Spermatogenesis generates mature male gametes and is critical for the proper transmission of genetic information between generations. However, the developmental landscapes of human spermatogenesis remain unknown. Here, we performed single-cell RNA sequencing (scRNA-seq) analysis for 2,854 testicular cells from donors with normal spermatogenesis and 174 testicular cells from one nonobstructive azoospermia (NOA) donor. A hierarchical model was established, which was characterized by the sequential and stepwise development of three spermatogonia subtypes, seven spermatocyte subtypes, and four spermatid subtypes. Further analysis identified several stage-specific marker genes of human germ cells, such as HMGA1, PIWIL4, TEX29, SCML1, and CCDC112. Moreover, we identified altered gene expression patterns in the testicular somatic cells of one NOA patient via scRNA-seq analysis, paving the way for further diagnosis of male infertility. Our work allows for the reconstruction of transcriptional programs inherent to sequential cell fate transition during human spermatogenesis and has implications for deciphering male-related reproductive disorders.

Highlights d Numbers of constitutive and inducible mRNAs scale with cell size d Coordination of RNAPII initiation rates with cell size underpins scaling d Amounts of DNA-bound RNAPII increase with cell size and are limiting d Transcription of constitutive and periodic mRNAs is a nonbursty Poisson process

The fission yeast Schizosaccharomyces pombe has been widely used to study eukaryotic cell biology, but almost all of this work has used derivatives of a single strain. We have studied 81 independent natural isolates and 3 designated laboratory strains of Schizosaccharomyces pombe. Schizosaccharomyces pombe varies significantly in size but shows only limited variation in proliferation in different environments compared with Saccharomyces cerevisiae. Nucleotide diversity, π, at a near neutral site, the central core of the centromere of chromosome II is approximately 0.7%. Approximately 20% of the isolates showed karyotypic rearrangements as detected by pulsed field gel electrophoresis and filter hybridization analysis. One translocation, found in 6 different isolates, including the type strain, has a geographically widespread distribution and a unique haplotype and may be a marker of an incipient speciation event. All of the other translocations are unique. Exploitation of this karyotypic diversity may cast new light on both the biology of telomeres and centromeres and on isolating mechanisms in single-celled eukaryotes.

Being infected by SARS-CoV-2 may cause damage to multiple organs in patients, such as the lung, liver and heart. Angiotensin-converting enzyme 2 (ACE2), reported as a SARS-CoV-2 receptor, is also expressed in human male testes. This suggests a potential risk in human male reproductive system. However, the characteristics of ACE2-positive cells and the expression of other SARS-CoV-2 process-related genes are still worthy of further investigation. Here, we performed singlecell RNA seq (scRNA-seq) analysis on 853 male embryo primordial germ cells (PGCs) and 2,854 normal testis cells to assess the effects of the SARS-CoV-2 virus on the male reproductive system from embryonic stage to adulthood. We also collected and constructed the scRNA-seq library on 228 Sertoli cells from three non-obstructive azoospermia (NOA) patients to assess the effects at disease state. We found that ACE2 expressing cells existed in almost all testis cell types and Sertoli cells had highest expression level and positive cells ratio. Moreover, ACE2 was also expressed in human male PGCs. In adulthood, the level of ACE2 expression decreased with the increase of age. We also found that ACE2 positive cells had high expressions of stress response and immune activation-related genes. Interestingly, some potential SARS-CoV-2 process-related genes such as TMPRSS2, BSG, CTSL and CTSB had different expression patterns in the same cell type. Furthermore, ACE2 expression level in NOA donors' Sertoli cells was significantly decreased. Our work would help to assess the risk of SARS-CoV-2 infection in the male reproductive system. . Single-cell transcriptome analysis of the novel coronavirus (SARS-CoV-2) associated gene ACE2 expression in normal and non-obstructive azoospermia (NOA) human male testes. Sci China Life Sci 63, https://doi.

20 21Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that 22 contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins 23 this variability, but is challenging to study genome-wide. Here we examine the transcriptomes 24 of >2000 single fission yeast cells in various environmental conditions by combining imaging, 25 single-cell RNA sequencing (scRNA-seq), and Bayesian true count recovery. We identify sets of 26 highly variable genes during rapid proliferation in constant conditions. By integrating scRNA-27 seq and cell-size data, we provide unique insights into genes regulated during cell growth and 28 division, including genes whose expression does not scale with cell size. We further analyse 29 the heterogeneity of gene expression during adaptive and acute responses to changing 30 environments. Entry into stationary phase is preceded by a gradual, synchronised adaptation 31 in gene regulation, followed by highly variable gene expression when growth decreases. 32Conversely, a sudden and acute heat-shock leads to a stronger, coordinated response and 33 adaptation across cells. This analysis reveals that the magnitude of global gene expression 34 heterogeneity is regulated in response to different physiological conditions within populations 35 of a unicellular eukaryote. 37Gene expression is tightly regulated at multiple levels, including chromatin structure, transcription, 38 mRNA degradation and translation. This multi-layered process underpins robust and timely expression 39 of single proteins as well as coordinated regulation of entire genetic programmes including dozens of 40 genes. Yet, even in constant environments, expression of specific genes varies between genetically 41 identical cells, leading to cell-to-cell heterogeneity in mRNA numbers and concentrations 1-3 . Cell-to-cell 42 variability in gene expression results from different phenomena. First of all, the random timing of 43 biological reactions makes transcription intrinsically stochastic. This form of variability, also called 44 intrinsic noise, is gene specific and depends on promoter sequence and chromatin states 4,5 . 45 Heterogeneity in quantitative traits such as cell size, growth rate, or concentration of transcription factors 46 also shapes gene expression variability in complex, non-trivial ways. This form of variability is not 47 entirely stochastic and depends on other single-cell attributes that affect biomolecule numbers 6,7 . 48 Furthermore, cells can enter dynamic cellular states characterised by specific gene expression 49 programmes. Examples are progression through the cell cycle or the adoption of distinct metabolic 50 states 8 . Different states co-exist in cell populations or tissues leading to dynamic, yet deterministic, cell-51 to-cell variability in gene expression. Finally, cells in metazoan tissues belong to different cell types that 52 are important for organ architecture and function. Although reversible and plastic, this form of 53 individuality i...

Motivation Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. Results Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. Availability and implementation The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. Supplementary information Supplementary data are available at Bioinformatics online.

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