010), the only difference between the two testicular phenotypes is the presence of SPG. Hence, in using statistical

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methods to filter out the differential transcripts between these two states (e.g. T-tests, foldratios), all markers of category b) are subtracted, so that a) and c) type markers remain. Because later germ cell stages with the same putative marker expression are not present

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(and thus their transcripts can not be mathematically subtracted from the spg population), this approach delivers an enriched set of markers that are expressed by SPG but may also

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be expressed in spermatocytes or spermatids, as is the case for 18% of markers in Supplemental Data 1. The same applies to chemically- or cryptorchism-induced spermatogenic arrest (Orwig et al., 2008), as well as to developmental stages presenting defined germ cell types (Shima et al., 2004). In contrast, the majority of studies compiled in

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this work are based on enriched, isolated or laser-microdissected germ cell types without (Fallahi et al., 2010; Griswold et al. 2006; Guo et al., 2017; Jan et al., 2017; Namekawa et al., 2006; Zhu et al., 2016) or with isolated Sertoli cells as the only somatic cell type (Chalmel et al., 2007; Chalmel et al., 2014; Johnston et al., 2008; Schlecht et al., 2004; Soumillon et al., 2013). In these experimental scenarios, it is possible to conduct pairwise differential testing. For example, in a study containing isolated SPG, SPC and SPT, it is possible to filter transcripts expressed in SPG, but not in SPC and SPT (by means of multiple testingcorrected pairwise T-tests, available in many statistical software packages). However, one may have filtered category b) transcripts that are expressed in SPG and also somatic cells, as these were not employed in the filtering procedure. In contrast, if gene expression data from isolated Sertoli cells is additionally provided, the somatic transcripts of this cell type can be subtracted from the SPG gene expression profile. This will result in a more stringent SPG-

ACCEPTED MANUSCRIPT specific gene expression pattern (as transcripts common with Sertoli cells are removed), but in light of many more existing somatic cell types in the testis (e.g. Leydig cells, peritubular myoid cells, interstitial immune cells) whose transcripts were not present during differential filtering, many transcripts deemed as SPG-specific may still be of category b) somatic origin. This disadvantage is somewhat minimized in the whole tissue setup of von Kopylow et al. (2010), Orwig et al. (2008) and Shima et al. (2004), because the transcripts of all testicular somatic cell types are included in the differential testing. To date, there is no experimental

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approach to filter markers of category a) (exclusive spg expression), as this would imply a testicular system in which only SPG are missing. Naturally, this scenario does not exist, as

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subsequent germ cell stages are then also absent.

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The paradigm of our de novo filtering approach is to employ the same statistical method (pairwise T-tests in conjunction with fold-ratio calculation) on the raw data of all available datasets, which were minimally manipulated in order to decrease methodological bias.

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Our analysis pipeline included a complete reanalysis and refiltering of the raw microarray fluorescence or RNAseq FPKM/RPKM values for the studies mentioned at the end of Section either

downloaded

from

http://www.ncbi.nlm.nih.gov/geo/)

Gene

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1,

Expression

Omnibus

(GEO,

or ArrayExpress (https://www.ebi.ac.uk/arrayexpress/),

supplied by personal communication (data to Orwig et al., 2008), or provided as

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Supplemental Data (Zhu et al., 2016). Specific details on the studies, datasets, included cell types and statistical results can be found in the ‘Description’ sheet of Supplemental Data 2.

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The effort of a complete reanalysis was undertaken as previous studies did not focus on SPG-specific transcripts but more on general germ cell gene expression. In the first step of our principle analysis pipeline, raw data values (microarray fluorescence values, RNAseq FPKM/RPKM values) were quantile-normalized and/or log2-transformed

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when this was compulsory due to uneven signal distribution and non-normality of the data, respectively. The expression data were merged with platform-specific annotation files available at the same database locations or otherwise obtained from the ENSEMBL database (http://www.ensembl.org/). Spg markers were then filtered using pairwise t-testing for each gene in combination with expression ratio selection (at least > 2), either between isolated germ cell types/somatic cells or testicular arrest types. In most of the other cases, we had to vary the p-value threshold (e.g. p = 0.001 for Namekawa et al. (2006) or p = 0.05 for Schlecht et al. (2004)), with the problem of having to ignore a multiple testing correction of three possible pairwise t-tests (SPG vs. SC; SPG vs. SPC; SPG vs. SPT) for filtering an adequate number of genes in some studies. Naturally, the p-value depends strongly on the number of replicates, such that the von Kopylow et al. (2010) and Guo et al. (2017) data could be filtered with a multiple testing-corrected (Benjamini-Hochberg) p-value of 0.05. For a

ACCEPTED MANUSCRIPT very recent human study (Zhu et al., 2016), no raw database values were deposited, which is why we considered to refilter the genes in their Supplemental Data. All analyses were conducted in the R statistical programming environment (www.r-project.org). The corresponding R script and R workspace to reproduce all data and figures are available from the authors on request. The filtered SPG-enriched transcripts for each of the 15 studies, together with study and database details as well as filtering criteria are compiled in Supplemental Data 2. The cohort

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of filtered transcripts was displayed as per-sample boxplots for 14 of the 15 raw data studies, with spg transcripts color-coded in dark orange (Figure 1). Here, the on average higher consequence of statistical significance and ratio filtering.

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expression of all filtered (putative) SPG-specific transcripts is clearly visible as a

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We included a very recent single cell RNAseq study by Guo and coworkers (2017), because this work demonstrated stringent statistical results from the high number of replicates. Here, the experimental setup was different from the other 14 studies, as two subgroups of SPG population

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consisting of a quiescent/stem cell-like SSEA4+ and a proliferating/differentiating c-KIT+ were interrogated. Filtering SSEA4+ population-specific

transcripts

was

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considered feasible, since the resulting transcript set should be enriched for stemness functionality and should also partly overlap with the other datasets. For the Guo et al. (2017) data, the preprocessing steps were somewhat more extensive, and are the following: i)

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Removal of samples with less than 5000 expressed genes and where a set of eight different housekeeping genes (NOUFA1, ALDOA, GAPDH, COX7A2L, RPS27A, RPL19, NONO, USP11; Eisenberg & Levanon, 2003) displayed baseline expression (RPKM < 0.1;

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Supplemental Figures 1A, 1B), thereby condensing the original set of 101 SSEA4+ and 74 cKIT+ samples to 59/38, respectively (Supplemental Figure 1C), and ii) differential testing (Welch t-Test) on the log2-transformed RPKM values with selection of genes with a

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Benjamini-Hochberg corrected p < 0.01 and ratio > 5 in the SSEA4+ group. The acquired differential dataset of 712 transcripts is provided in Supplemental Data 2 and displays, in analogy to Figure 1, clear expression differences when plotted (Supplemental Figure 1D). The high number of obtained differential transcripts is in strong contrast to the results from Jan et al. (2017), who found no gene expression differences between quiescent Adark and differentiating Apale SPG. We reanalyzed the nine RNAseq profiles given in Table S1 of Jan et al. (2017) and can confirm this, so that we chose to group the data of these two subtypes together (compare Figure 1). The Guo et al. (2017) study probably delivered more differential transcripts because complete single cells were employed that harbor more mRNA than lasercaptured and fixed histological slices. Moreover, the sample size was considerably larger, enabling more sensitive statistics. Although clustering of gene expression profiles after statistical filtering is only a mere

ACCEPTED MANUSCRIPT visualization of the preceding selection method (one will obtain as many main clusters as the method separates groups), sometimes subclusters within the data are uncovered. Along these lines, when we conducted hierarchical clustering on the differential data, a sharp separation into the two SSEA4+/c-KIT+ clusters was evident (Supplemental Figure 1E). Unexpectedly, one SSEA4+ sample clustered within the c-KIT+ cluster, and the SSEA4+ cluster indicated the presence of two distinct subclusters, the latter most probably reflecting

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one of the two additional “intermediate states” mentioned in Guo et al. (2017). By means of the above pipeline, we acquired between 39 and 759 unique transcripts

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(Supplemental Data 2) that may prove as a valuable foundation for future research and that

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were subjected to cross-study intersection analysis in the next section. Cross-study intersection analysis of nine different gene expression studies

The second approach (study intersection analysis) is based on the paradigm that multiple

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evidence acquired by cross-study marker intersection confers robustness to the data and identifies feasible SPG markers with increased likelihood.

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Intersections between all 15 studies were built between the different human and rodent studies on the basis of the orthologous gene symbols, e.g. FGFR3 in human and Fgfr3 in mouse/rat. Most of the intersecting genes pertained to two different studies, however in this

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work we restricted the intersection dataset to an overlap of at least three studies, resulting in 70 cross-study and SPG-enriched transcripts (Figure 2A). One gene (COL3A1) was present

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in seven filtered studies, 3 in five studies (COL1A1, OGN, H19), 10 in four studies (TCF3, BMP2, UCHL1, LAMB1, SPRY2, GLI1, MDK, IFITM1, PEG3, COL5A1) and another 56 genes in three studies. For a complete list of all intersections, see Supplemental Data 3, Sheet “15 studies intersections”. Strikingly, when condensed to the human studies, a

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maximum of two overlaps was encountered for TEX15, SYCP1, ASXL3, AFF3 and MTUS1, although most of them also overlap with rodent studies. HPA analysis similar to Supplemental Data 1 (Figure 2B) indicated largely spg expression for the protein products, with co-staining frequently encountered in SPC and SPT, and often in the interstitial compartment (IC). Some transcripts (TEX15, SYCP1, SPATS2L) were the exception to the rule with exclusive spg expression. Once again, we must attend to the matter of differential filtering in light of the investigated cell types. The intersection analysis revealed frequent co-expression in the interstitial compartment. Despite the common observation of unspecific antibody binding by Leydig cells, studies using only isolated germ cells (e.g. Namekawa et al., 2006), or those including Sertoli cells (e.g. Chalmel et al., 2007, 2014), will not filter out transcripts co-expressed in Sertoli cells/interstitial cells or interstitial cells, respectively.

ACCEPTED MANUSCRIPT Interestingly, although the Zhu et al. (2016) gene list contained a mere 18 markers (and no raw data were database deposited), an overlap of three transcripts was encountered (TCF3, BMP2, UCHL1), which is quite significant considering these low gene numbers. TCF3 is a candidate key transcriptional factor for human spermatogenesis (Zhu et al., 2016) and BMP2 was described to promote spg proliferation (Puglisi et al., 2004). Finally, a set of seven SPG-enriched transcripts in the human study group (SEMA3C, SPR, HRSP12, BEX1, GFRA1, TRIB1 and CD47) was limited to the Guo et al. (2017) data. HPA

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analysis of the corresponding protein expression suggested no exclusive spg expression of these potential markers (although HRSP12 may have some potential), but this seems somewhat logical in light of their study question: genes differentially filtered between two spg

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states (SSEA4+, C-KIT+) are not necessarily SPG-specific, especially when no expression

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profiles of other germ cell types and/or somatic cells were subtracted.

5. Markers from the intersection analysis and Guo et al. (2017) that may prove useful

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The amount of SPG-enriched and -specific transcripts filtered from the 15 different studies is far too extensive to enable a HPA per-protein expression analysis, so we pay attention in this

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review to some interesting potential spg markers belonging to the family of DNA- and RNAbinding proteins, such as transcription factors (TFs). The dedicated reader is strongly encouraged to browse and exploit Supplemental Data 1 - 3, as these were compiled exactly

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for this purpose.

We focus here on TFs because they are key role players in stemness (Phillips et al., 2010)

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and constitute the majority of spg markers in Table 1 (DMRT1, NANOG, PASD1, PAX7, PLZF, OCT3/4, SALL4, SOX3, SPOC1, SPOCD1, SSX1-4, UTF1, ZKSCAN2). The selected TFs, together with a few other markers, were checked in the HPA for exclusive expression in SPG, with detailed exemplary images depicted in Figure 3.

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PIWIL4 (Piwi Like RNA-Mediated Gene Silencing 4) is a piRNA-binding protein belonging to the Argonaute family of proteins, which function in development and maintenance of germline stem cells by suppressing transposable elements (Sasaki et al., 2003). On the mRNA level, it is predominantly expressed in Adark /Apale SPG (Jan et al., 2017) and essential for efficient testicular regeneration after injury in mice (Carrieri et al., 2017), pointing to its pivotal role in replenishing an impaired stem cell pool. L1TD1 (LINE1 Type Transposase Domain Containing 1) is a new candidate hSSC marker enriched in the SSEA4+ spg population (Guo et al., 2017) and a member of the pluripotency interactome network OCT4/SOX2/NANOG (Emani et al., 2015). ZBTB33 (Zinc Finger and BTB Domain Containing 33) is a transcriptional regulator with bimodal DNA binding specifity that promotes histone deacetylation and formation of repressive chromatin structures in target gene promotors (Prokhortchouk et al., 2001). USF3 (KIAA2018; Upstream Transcription Factor Family Member 3) is a thyroid carcinoma-

ACCEPTED MANUSCRIPT associated TF (Ni et al., 2017) whose testicular function in hitherto unknown. TCF3 (Transcription Factor 3), present in the SPG-enriched data of von Kopylow et al. (2010), Namekawa et al. (2006) and Zhu et al. (2016), where in the latter it is mentioned as a key spg TF, displays exclusive spg expression in the HPA with two of three antibodies. Guo et al. (2017) define TCF3 as an ancillary pluripotency factor from its promoter hypomethylation in the SSEA4+ group. Results from fish studies corroborate spg expression, but also in later germ cell stages (Wang et al., 2011).

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SIX1 (SIX homeobox 1 transcriptional regulator), in cooperation with Six4, is required for male gonadal differentiation in mice (Fujimoto et al., 2013). Six1-/- knockout mice die at birth

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from severe myogenesis and rib deformation (Laclef et al., 2003).

TEX15 (Testis expressed 15) has been described as a human SPG-expressed (Loriot et al.,

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2003), infertility-associated (Okutman et al., 2015) cancer testis antigen that is important for meiotic chromosomal synapsis in mice (Yang et al., 2008). Interestingly, HPA analysis reveals the strongest expression in SPG (data not shown), so other functions are not

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unlikely.

AMBRA1 (Autophagy and beclin 1 regulator 1) links autophagy to cell proliferation by

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promoting dephosphorylation and subsequent degradation of the protooncogene C-MYC (Cianfanelli & Cecconi, 2015; Cianfanelli et al., 2015). AMBRA1 defects are associated with increased tumorigenesis, and cells with only one AMBRA1 allele hyperproliferate (Cianfanelli

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et al., 2015). Besides its implication in macroautophagy, it is involved in mitophagic stressinduced mitochondrial autophagy (Strappazzon et al., 2015). AMBRA1 gene-trap disruption

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results in late embryonic lethality with severe exencephalic defects and impaired autophagy (http://www.informatics.jax.org/allele/genoview/MGI:3716751). SLC25A22 (Solute Carrier Family 25 Member 22), a mitochondrial glutamate carrier (Fiermonte et al., 2002), DCAF4L1 (DDB1 And CUL4 Associated Factor 4 Like 1), a paralog

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to the ubiquitin ligase complex receptor protein DCAF4, ZNF654 (Zinc Finger Protein 654), PPRC1 (Peroxisome Proliferator-Activated Receptor Gamma, Coactivator-Related 1) and SPATS2L (Spermatogenesis Associated Serine Rich 2 Like) have no known function in the testis. 6. Outlook The main intention of this review - in addition to providing a complete up-to-date compilation of published human spg markers - is to supply the scientific community with reanalysed tables of markers enriched in or specific to SPG. From the different approaches of obtaining spg markers (spermatogenic arrest states, isolated germ cells), we aimed to convey the limitations of these methods for acquiring markers exclusively expressed in this cell type. As the HPA analysis confirmed, many of the spg markers are co-expressed in later germ cell

ACCEPTED MANUSCRIPT stages or even somatic cells. However, the conducted intersection analysis with rodent data, in combination with HPA protein localizations, clearly demonstrated that previously published gene expression data is amenable to a more exhaustive interrogation. This way, we were able to extract and compile a plethora of novel SPG-enriched and often -specific markers that provide a solid base for future research. Acknowledgements

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The project was funded by DFG grants to K.v.K. (KO 4769/2-1) and A.-N.S. (SP 721/4-1). We also thank Ewa Rajpert-De Meyts for the inspection of archived immunohistochemical

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stainings and Matthias Schaks for fruitful discussions.

Table 1: Compilation of 43 published human markers expressed (on the protein level) exclusively in SPG as the only germ cell type, and their expression in rodents.

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The table states the spg markers, the spg subtypes (Adark , Apale and B) that express the corresponding marker, the cellular compartment (S, surface; C, cytoplasm; N, nucleus) of

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expression as well as the references. Color coding for marker expression: Green, in humans and rodents; black, in humans, unclear if in rodents; red, in rodents only; blue, in humans, rat, but not in mouse; pink, in humans, contradictory results in mouse. Asterisks, cell surface

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markers used for isolation of human SPG/SSC. Some of these markers exhibit additional expression in testicular somatic cells, such as DMRT1 and DMRT6 in Sertoli, KIT in

Marker

Subtype

CBL

CD9*

undiff., differentiating

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ITGA6 (CD49f)*

ITGB1 (CD29)

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interstitial and THY1 in peritubular/interstitial cells.

undifferentiated

Com partment S/C S

S S

CD133*

S

CHEK2

N

DMRT1

A pale, B

DMRT6 (DMRTB1)

N

DSG2 ENO2 (NSE) ELAVL2

N

S/C undifferentiated

C

basal SPG

C

References von Kopylow et al., 2010 Shinohara et al., 1999 He et al., 2010 Izadyar et al., 2011 Valli et al., 2014 Schaller et al., 1993 Shinohara et al., 1999 Lim et al., 2010 Kanatsu-Shinohara et al., 2004 Lim et al., 2010 Zohni et al., 2012 Guo et al., 2014 Bartkova et al., 2001 Rajpert-de Meyts et al., 2003 Looijenga et al., 2006 Matson et al., 2010 von Kopylow et al., 2012a & 2012b Jørgensen et al., 2012 Djureinovic et al., 2014 Djureinovic et al., 2014 Zhang et al., 2014 von Kopylow et al., 2010 Kang et al., 1996 Waheeb & Hofmann, 2011 Valli et al., 2014 Jan et al., 2017

ACCEPTED MANUSCRIPT undifferentiated

S

EXOSC10

A dark, A pale, B

N

A dark, A pale

S/C

undifferentiated

C

GFRA1*

A dark, A pale

S

GPR125*

undifferentiated

S

ID4

undifferentiated

N/C

A pale, B

S

LIN28

undifferentiated

C

MAGE-A4

A dark, A pale, B

C N

NANOS2

C

ED

NANOG

NANOS3

C N

EP T

PASD1 rare basal SPG

N

PLZF (ZBTB16)

A dark, A pale

N

POU2F2 (OCT2)

A dark

N

AC C

PAX7

POU5F1 (OCT3/4) SAGE1 SALL4

N

B

N

A dark, A pale, B

N

SOX3

N

SPOC1 (PHF13)

A dark, A pale, B

N

SPOCD1

undifferentiated

N

SSEA4*

undifferentiated

S

differentiating

N

A pale, B

N

contradictory results

S

SSX1 SSX2-4

THY-1 (CD90)*

RI

SC

KIT (CD117)

NU

FMR1 (FMRP)

MA

FGFR3*

Ryu et al., 2004 Kanatsu-Shinohara et al., 2011 Valli et al., 2014 von Kopylow et al., 2012a Willerton et al., 2004 Juul et al., 2007 von Kopylow et al., 2010, 2012a & 2012b, 2016 Ew en et al., 2013 Lai et al., 2016 Devys et al., 1992 Bächner et al., 1993 Guo et al., 2017 Meng et al., 2000 He et al., 2007 Grisanti et al., 2009 Guo et al., 2014 Seandel et al., 2007 Dym et al., 2009 He et al., 2010 Oatley et al., 2011 Chan et al., 2014 Sachs et al., 2014 Helsel et al., 2017 Yoshinaga et al., 1991 Schrans-Stassen et al., 1999 Unni et al., 2009 Izadyar et al., 2011 von Kopylow et al., 2012a Valli et al., 2014 Zheng et al., 2009 Aeckerle et al., 2012 Rajpert-de Meyts et al., 2003 He et al., 2010 Kuijk et al., 2010 Izadyar et al., 2011 Liu et al., 2012 Kusz et al., 2009 Sada et al., 2009 Suzuki et al., 2009 Lolicato et al., 2008 Suzuki et al., 2009 Jørgensen et al., 2012 Cooper et al., 2006 von Kopylow et al., unpublished Djureinovic et al., 2014 Aloisio et al., 2014 Buaas et al., 2004 Costoya et al., 2004 Valli et al., 2014 Lim et al., 2011 Looijenga et al., 2003 Ohbo et al., 2003 Dann et al., 2008 Lim et al., 2011 Eildermann et al., 2012 Gassei & Orw ig, 2013 Valli et al., 2014 Raverot et al., 2005 Chen et al., 2009 Bördlein et al., 2011 von Kopylow et al., 2012a & 2012b Guo et al., 2017 Izadyar et al., 2011 Kokkinaki et al., 2011 Guo et al., 2017 dos Santos et al., 2000 Stoop et al., 2001 Lim et al., 2011 Kubota et al., 2003 He et al., 2010 Izadyar et al., 2011 Guo et al., 2014 Valli et al., 2014

PT

EPCAM*

ACCEPTED MANUSCRIPT TRAPPC6A

differentiating

N

TSPY

C

UCHL1

A dark, A pale

C

UTF1

A dark, A pale

N

undifferentiated

N

ZKSCAN2

Guo et al., 2017 Schnieders et al., 1996 Kido & Lau, 2006 Wang et al., 2006 He et al., 2010 Valli et al., 2014 Kristensen et al., 2008 van Bragt et al., 2008 von Kopylow et al. 2010 & 2012a & 2012b Easley et al., 2012 Valli et al., 2014 Guo et al., 2017

PT

Figure 1: Identification of spg markers from raw expression data of 14 different microarray/RNAseq based studies employing human and rodent germ cells. Original

RI

expression values (raw fluorescence values; RPKMs; FPKMs; CPMs) were downloaded from Gene Expression Omnibus/ArrayExpress databases or extracted from Supplemental Data,

SC

merged with platform-specific annotation files and optionally normalized/logarithmized. Spg markers were filtered using t-testing/pairwise t-testing in combination with expression ratio

NU

selection either between isolated germ cell types or testicular arrest types. Selection criteria are given in the ‘Description’ sheet of Supplemental Data 2, i.e. “T-test < 0.001 & Ratio > 2” denoting a selection of transcripts with an uncorrected p-value < 0.001 and (averaged)

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expression ratio > 2. The number of acquired differential markers is supplied in the graph titles. The expression values of filtered transcripts are displayed as per-sample boxplots using color coding as defined in the legend (SC, green; SPG, orange; SPC, red; SPT, blue).

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For more details, see Section 3 and main text. The filtered spg markers for all 14 datasets

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are compiled in the corresponding sheets of Supplemental Data 2. Figure 2: Obtaining 70 robust spg markers by cross-study intersection filtering of all studies. (A) 70 spg markers were filtered that were present in three (gold), four

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(darkorange), five (red) or seven (darkred) of the differential datasets (overlap of six did not occur). The names of the studies are given as abbreviations under the heatmap columns and in Section 3. (B) Expression of these markers (weak, grey; strong, black) as obtained from the Human Protein Atlas. White cells indicate no expression; cross-hatched cells indicate absence in the database. More details for this figure can be found in Supplemental Data 3. SPG, spermatogonia; SPC, spermatocytes; SPT, spermatids; SC, Sertoli cells; IC, interstitial cells; LM, lamina propria. Figure 3: Representative Human Protein Atlas examples of spg markers filtered from all 15 gene expression studies. Depicted are the immunohistochemical images of 12 markers with exclusive protein expression in SPG, as obtained from the HPA. These markers were filtered from the 15 different gene expression studies, i.e. Guo et al. (2017), von Kopylow et al. (2010) and those used for intersection analysis in Figure 2. Color bars in the

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