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Transcriptome analyses in infertile men reveal germ
cell–specific expression and splicing patterns
Lara M Siebert-Kuss
1,
* , Henrike Krenz
2,
*, Tobias Tekath
2
, Marius W¨
oste
2
, Sara Di Persio
1
, Nicole Terwort
1
,
Margot J Wyrwoll
3
, Jann-Frederik Cremers
4
, Joachim Wistuba
1
, Martin Dugas
2,5
, Sabine Kliesch
4
, Stefan Schlatt
1
,
Frank Tüttelmann
3
,J
¨
org Gromoll
1
, Nina Neuhaus
1,
†, Sandra Laurentino
1,
†
The process of spermatogenesis—when germ cells differentiate
into sperm—is tightly regulated, and misregulation in gene ex-
pression is likely to be involved in the physiopathology of male
infertility. The testis is one of the most transcriptionally rich
tissues; nevertheless, the specific gene expression changes oc-
curring during spermatogenesis are not fully understood. To
better understand gene expression during spermatogenesis, we
generated germ cell–specific whole transcriptome profiles by
systematically comparing testicular transcriptomes from tissues
in which spermatogenesis is arrested at successive steps of germ
cell differentiation. In these comparisons, we found thousands of
differentially expressed genes between successive germ cell
types of infertility patients. We demonstrate our analyses’po-
tential to identify novel highly germ cell–specific markers (TSPY4
and LUZP4 for spermatogonia; HMGB4 for round spermatids) and
identified putatively misregulated genes in male infertility
(RWDD2A,CCDC183,CNNM1,SERF1B). Apart from these, we found
thousands of genes showing germ cell–specific isoforms (in-
cluding SOX15,SPATA4,SYCP3,MKI67). Our approach and dataset
can help elucidate genetic and transcriptional causes for male
infertility.
DOI 10.26508/lsa.202201633 | Received 26 July 2022 | Revised 7 November
2022 | Accepted 8 November 2022 | Published online 29 November 2022
Introduction
Spermatogenesis is a complex process by which spermatogonia
undergo differentiation, becoming spermatocytes, which, after under-
going meiosis, originate haploid spermatids and finally sperm. Distur-
bances in spermatogenesis, which cause male infertility, can range from
arrest at different steps during germ cell differentiation to the complete
absence of germ cells, known as a Sertoli cell–only (SCO) phenotype.
To understand the gene expression profiles of specific testicular
cell types and, thus, to gain information about changes in gene
expression during spermatogenesis that may lead to male infer-
tility, previous studies have taken advantage of samples with
distinct histological phenotypes of male infertility. Specifically,
prior studies used samples matched by cellular composition and
also performed comparative microarray analyses of samples dif-
fering in the presence of one specific germ cell type (von Kopylow et
al, 2010;Chalmel et al, 2012;Lecluze et al, 2018). For example, in a
study that compared testicular tissues with SCO and spermato-
gonial arrest phenotypes, which only differ in the presence of
spermatogonia, von Kopylow et al (2010) were able to identify
transcripts specifically expressed by spermatogonia. They identi-
fied the spermatogonial markers FGFR3 and UTF1, which are cur-
rently considered specific markers for different spermatogonial
subpopulations (Guo et al, 2018;Sohni et al, 2019;Di Persio et al,
2021). Chalmel et al (2012) expanded on this approach by including
samples from (pre)pubertal and adult arrest phenotypes, thereby
extracting the transcriptional profiles of additional germ cell types.
These studies demonstrated that comparing distinct arrest phe-
notypes allows for identifying transcripts expressed at specific
stages of germ cell differentiation during normal spermatogenesis
(von Kopylow et al, 2010;Chalmel et al, 2012).
Currently, technological developments such as RNA sequencing
(RNA-seq) enable an unbiased and more comprehensive analysis
of the transcriptome. Specifically, single-cell RNA sequencing
(scRNA-seq) of human testicular tissues has revolutionized germ
cell–specific RNA profiling by allowing the identification of cell
type–specific gene expression patterns (Guo et al, 2018;Hermann et
al, 2018;Wang et al, 2018;Sohni et al, 2019;Di Persio et al, 2021).
However, scRNA-seq offers sparser data compared with conven-
tional bulk RNA-seq and, by sequencing only the near-poly-A ex-
tremities of the transcripts, generates limited information on
transcriptional isoforms (Tekath & Dugas, 2021). Therefore, RNA-seq
1
Centre of Reproductive Medicine and Andrology, Institute of Reproductive and Regenerative Biology, University of Münster, Münster, Germany
2
Institute of Medical
Informatics, University of Münster, Münster, Germany
3
Institute of Reproductive Genetics, University of Münster, Münster, Germany
4
Department of Clinical and Surgical
Andrology, Centre of Reproductive Medicine and Andrology, University Hospital of Münster, Münster, Germany
5
Institute of Medical Informatics, Heidelberg University
Hospital, Heidelberg, Germany
Correspondence: Sandra.Laurentino@ukmuenster.de
*Lara M Siebert-Kuss and Henrike Krenz are joint first authors
†Nina Neuhaus and Sandra Laurentino are joint senior authors
©2022Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 1of15
on 29 November, 2022life-science-alliance.org Downloaded from
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provides the most complete capture of the transcriptome, including
all transcripts obtained through post-transcriptional processing.
Notably, the testis presents unusually high levels of these post-
transcriptional events, including alternative splicing (AS) (Kan et al,
2005). AS enables the production of different transcripts and po-
tentially different proteins from a single gene. Splice-site variants in
some genes (the follicle-stimulating and luteinizing hormone re-
ceptor genes) have been linked to human male infertility (Song et al,
2002;Bruysters et al, 2008). However, it remains to be elucidated
which role different transcript isoforms play in regulating sper-
matogenesis and how different isoforms are involved in the pa-
thology of male infertility. Knowledge of the changes in isoforms that
result from AS during human spermatogenesis would open a new
avenue for identifying so far unknown causes of male infertility.
The role of different genes and their variants in testicular phys-
iopathology is far from being elucidated. In this study, we aimed at
generating whole transcriptome profiles of human testicular germ
cells. For the first time, we combined total RNA-seq of distinct
pathological phenotypes with published scRNA-seq data to unveil the
transcriptome profiles of male germ cells and determined changes in
AS during human spermatogenesis. Using this setup, we evaluated the
functional consequences of a pathogenic variant in a male infertility
case, demonstrating the potential of the outlined approach.
Results
Clinical and histological evaluation of the patient cohort
To study germ cell–specific whole transcriptome changes during human
spermatogenesis, we carefully selected histologically characterized
testicular biopsies (Tables 1 and S1) presenting homogenous pheno-
types of azoospermia (azoospermia = absence of sperm in the ejaculate,
n = 16), namely, no germ cells present in the testicular tissue (SCO, n = 3);
arrests at the spermatogonial (SPG, n = 4), spermatocyte (SPC, n = 3), or
round spermatid (SPD, n = 3) levels; and complete spermatogenesis as
controls (CTR, n = 3) (Fig 1A and B). Except in the CTR samples with
complete spermatogenesis, no sperm was found via microscopic ex-
amination of the mechanically dissociated biopsies (Table 1).
Genetic characterization of the patient cohort
No patients showed chromosomal abnormalities except for one
(spermatid arrest patient SPD-3) who had a low-grade XXY mo-
saicism (47,XXY[2]/46,XY[28]). A control patient (CTR-1) was previ-
ously diagnosed during routine genetic diagnostics with the
heterozygous CFTR variants c.1521_1523delCTT p.(Phe508del) and
c.2991G>C p.(Leu997Phe), suggesting compound heterozygosity, which
represents the cause for a congenital absence of vas deferens (CBAVD) in
this man. By analyzing whole exome sequencing (WES) data of our
patients, we identified a heterozygous missense variant in SYCP3 (patient
SCO-2), which is predicted to potentially affect splicing (NM_153694.4:
c.551A>C p.(Lys184Thr)). We identified a heterozygous missense variant in
PLK4 with a CADD score of 28.8 (NM_014264.5 c.950C>T p.(Pro317Leu)) and
the heterozygous splice-site variant NM_021951.3 c.355-4C>T p.? in DMRT1,
which might also have an impact on splicing (patient SPD-1). A patient
with spermatocyte arrest (SPC-1) was identified in a parallel study to
carry a homozygous deletion affecting the complete SYCE1 gene (Wyrwoll
et al, 2022). A patient with spermatogonial arrest (SPG-1) was in parallel
identified with the heterozygous synonymous variant NM_004959.5
c.990G>A p.(Glu330=) in NR5A1, which affects the last base of exon 5
and is also predicted to alter splicing (Wyrwoll et al, 2022).
Transcriptome analyses recapitulate the phenotypic and genetic
characteristics of the patient cohort
We sequenced total RNA obtained from testicular biopsies, in-
cluding all transcript isoforms deriving from alternative splicing.
Table 1. Clinical characteristics of the patient groups.
Patient
groups Karyotype
Histological parameters of tubules Hormonal parameters (normal
range) Sperm
mTESE
Score % ES % RS % SC % SG % SCO % TS FSH (1–7
U/l)
LH (2–10
U/l)
T(>12
nmol/l)
SCO (n = 3) 46,XY 0 0 0 0 0 98.7
(±1.5)
1.3
(±1.5)
13.3
(±4.2) 5.8 (±2.6) 13.7
(±3.4) No
SPG (n = 4) SPG-1, SPG-2, SPG-3:
46,XY, SPG-4: n.d. 00 0 0 31.0
(±34.6)
34.3
(±20.7)
35.0
(±20.6)
20.4
(±14.2)
13.4
(±9.7)
16.2
(±6.9) No
SPC (n = 3) 46,XY 0 0 0 89.3
(±11.0)
4.7
(±4.6)
1.0
(±1.0)
5.3
(±5.5) 5.7 (±1.3) 5.7 (±4.5) 9.9 (±2.4) No
SPD (n = 3) SPD-1, SPD-2: 46,XY,
SPD-3a 00 28.3
(±2.3)
59.3
(±18.0)
3.0
(±2.0)
1.7
(±2.9)
8.7
(±14.2) 7.4 (±0.9) 3.7 (±0.5) 18.7
(±5.7) No
CTR (n = 3) 46,XY 8–10 87.3
(±8.6)
3.3
(±2.5)
8.7
(±5.7) 001.0
(±1.0) 2.5 (±1.3) 2.6 (±1.0) 24.7
(±2.2) Yesb
Data are presented as mean ± SD. Percentage of tubules with the most advanced germ cell type present: elongated spermatids (%ES), round spermatids (%RS),
spermatocytes (%SPC), spermatogonia (%SPG), Sertoli cell–only phenotype (%SCO), or tubular shadows (%TS). Score refers to Bergmann and Kliesch score
(Bergmann & Kliesch, 2010). Hormonal parameters for follicle-stimulating hormone (FSH), luteinizing hormone (LH) and testosterone (T).
a
Patient SPD-3 had a low number of XXY karyotype mosaicism (47,XXY[2]/46,XY[28]).
b
Testicular sperm extraction (TESE) results: CTR-1 had 100/100 sperm, CTR-2 had an average of 89/100 sperm; no TESE result available for CTR-3 because the
reason for surgery was a suspected malignant tumor. SCO, Sertoli cell–only; SPG, spermatogonial arrest; SPC, spermatocyte arrest; SPD, round spermatid arrest;
CTR, control spermatogenesis; n.d., not determined.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 2of15
After RNA-seq, principal component analysis (PCA) organized the
spermatogenic arrest samples in a consecutive order (Fig 1C),
mirroring their sequential spermatogenic phenotypes.
To evaluate the extent to which the identified exome variants
influence the testicular transcriptome, we analyzed the identified
variants in the total RNA-seq data of the respective patients. In line
with the homozygous deletion of SYCE1, we detected no RNA of
SYCE1 in SPC-1 in comparison to SPC-2 and SPC-3. We found that the
heterozygous synonymous variant in NR5A1 of patient SPG-1 led to
an alternative 59splice site in the affected exon 5 (Fig 2). This
originates from a transcript with an in-frame deletion of 48 nu-
cleotides. For all other variants, which were predicted to affect
splicing, no alternative splice sites were identified.
Comparative analysis reveals germ cell–specific transcriptome
profiles
We aimed at generating germ cell–specific expression profiles to
study transcriptome changes throughout spermatogenesis. To this
end, we systematically performed differential gene expression
(DEG) analysis between groups of different cellularities, repre-
senting the four main differentiation steps of male germ cells: SCO
versus SPG, SPG versus SPC, SPC versus SPD, and SPD versus CTR (Fig
3A). This revealed between 839 and 4,138 DEGs in the four group
comparisons (FDR < 0.05 and absolute log
2
FC ≥1).
In the SCO versus SPG comparison, most transcript changes were
due to the increased expression of 2,073 genes in SPG samples
(Table S2). These DEGs also remained highly expressed in other
groups containing spermatogonia (SPC, SPD, CTR), indicating that
most of these transcripts originate from the presence of sper-
matogonia (Fig 3B). Indeed, among the highly expressed genes were
well-known spermatogonial genes such as MAGEA4 and FGFR3
(Table S3). The most prominent changes in gene expression were
found when comparing SPG with SPC samples (Table S4). The 2,886
genes that were high in expression included spermatocyte-specific
genes like AURKA and OVOL1 (Table S3). The same genes also
showed high expression in SPD and CTR samples and low to absent
expression in SPG and SCO. This indicates that these genes are
specific to spermatocytes rather than the result of gene expression
alterations in other cell types. When comparing SPC with SPD
samples, we found 2,345 highly expressed genes in SPD samples
(Table S5), including spermiogenesis marker genes TNP1 and PRM1
(Table S3). These genes also showed higher expression in CTR
samples and lower expression in samples lacking spermatids (SPC,
SPG, SCO), in accordance with their spermatid-specific expression
pattern. The most subtle changes in gene expression were detected
when comparing SPD with control samples (Table S6), in which the
presence of elongated spermatids is the only histological differ-
ence. Genes with increased expression in CTR samples (776) showed
lower expression levels in the spermatogenic arrest samples (SPD,
SPC, SPG, SCO) and, among others, included genes associated with
the sperm flagellum like CATSPER3 and TEKT2 (Table S3).
Novel germ cell–specific marker genes and their expression at
single-cell resolution
To identify novel germ cell–specific marker genes, we focused on
the top 120 DEGs, ranked by their log
2
FC, with elevated expression
in SPG, SPC, SPD, and CTR samples (Fig 3C–F). We evaluated all top
DEGs per group comparison for their germ cell specificity in our
published scRNA-seq dataset of three patients with complete
spermatogenesis (Di Persio et al, 2021)(Fig 3G) and in one additional
Figure 1. Cellular composition of the human testicular biopsies.
(A) Schematic illustration depicts the cellular composition of the testicular biopsies with Sertoli cell–only phenotype, arrest at the spermatogonial (SPG), spermatocyte
(SPC), and spermatid (SPD) stage and samples with complete spermatogenesis, which were used as controls (CTR). (B) Stacked bar plots represent the proportional
cellularity of round seminiferous tubules ranked according to the most advanced germ cell type in the tubule. The cellularity of samples from each group is averaged.
(C) Principal component analysis (PCA) plot depicts clustering of the total RNA–sequenced samples based on the top 500 genes.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 3of15
scRNA-seq dataset also of three patients with complete sper-
matogenesis (Hermann et al, 2018). We found that 12–27% of the top
DEGs per group comparison were absent or showed very low ex-
pression levels in the scRNA-seq datasets evaluated (Table S7).
Among the undetected genes were long non-coding and read-
through RNAs of two neighboring genes. An average of 85 ± 9% of
genes were represented in the scRNA-seq datasets and displayed
highly germ cell–specific expression patterns (Fig S1).
Among the genes with highly germ cell–specific expression (Fig
S2), we identified potential new marker genes for spermatogonia
(Fig 3H;leucine zipper protein 4 (LUZP4); testis-specific protein
Y–linked 4 (TSPY4); anomalous homeobox (ANHX)), spermatocytes
Figure 2. Alternative 59splice site in exon 5 of NR5A1 in one patient with spermatogonial arrest (SPG-1).
(A) Sashimi plots depicting the read coverage as bars across the genomic location of NR5A1 in patient SPG-1 carrying the heterozygous synonymous variant
NM_004959.5 c.990G>A p.(Glu330=) (red) in comparison to the other SPG patients (green) and one control patient (CTR-1, purple). Arcs represent the splice junctions of
exon 5 according to the sequencing reads. Boxes indicate the coding region and larger boxes the untranslated regions in the Refseq. (B) Zoom into the coverage plots for
exon 5 shows the alternative 59splice site in SPG-1 (dark red arc and arrow), which is not present in the other patients and which leads to a decrease in coverage in the
last 48 nucleotides of exon 5. (C) Schematic illustration of the splicing consequence in the coding region because of the heterozygous synonymous variant in comparison
to the other patients without the pathogenic variant serving as controls. In the patient carrying the variant, both the canonical transcript and a transcript with a 48
nucleotide deletion are present.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 4of15
(Fig 3I;proline rich acidic protein 1 (PRAP1); ferritin heavy chain like
17 (FTHL17); synaptogyrin 4 (SYNGR4)), round spermatids (Fig 3J;
proline rich 30 (PRR30); actin like 7A (ACTL7A); high mobility group
box 4 (HMGB4)), and elongated spermatids (Fig 3K;TP53 target 5
(TP53TG5); 3-oxoacid CoA-transferase 2 (OXCT2); hemogen
(HEMGN)). We evaluated the expression at the protein level for
three of the identified marker genes (TSPY4, LUZP4, HMGB4) and
found that these markers are indeed expressed specifically in the
Figure 3. Examination of germ cell–specific gene expression.
(A) Schematic illustration of the group comparisons and the respective color codes of their differentially expressed genes (DEGs). (B) The heat map displays the
normalized expression counts of the DEGs (rows) of each group comparisons across all samples (columns) scaled via a row Z-score. Red = increased; blue = decreased. (C,D,E,F)
Volcano plots of the increased and decreased genes in samples with (C) spermatogonial, (D) spermatocyte, (E) and spermatid arrest and in (F) complete spermatogenesis.
(G) UMAP plot depicts 15,546 cells integrated from three patients with obstructive azoospermia and complete spermatogenesis (Di Persio et al, 2021). (H,I,J,K)Feature plots
show the expression of three novel genes for (H) spermatogonia, (I) spermatocytes, (J) round spermatids, and (K) elongated spermatids at single-celllevel.(L) Micrographs
showing immunohistochemical stainings for LUZP4, TSPY4, and HMGB4 in testicular tissue with full spermatogenesis (n = 3). Arrow heads in the inlays indicate positive
spermatogonia (white) and round spermatids (black). IgG control shows no staining. Scale bars = 50 μm for micrographs and 20 μm for inlays. Data information: genes with a
false discovery rate (FDR) < 0.05 and a log
2
fold change (FC) ≥1 were considered DEGs based on Wald test and adjusted with Benjamini–Hochberg.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 5of15
expected germ cell types in control samples with full spermatogenesis
(Fig 3L). We further characterized the spermatogonial specificity of our
newly identified spermatogonial marker TSPY4. Co-localization with
the pan-spermatogonial marker MAGEA4 revealed that TSPY4 is
expressedin88±5.2%ofMAGEA4+cells(Fig S3A). To evaluate whether
TSPY4 is a marker for undifferentiated spermatogonia, we co-immu-
nolocalized TSPY4 with the pan-undifferentiated spermatogonial
marker UTF1. We found that an average of 85 ± 5.6% of undifferentiated
spermatogonia also express TSPY4 (Fig S3B).
Alternative splicing is uncoupled from gene expression
To study alternative splicing, we performed a differential transcript
usage (DTU) analysis between all four group comparisons. DTU analysis
calculates and compares the proportional contributions (referred to as
“usage”) of transcripts to the overall expression of a gene. A gene has a
DTUevent,thatis,isaDTUgene,whenatleasttwoofitstranscriptsare
differentially used between two groups. We found between 1,062 and
2,153 DTU genes in each of the four comparisons (Tables S8–S11). By
comparing DTU genes to DEGs, we found an overlap of less than 8% in
all four comparisons, indicating that the expression of most genes is
regulated either at the pre- or the post-transcriptional level (Fig 4)and
that only few genes are regulated at both levels. Furthermore, we
found that the proportion of DEGs to DTU genes in all group com-
parisons was 2:1 (Fig. 4A–C), except for SPD versus CTR, where this ratio
was inversed with more DTU genes than DEGs (Fig 4D).
DEGs and DTU genes are involved in different biological pathways
We used Ingenuity Pathway Analysis (IPA) to evaluate the molecular
functions of the DEGs and DTU genes in the different germ cell
types. In line with the small overlap between the DEG and DTU gene
sets, we found minor overlaps between the top 20 significantly
enriched molecular functions of DEGs and DTU genes in all four
groups (Fig 5). Both gene sets contained genes involved in orga-
nization of cytoskeleton/cytoplasm, microtubule dynamics, apo-
ptosis, necrosis, and segregation of chromosomes. IPA analysis on
DEGs highlighted functional enrichment annotations that can be
attributed to the most advanced germ cell type in each group
comparison (e.g., development of stem cells, segregation of
chromosomes) (Fig 5A). In comparison to the functional annota-
tions of DEGs, 26% of molecular functions of the DTU genes
overlapped across the four group comparisons (Fig 5B). Among the
overlapping terms were microtubule dynamics, organization of
cytoplasm, and cytoskeleton. More general biological functions
(e.g., RNA metabolism, cell survival) were enriched among the DTU
genes in each group comparison. To further classify the biological
pathways enriched among DEGs and DTU genes, we performed
pathway analysis via the Reactome Knowledgebase (Gillespie et al,
2022), which confirmed that germ cell–specific and general path-
ways are enriched among DEGs (Fig.S4) and DTU genes (Fig.S5),
respectively.
Germ cell type–dependent splicing is an additional layer of gene
regulation in the germline
To study alternatively spliced transcripts, we investigated the
transcript biotypes of selected DTU genes. In comparison to the
proportional distribution of transcript biotypes annotated in
GENCODE (Frankish et al, 2019), we found that most of the DTU
events, regardless of the group comparison, result in protein-
coding transcripts (Fig.6A). In the comparison between SPG and SPC
Figure 4. Comparison of differentially expressed
gene (DEG) and differential trascript usage (DTU)
gene numbers in all four group comparisons.
(A, B, C, D) Venn diagrams display number and
proportion of genes that are differentially expressed,
have a DTU event, or both in the (A) Sertoli cell–only
versus SPG, (B) SPG versus SPC, (C) SPC versus SPD, and
(D) SPD versus CTR group comparisons. Yellow =
differential gene expressions, blue = DTU genes.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 6of15
samples (Fig 6B), two protein-coding isoforms of SRY-Box Tran-
scription Factor 15 (SOX15) displayed differential usage without
changes in gene expression (Fig 6C). Although SOX15-201
(ENST00000250055.3) was the predominant isoform, with an aver-
age usage of 48% in SPC samples, SPG samples predominantly used
the SOX15-202 isoform (ENST00000538513.6), which has an
alternative 59splice site in the 59UTR region. Reverse transcriptase
quantitative PCR (RT-qPCR) analysis of SOX15 replicated both the
differential usage of SOX15-201 and the equal gene expression
levels between SPG and SPC samples (Fig S6A–D). Spermatogenesis
associated 4 (SPATA4) also showed a switch in usage for its protein-
coding isoforms SPATA4-201 (ENST00000280191.7) and SPATA4-203
Figure 5. Molecular functions of differentially expressed genes (DEGs) and differential trascript usage (DTU) genes.
(A, B) Heat maps displaying the molecular functions revealed by IPA of all (A) DEGs and (B) DTU genes per group comparisons according to the −log
10
P-values. The top 20
molecular functions of each group comparison with P-values < 0.01 were included. * Molecular functions enriched in both the DEG and DTU gene sets.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 7of15
(ENST00000515234.1) in the comparison of SPC versus SPD samples
(Fig 6D). SPC samples showed a significantly decreased usage of
SPATA4-201 and a significantly increased usage of SPATA4-203,
whereas SPD samples exclusively used the SPATA4-201 isoform.
These two isoforms use alternative stop sites. In contrast to SOX15,
SPATA4 was also a DEG in this group comparison and had a higher
expression level in SPD samples.
Intriguingly, the second largest group of biotypes with DTU
events were retained introns (Fig 6A). For synaptonemal complex
protein 3 (SYCP3), we found a significantly increased usage of the
Figure 6. Transcript biotypes with differential transcript usage (DTU) events.
(A) Stacked bar plots represent the relative amount of different transcript biotypes with DTU events in each of the four group comparisons compared with the transcript
biotype annotation from the GENCODE release 36 genome annotation based on the GRCh38.p13 genome reference (Frankish et al, 2019). (B) Schematic illustration of the
groups and the respective color codes. (C, D, E, F) Schematic representation of the transcript isoforms with a DTU event, which predominantly contribute to the relative
change in isoform usage (box plots of proportion), independent of gene expression (boxplots of normalized counts) in (C) SOX15,(D)SPATA4,(E)SYCP3,and (F) MKI67.P-
values refer to specific transcripts that significantly drive the change in isoform usage in geneswith anoveral l significant change in transcript usage. In (C, D, E, F), data are
represented as median (center line), upper/lower quartiles (box limits), 1.5× interquartile range (whiskers), and outliers (points). Likelihood ratio test: **P≤0.01, ***P≤
0.001. Exons/coding region = boxes, UTR = smaller boxes, introns = lines. SPG: n = 4; SPC: n = 3, SPD: n = 3.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 8of15
retained intron isoform SYCP3-204 (ENST00000478139.1) in SPG
samples, whereas SPC samples had an increased usage of the
protein-coding isoform SYCP3-202 (ENST00000392924.2; Fig 6E). In
this group comparison, SYCP3 showed increased expression in SPC
samples. We confirmed the increased usage of the retained intron
isoform together with the decreased expression in SPG samples
by RT-qPCR analysis (Fig S6E–H). A switch in usage from coding to
non-coding transcripts was also observed for marker of prolifer-
ation Ki-67 (MKI67), which did not show changes in gene expres-
sion (Fig 6F). However, the protein-coding isoform MKI67-202
(ENST00000368654.8) was less expressed in SPC samples in com-
parison to SPD samples. In contrast, the processed transcript
isoform MKI67-205 (ENST00000484853.1) showed significantly in-
creased usage in SPC samples and decreased usage in SPD
samples.
Identification of putative infertility genes
By making use of samples derived from infertility patients, we
aimed at identifying genes related to male infertility that have so far
been understudied. We analyzed genes with enriched expression in
SCO samples compared with SPG, SPG to SPC, SPC to SPD, and SPD to
CTR (genes in blue in Tables S2 and S4–S6). Analysis via the
Reactome Knowledgebase revealed that the most significant bio-
logical pathways enriched among the up-regulated genes in the
SCO group were GABA-related processes, for example, MECP2
regulates the transcription of genes involved in GABA signaling and
GABA synthesis (Fig S7A). A significant enrichment of genes involved
in the immune response was found up-regulated in SPG samples,
involving pathways for interferon and cytokine signaling (Fig S7B).
The most significant pathway enriched in up-regulated genes of
SPC samples was the regulation of IGF transport and uptake by IGF-
binding proteins (Fig S7C). In contrast, up-regulated genes in the
SPD group were most significantly enriched for metabolic pathways,
including rRNA processing in the mitochondrion and electron
transport from NADPH to ferredoxin (Fig S7D). We then evaluated
the cell type–specific expression of the most severe 50 putatively
misregulated genes in our scRNA-seq dataset. In all group com-
parisons, genes showed predominant expression in the somatic
cells (Fig S7E–H). Some genes stood out, such as those that were up-
regulated in SCO (RWDD2A,CCDC183,CNNM1) or SPD (SERF1B)
samples but, according to scRNA-seq, displayed a germ cell–spe-
cific or meiotic-specific expression pattern, respectively (Fig S7E
and H). According to normal tissue data available in the Genotype-
Tissue Expression (GTEx) portal (release v8, accessed on July 2022),
the exons of RWDD2A,CCDC183,CNNM1,and SERF1B are predomi-
nantly expressed in the testis (Fig S8).
Discussion
Reports on gene expression patterns in the testis are accumulating
rapidly, but a complete picture of the transcriptome of human germ
cells has remained unexplored. Here, we demonstrate that the
progression of human male germ cell differentiation is accom-
panied by major transcript dynamics, including germ cell
type–dependent transcription and splicing events; these splicing
events result in germ cell type–dependent transcript isoforms.
Because of the use of microarrays in previous studies, the full
spectrum of transcriptome profiles, including isoform information,
has remained largely unknown. Our systematic analysis of total RNA
from testicular biopsies with well-defined, distinct germ cell
compositions allowed us to identify highly germ cell–specific genes
that, to our knowledge, have not been previously associated with
the respective germ cell types in humans (Table S12).
In silico analyses of these putative infertility-related genes
pointed to potentially misregulated pathways. To date, none of the
identified germ cell–specific genes that were significantly up-
regulated in our infertility groups (RWDD2A,CCDC183,CNNM1,
SERF1B) had been linked to male reproductive health, rendering
genes revealed in this study potential candidates to investigate
their role in male infertility. Future studies will be necessary to
conclude whether the different expression of the genes is due to
misregulation or is secondary to the absence of specific sper-
matogenic cells.
The transcriptional output of a gene depends not only on the
level of RNA expression but also on post-transcriptional processing
of RNA transcripts, for instance, through AS, which allows a single
gene to originate different transcripts and potentially different
proteins (Baralle & Giudice, 2017). Although it is well known that the
testis is an organ with high transcriptome diversity, AS is still
understudied in human spermatogenesis. Making use of a powerful
bioinformatic technique, the DTU analysis, we were able to study,
for the first time, transcriptome changes at isoform resolution
during human spermatogenesis. Although several studies have
observed discontinuous patterns of transcription throughout
murine and human spermatogenesis (Jan et al, 2017;Vara et al,
2019), in our study, we further characterized the ongoing changes in
transcript levels during human spermatogenesis by identifying
between 1,062 and 2,153 genes whose transcripts were alternatively
spliced in different germ cell types. Our results indicate that al-
ternative splicing extends the transcriptome diversity in germ cells,
which already present high transcriptional activity, as we found that
alternative splicing events are more prevalent between the pre-
meiotic and meiotic germ cell types. As we identified more alter-
natively spliced genes than changes in gene expression between
the round spermatid arrest and control samples, we hypothesize
that in the final stage of spermiogenesis, transcriptome diversity
arises primarily from alternative splicing rather than by changes in
gene transcription. In line with this idea are studies in mice showing
that genes required for spermiogenesis are already expressed at
the beginning of meiosis (da Cruz et al, 2016) and that transcription
in elongated spermatids is decreased because of the highly
compacted chromatin structure (Sassone-Corsi, 2002). Even in the
absence of transcriptional activity in the nucleus, stored unpro-
cessed transcripts can maintain translational activity in late stages
of germ cell differentiation (Wang et al, 2020). Furthermore, our
study demonstrates that alternative splicing is uncoupled from the
level of gene expression during human spermatogenesis, as only a
minority of genes (3–8%) were both differentially expressed and
differentially spliced at each respective germ cell stage. Data on the
comparison of DEG and DTU genes in healthy and diseased muscle
and brain tissues also revealed a small overlap (Dick et al, 2020;
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 9of15
Marques-Coelho et al, 2021;Solovyeva et al, 2021). Whether this is
true for other tissues remains to be elucidated. Interestingly, we
found that DEGs were predominantly associated with germ cell–
specific processes, whereas DTU genes were involved in more
general biological processes, suggesting that during human
spermatogenesis, these functions are predominantly regulated at
transcriptional and post-transcriptional levels, respectively. We
suggest that general processes are uncoupled from the level of
gene expression as these need to be maintained even in tran-
scriptionally silent cells such as later germ cells.
By looking more closely into four DTU genes, we demonstrate the
importance of our dataset for further research in the field of male
infertility. For example, we were able to reveal that SPG and SPC
samples express different protein-coding transcripts of SOX15,
something that would have been overlooked by conventional DEG
analysis. Our findings demonstrate the importance of under-
standing which gene products with potentially different func-
tionality are produced by AS as it has been shown that this may play
a role in the etiology of several diseases (Scotti & Swanson, 2016)
such as cancer (Wiesner et al, 2015;Vitting-Seerup & Sandelin 2017).
How alterations in alternatively spliced transcript expression play a
role in the pathology of infertility remains to be assessed. We
showed that some crucial spermatogenic genes such as SYCP3
appear to be regulated at both the transcriptional and post-
transcriptional levels. SYCP3 is already expressed as an immature
non-coding transcript with a retained intron in SPG samples,
whereas the mature transcript is predominantly expressed in SPC
samples, suggesting intron retention is a mechanism to produce
transcripts required for later differentiation steps. Our hypothesis
is supported by a study in mice that showed intron retention
ensures timely and stage-dependent gene expression during
spermatogenesis (Naro et al, 2017). In humans, a previous study
indicated that spermatogonia already express genes required for
meiosis (Jan et al, 2017), but the mechanism behind this observation
was not addressed. Our data strongly highlight the need to further
analyze the splicing machinery in human germ cells.
Although we can report on germ cell–specific transcriptome
patterns that include non-coding RNAs and other RNA biotypes not
covered by existing scRNA-seq studies on the human testis, we
cannot address rRNAs because of rRNA depletion before total RNA
sequencing. Moreover, we included samples based on careful
histological examination and homogeneous histological pheno-
types rather than on underlying etiologies. Therefore, the changes
in gene expression we report can be confidently traced to the
presence or absence of certain germ cell types rather than, for
example, underlying genetic variants. For the same reason, we
cannot exclude a common effect of arrests on gene expression,
especially deriving from the interplay between different cell types.
In the future, it will be important to validate these findings in
healthy testicular tissue and discriminate between cell-specific and
arrest-specific gene expression patterns.
Our whole transcriptome analysis approach provides an unbi-
ased evaluation of transcriptome patterns during human sper-
matogenesis for novel and/or germ cell–specific genes. By not only
focusing on protein-coding exons but by capturing the presence of
all alternative transcripts at different germ cell stages, including
non-coding RNAs and splice variants, our dataset increases the
understanding of human spermatogenesis and its transcriptional
regulation. Our framework ultimately helps with the interpretation
of pathologic variants associated with male infertility.
Materials and Methods
Ethical approval
Male infertility patients included in this study underwent surgery
for microdissection testicular sperm extraction (mTESE; n = 15) or to
rule out a suspected malignant tumor (n = 1) at the Department of
Clinical and Surgical Andrology of the Centre of Reproductive
Medicine and Andrology, University Hospital of Münster. Each
patient gave written informed consent (ethical approval was ob-
tained from the Ethics Committee of the Medical Faculty of Münster
and the State Medical Board no. 2008-090-f-S) and one additional
testicular sample for the purpose of this study was obtained. Tissue
proportions were snap-frozen or fixed in Bouin’s solution.
Patient selection
In this study, we included testicular biopsies with a homogenous
histological phenotype in both testes from men showing SCO (SCO-
1/M1045, SCO-2/M911, SCO-3/M1742), spermatogenic arrests at the
spermatogonial (SPG-1/M1570, SPG-2/M1575, SPG-3/M1072, SPG-4/
M2822), spermatocyte (SPC-1/M1369, SPC-2/M799, SPC-3/M921), and
round spermatid stage (SPD-1/M2227, SPD-2/M1311, SPD-3/M1400)
(Table 1). For complete histological evaluation, the interstitium of
each biopsy was ranked with parameters describing the condition
of the tubular wall, Leydig cells, and lumen (Table S1). We excluded
patients with germ cell neoplasia and a history of cryptorchidism
and acute infections. For complete representation of the sper-
matogenic process, samples with qualitatively and quantitatively
normal spermatogenesis were included as controls (CTR) in this
study (CTR-1/M1544, CTR-2/M2224, CTR-3/M2234) obtained from
patients with obstructive azoospermia, for example, because of
congenital bilateral absence of the vas deferens (CBAVD; CTR-1),
anorgasmia (CTR-2) or because of suspected tumor that was not
confirmed (CTR-3). Before surgery, all patients underwent physical
evaluation, hormonal analysis of luteinizing hormone (LH), follicle-
stimulating hormone (FSH), and testosterone (T), and semen
analysis (World Health Organization, 2010). In addition to con-
ventional karyotyping and screening for azoospermia factor (AZF)
deletions, WES was performed for all patients, except for SPG-4
(who had undergone chemotherapy because of leukemia) and for
CTR-3. WES data were generated within the Male Reproductive
Genomics (MERGE) study as previously published (Wyrwoll et al,
2020) and were screened for variants in 230 candidate genes that
have at least a limited level of evidence for being associated with
male infertility according to a recent review (Houston et al, 2021). We
also included a screening in the recently published genes ADAD2,
GCNA,MAJIN,MSH4,MSH5,RAD21L1,RNF212,SHOC1,STAG3,SYCP2,
TERB1,TERB2,TRIM71,ATG4D,BRDT,CCDC155,CHD5,CTCFL,C11orf80,
C14orf39,DDX25,EXO1,GCNA,FBXO43,FKBPL,HENMT1,HFM1,HSF2,
KASH5,MAGEE2,MBOAT1,MCMDC2,MCM8,MCM9,MLH3,MOV10L1,
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 10 of 15
PDHA2,PIWIL2,PNLDC1,PSMC3IP,RBM5,REC8,RPL10L,SPATA22,
TDRD9,TDRKH,ZFX,ZSWIM7 which are associated with non-ob-
structive azoospermia (Riera-Escamilla et al, 2019;Krausz et al, 2020;
Schilit et al, 2020;Hardy et al, 2021;Salas-Huetos et al, 2021;Torres-
Fern´
andez et al, 2021;Wyrwoll et al, 2021). We screened for rare
(minor allele frequency [MAF] in gnomAD database < 0.01), possibly
pathogenic variants (stop-, frameshift-, splice-site variants, and
missense variants with a CADD score > 25) with a read depth > 10x,
which were detected in accordance with the reported mode of
inheritance in genes associated with non-syndromic infertility.
Histological evaluation of the human testicular biopsies
After overnight fixation in Bouin’s solution, the tissues were washed
in 70% ethanol, embedded in paraffin, and sectioned at 5 μm.
AppiClear (Cat# A4632.2500; Applichem) was used to dewax the
tissue section. The cellular composition of all testicular biopsies
(n = 16) was histologically examined on two periodic acid-Schiff
(PAS)-stained sections from two independent biopsies per testis.
For PAS staining, the sections were first incubated with 1% PA (Cat#
1.005.240.100; Sigma-Aldrich) and then in Schiffs reagent (Cat#
1.090.330.500; Sigma-Aldrich). Cell nuclei were counterstained with
Mayer’s hematoxylin solution (Cat# 1.092.490.500; Sigma-Aldrich).
After washing in tap water and dehydration through increasing
ethanol concentrations and AppiClear, slides were closed with
Merckoglas (Cat# 1.039730.001; Sigma-Aldrich). The slides were
scanned using the Precipoint Viewpoint software (Precipoint). The
biopsies were evaluated based on the Bergmann and Kliesch
scoring method (Bergmann & Kliesch, 2010), which assigns a score
from 0 to 10 to each patient according to the percentage of tubules
containing elongated spermatids. Furthermore, the percentage of
the seminiferous tubules with round spermatids, spermatocytes, or
spermatogonia as the most advanced germ cell type was assessed
and seminiferous tubules with SCO or hyalinized tubules (tubular
shadows) (Table 1).
Immunohistochemical and immunofluorescence stainings on
testicular tissue sections
Immunohistochemical (IHC) and immunofluorescence (IF) stain-
ings were performed as previously described (Di Persio et al, 2021).
After rehydration, heat-induced antigen retrieval in sodium citrate
buffer, pH 6.0, was performed. Incubation and washing steps were
performed at room temperature unless otherwise stated.
For IHC stainings, blocking of endogenous peroxidase activity
and of unspecific antibody binding was achieved using hydrogen
peroxide (Cat# GH06201; Hedinger) and goat serum (Cat# G6767-
100ML; Sigma-Aldrich) diluted in TBS containing bovine serum
albumin (Cat# A9647-50G; Sigma-Aldrich), respectively. Primary
antibodies for leucine zipper protein 4 gene (LUZP4, HPA046436,
1:50; Sigma-Aldrich), testis-specific protein Y-linked 4 (TSPY4,
HPA049384, dilution 1:20; Sigma-Aldrich), and high mobility group
box 4 (HMGB4, HPA035699, dilution 1:50; Sigma-Aldrich) were diluted
in blocking solution and incubated overnight at 4°C. Incubation
with unspecific immunoglobulin G (IgG) served as negative controls.
After this, sections were incubated with goat anti-rabbit biotin-
labeled secondary antibody (Cat# ab6012, dilution 1:100; Abcam) for
1 h, followed by a 45-min incubation step with streptavi-
din–horseradish peroxidase from Streptomyces avidinii (Cat# S5512;
Sigma-Aldrich). Detection of the peroxidase activity was achieved
by incubation with 3,30-diaminobenzidine tetrahydrochloride so-
lution (Cat# A0596.0001; Applichem) and stopped by washing in
double distilled water. Nuclei were counterstained with Mayer’s
hematoxylin. The sections were dehydrated with increasing ethanol
concentrations, cleared with AppiClear, and mounted under a glass
cover slip with Merckoglas. Digitalization of the sections was per-
formed with the Olympus BX61VS microscope and scanner software
VS-ASW-S6 (Olympus).
For IF stainings, tissues were incubated with 1M glycine (Cat#
G7126-500G; Sigma-Aldrich) and with a blocking solution containing
TWEEN-20 (Cat# 655205; Sigma-Aldrich) and sterilized donkey serum
(Cat# LIN-END9000-100; Biozol). Primary antibodies for TSPY4
(HPA049384, dilution 1:20; Sigma-Aldrich), undifferentiated em-
bryonic cell transcription factor 1 (UTF1, MAB4337, 1:20; Merck Mil-
lipore), and MAGE family member A4 (MAGEA4, Prof. G. C. Spagnoli,
University Hospital of Basel, CH, 1:20) were diluted in blocking
solution and incubated overnight at 4°C. Incubation with unspecific
IgG served as negative control. The next day, sections were washed
and incubated for 1 h with species-specific secondary antibodies
(donkey anti-rabbit Alexa 488, Cat# 711-546-152; Jackson Immuno-
Research; donkey anti-mouse Alexa 647, Cat# 715-606-150; Jackson
ImmunoResearch) diluted in blocking solution. Cell nuclei
were counterstained with 4,6-diamidino-2-phenylindole-dihydro-
chloride (DAPI, Cat# D9542-10MG, 1:1,000; Sigma-Aldrich) in TBS for
10 min. After a last washing step, slides were mounted with Vec-
tashield Mounting Media (Cat# VEC-H-1000; Vector Laboratories).
Digitalization of the sections was performed with the Olympus
BX61VS microscope and scanner software VS-ASW-S6 (Olympus).
After immunofluorescence analyses, TSPY4, MAGEA4, and UTF1
stained cells were quantified using Qupath 0.3.2 (Bankhead et al,
2017), as described by Di Persio et al (2021). The number of TSPY4+
cells among MAGEA4+ and UTF1+ cells was quantified in three in-
dependent patient samples with full spermatogenesis. The per-
centages of TSPY4+ cells per sample were calculated among 200
MAGEA4+ and UTF1+ cells, respectively.
RNA extraction from testicular tissues
We extracted total RNA from snap-frozen testicular tissues from all
biopsies using the Direct-zol RNA Microprep kit (Zymo Research)
according to manufacturer’s protocol. Quantity and quality of
isolated RNA were evaluated using RNA ScreenTape and the
TapeStation Analysis software 3.1.1 (Agilent Technologies, Inc.). All
samples had intact ribosomal 18S and 21S bands. Samples with an
RNA integrity number (RIN) > 3.6 were included in the analysis as for
human tissues, it has been shown that samples with much lower
RIN values (1 < RIN < 2) can have a sufficient number of reads and
pass quality control (Suntsova et al, 2019).
Library preparation and sequencing
Next-generation sequencing was performed by the service unit
Core Facility Genomics of the medical faculty at the University of
Münster. Libraries were prepared according to the NEBNext Ultra
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 11 of 15
RNA II directional Library Prep kit (New England Biolabs) after
NEBNext rRNA depletion (New England Biolabs). The NextSeq HO Kit
(Illumina Inc.) with 150 cycles was used for paired end sequencing
on the NextSeq 500 system (Illumina Inc.) with ~400 million single
reads per run.
Data processing
We processed the raw sequence data with the Nextflow analysis
pipeline nf-core/rnaseq 2.0 (Ewels et al, 2020) and annotated the
transcripts with GENCODE release 36 genome annotation based on
the GRCh38.p13 genome reference (Frankish et al, 2019). Gene ex-
pression counts were estimated using Salmon (Patro et al, 2017).
DEG analysis
All data were analyzed within the R Statistical Environment
(RCoreTeam, 2020). We used DESeq2 (Love et al, 2014) for analyzing
differentially expressed genes (DEGs) following the standard
workflow for Salmon quantification files. DESeq2 uses a generalized
linear model based on estimated size factors and dispersion to
calculate the log
2
fold changes for each gene (Love et al, 2014).
Annotation was performed using the biomaRt R package. Nor-
malization was performed using DESeq2 with the median of ratios
method (Love et al, 2014). Genes with a total count > 10 were
considered for further analysis. DEGs were calculated for each
group comparison, that is, SCO versus SPG, SPG versus SPC, SPC
versus SPD, and SPD versus CTR. P-values are calculated based on
Wald test and adjusted with Benjamini–Hochberg. Genes with a
false discovery rate (FDR) < 0.05 and a log
2
fold change (FC) ≥1 were
considered DEGs. Dispersion of samples was visualized using
DESeq2’sPCAplot function for the top 500 genes with a total count >
10.
To evaluate gene expression of selected genes of interest at
single-cell level, we generated uniform manifold approximation
and projection (UMAP) plots (McInnes et al, 2020 Preprint) based on
our previously published dataset (Di Persio et al, 2021) using the
tool Seurat (Stuart et al, 2019;Hao et al, 2021). We used the freely
available loupe cell browser (v4.0.0) from 10x Genomics, Inc. to
generate t-distributed stochastic neighbor embedding (t-SNE) plots
of selected genes in an additional scRNA-seq dataset (Hermann et
al, 2018).
Differential transcript usage analysis
For computing differential transcript usage (DTU), we employed the
R package DTUrtle (Tekath & Dugas, 2021), following the vignette
workflow for human bulk RNA-seq analysis. As for the DEG analysis,
we annotated the transcripts with GENCODE release 36 genome
annotation. We calculated DTU genes for each group comparison
(i.e., SCO versus SPG, SPG versus SPC, SPC versus SPD, and SPD
versus CTR) with the run_drimseq function. DTUrtle conducts sta-
tistical analyses based on DRIMSeq (Nowicka & Robinson, 2016),
that is, a likelihood ratio test is used on the estimated transcript
proportions and precision parameter (Tekath & Dugas, 2021). To
increase the statistical power of the analysis, we filtered out
transcripts with low impact, that is, less than 5% usage for all
samples or a corresponding total gene expression of less than five
counts for all samples before the statistical testing. Also, only genes
with at least two high impact transcripts were considered. From the
analysis, we obtained genes with an overall significant change in
transcript usage and the corresponding transcripts that drive the
change in usage in those genes (both with overall FDR < 0.05).
DTUrtle conducts a conservative selection of transcripts contrib-
uting to change in isoform usage by disregarding transcripts with a
potential priming bias (Tekath & Dugas, 2021).
To decrease the number of analyzed transcripts per DTU gene, a
post hoc filtering was applied; that is, transcripts whose propor-
tional expression deviated by less than 10% between samples were
excluded. In this study, we decided to only include transcripts that
fulfill the criterion that all samples from one group must have a
higher transcript usage compared with all samples from the other
group.
Pathway analyses
Molecular functions of DEGs and DTU genes were assessed using
Ingenuity Pathway Analysis (IPA; QIAGEN) and the Reactome
Knowledgebase v81 (Gillespie et al, 2022). A Benjamini–Hochberg
multiple testing correction P-value (FDR) < 0.01 was used as
threshold for significant molecular functions in IPA. We selected the
top 20 significant terms for molecular functions.
cDNA synthesis and quantitative PCR analysis of testicular tissues
cDNA was synthesized from 500 ng total RNA using the iScript cDNA
Synthesis Kit (Bio-Rad) according to the manufacturer’s instruc-
tions. cDNA was diluted 1:3 with nuclease-free water (QIAGEN). RT-
qPCR analyses were performed with PowerSYBR Green Mastermix
(Life Technologies GmbH, Applied Biosystems). 1.5 μl cDNA was used
for each 15 μl PCR reaction. The PCR program consisted of one cycle
of 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for
1 min on a StepOnePlus machine, and results were analyzed using
the StepOne software. Results for gene expression were normalized
to the reference gene GAPDH and are plotted as 2
−ΔCt
values (Livak &
Schmittgen, 2001;Schmittgen & Livak, 2008). DTUs were calculated
as the relative incidence of variants (RIV) (Camacho Londoño &
Philipp, 2016) based on the relation of the specific isoform to the
overall expression of its gene. Primer sequences and product sizes
are summarized in Table S13.
Statistical analysis
Statistical analysis was conducted as described in sections for DEG
analysis, differential transcript usage analysis, and pathway
analysis.
Data Availability
The testicular RNA-Seq data from this publication have been de-
posited in the European Genome-Phenome Archive and are
available under EGAS00001006135.
Transcriptome of human male germ cells Siebert-Kuss et al. https://doi.org/10.26508/lsa.202201633 vol 6 | no 2 | e202201633 12 of 15
Supplementary Information
Supplementary information is available at https://doi.org/10.26508/lsa.
202201633.
Acknowledgements
We thank Heidi Kersebom and Elke K ¨
oßer for histological evaluation
of testicular tissues and Karen Schiwon for support with histological
stainings. We also thank Sabine Forsthoff for excellent support in
endocrinological measurements. We thank the service unit Core Fa-
cility Genomik of the medical faculty from the University of Münster for
performing the next-generation sequencing. We thank Celeste Bren-
necka for her assistance with language editing. Schematic drawings of
testicular tissues in Figs 1,3,and6were created with BioRender.com.
This work was funded by the German research foundation (CRU362
grants to N Neuhaus (NE 2190/3-1, NE 2190/3-2), S Laurentino (LA 4064/
3-2), F Tüttelmann (TU 298/4-1, 4-2, 5-1, 5-2, 7-1), J Gromoll (GR 1547/24-
2), and a pilot project to H Krenz; individual research grant to S
Laurentino (LA 4064/4-1)) and by institutional funding by the CeRA. We
acknowledge support from the Open Access Publication Fund of the
University of Münster. The manuscript contains more specificinfor-
mation on the contribution of each author to the work.
Author Contributions
LM Siebert-Kuss: formal analysis, validation, visualization, and
writing—original draft, review, and editing.
H Krenz: data curation, software, formal analysis, visualization,
methodology, and writing—original draft.
T Tekath: data curation, software, methodology, and writing—original
draft.
MW
¨
oste: data curation, software, methodology, and wri-
ting—original draft.
S Di Persio: formal analysis, investigation, and writing—original
draft, review, and editing.
N Terwort: formal analysis, investigation, methodology, and wri-
ting—original draft.
MJ Wyrwoll: resources, data curation, formal analysis, investigation,
methodology, and writing—original draft, review, and editing.
J-F Cremers: resources, data curation, investigation, and wri-
ting—review and editing.
J Wistuba: formal analysis, investigation, methodology, and wri-
ting—review and editing.
M Dugas: software, methodology, and writing—review and editing.
S Kliesch: data curation, investigation, and writing—review and editing.
S Schlatt: resources, investigation, and writing—review and editing.
F Tüttelmann: conceptualization, data curation, formal analysis,
funding acquisition, and writing—original draft, review, and editing.
J Gromoll: conceptualization, funding acquisition, investigation,
methodology, and writing—original draft, review, and editing.
N Neuhaus: conceptualization, formal analysis, supervision, funding
acquisition, methodology, project administration, and writing—original
draft, review, and editing.
S Laurentino: conceptualization, supervision, funding acquisition,
investigation, visualization, methodology, project administration,
and writing—original draft, review, and editing.
Conflict of Interest Statement
The authors declare that they have no conflict of interest.
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