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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?

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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/).

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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

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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.

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Author notes

  1. Samuele Soraggi and Meritxell Riera shared first author.

Authors and Affiliations

  1. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

    Samuele Soraggi, Meritxell Riera & Mikkel H. Schierup

  2. Department of Growth and Reproduction, GR-5064, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, DK-2100, Copenhagen, Denmark

    Ewa Rajpert-De Meyts & Kristian Almstrup

  3. International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    Ewa Rajpert-De Meyts & Kristian Almstrup

Authors
  1. Samuele Soraggi
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  2. Meritxell Riera
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  3. Ewa Rajpert-De Meyts
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  4. Mikkel H. Schierup
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Correspondence to Kristian Almstrup.

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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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Soraggi, S., Riera, M., Rajpert-De Meyts, E. et al. Evaluating genetic causes of azoospermia: What can we learn from a complex cellular structure and single-cell transcriptomics of the human testis?. Hum Genet 140, 183–201 (2021). https://doi.org/10.1007/s00439-020-02116-8

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  • DOI: https://doi.org/10.1007/s00439-020-02116-8

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