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Evaluating genetic causes of azoospermia: What can we learn from a complex cellular structure and single-cell transcriptomics of the human testis?

  • Samuele Soraggi
  • Meritxell Riera
  • Ewa Rajpert-De Meyts
  • Mikkel H. Schierup
  • Kristian AlmstrupEmail author
Review
Part of the following topical collections:
  1. Molecular genetics of male infertility

Abstract

Azoospermia is a condition defined as the absence of spermatozoa in the ejaculate, but the testicular phenotype of men with azoospermia may be very variable, ranging from full spermatogenesis, through arrested maturation of germ cells at different stages, to completely degenerated tissue with ghost tubules. Hence, information regarding the cell-type-specific expression patterns is needed to prioritise potential pathogenic variants that contribute to the pathogenesis of azoospermia. Thanks to technological advances within next-generation sequencing, it is now possible to obtain detailed cell-type-specific expression patterns in the testis by single-cell RNA sequencing. However, to interpret single-cell RNA sequencing data properly, substantial knowledge of the highly sophisticated data processing and visualisation methods is needed. Here we review the complex cellular structure of the human testis in different types of azoospermia and outline how known genetic alterations affect the pathology of the testis. We combined the currently available single-cell RNA sequencing datasets originating from the human testis into one dataset covering 62,751 testicular cells, each with a median of 2637 transcripts quantified. We show what effects the most common data-processing steps have, and how different visualisation methods can be used. Furthermore, we calculated expression patterns in pseudotime, and show how splicing rates can be used to determine the velocity of differentiation during spermatogenesis. With the combined dataset we show expression patterns and network analysis of genes known to be involved in the pathogenesis of azoospermia. Finally, we provide the combined dataset as an interactive online resource where expression of genes and different visualisation methods can be explored (https://testis.cells.ucsc.edu/).

Abbreviations

ART

Assisted reproductive techniques

AZF

Azoospermia-factor

CAVD

Congenital absence of the vas deferens

DEG

Differentially expressed gene

GEMINI

GEnetics of Male INfertility Initiative

GWAS

Genome-wide association studies

ICSI

Intracytoplasmic sperm injection

IMiGC

International Male infertility Genomics Consortium

INSL3

Insulin-like 3

KS

Klinefelter syndrome

MNN

Mutual nearest neighbour

NOA

Non-obstructive azoospermia

OA

Obstructive azoospermia

PCA

Principal component analysis

SCO

Sertoli-cell-only

SCOS

Sertoli-cell-only syndrome

scRNAseq

Single-cell RNA sequencing

SOM

Self-organizing maps

SPA

Spermatocytic arrest

TESE

Testicular sperm extraction

tSNE

T-distributed stochastic nearest neighbour

UMAP

Uniform manifold approximation

UMI

Unique molecular identifier

WES

Whole-exome sequencing

WGS

Whole-genome sequencing

WHO

World Health Organisation

Notes

Acknowledgements

The authors thank the editor, Prof. Csilla Krausz and the anonymous referees for their constructive comments and suggestions.

Funding

The Danish Council for Independent Research | Natural Sciences (Grant Number 6108-00385A), and the Novo Nordisk Foundation (Grant Number NNF17OC0031004) to MHS.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

439_2020_2116_MOESM1_ESM.pdf (197 kb)
Supplementary file1 (PDF 196 kb)
439_2020_2116_MOESM2_ESM.xlsx (12 kb)
Supplementary file2 (XLSX 11 kb)
439_2020_2116_MOESM3_ESM.xlsx (1.2 mb)
Supplementary file3 (XLSX 1202 kb)
439_2020_2116_MOESM4_ESM.pdf (626 kb)
Supplementary Figure 1: Mean normalized unspliced and spliced counts in 20 bins along pseudotime for four selected genes, with error bars representing confidence intervals. Except for ZMYND15, all genes show unspliced transcripts preceding spliced transcripts, as expected. It is worthwhile to note that ZMYND15 shows the smallest maximum and mean counts for unspliced transcripts, thus subjected to more sampling error (see Supplementary Table 3) (PDF 625 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Bioinformatics Research CentreAarhus UniversityAarhusDenmark
  2. 2.Department of Growth and ReproductionGR-5064, Rigshospitalet, University of CopenhagenCopenhagenDenmark
  3. 3.International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC)Rigshospitalet, University of CopenhagenCopenhagenDenmark

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