The single-cell chromatin accessibility landscape in mouse perinatal testis development

Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility

Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.

Inter- and trans-generational impacts of environmental exposures on the germline resolved at the single-cell level

Reproduction is a remarkably intricate process involving the interaction of multiple cell types and organ systems unfolding over long periods of time and that culminates with the production of gametes. The initiation of germ cell development takes place during embryogenesis but only completes decades later in humans. The complexity inherent to reproduction and its study has long hampered our ability to decipher how environmental agents disrupt this process. Single-cell approaches provide an opportunity for a deeper understanding of the action of toxicants on germline function and analyze how the response to their exposure is differentially distributed across tissues and cell types. In addition to single-cell RNA sequencing, other single-cell or nucleus level approaches such as ATAC-sequencing and multi-omics have expanded the strategies that can be implemented in reproductive toxicological studies to include epigenomic and the nuclear transcriptomic data. Here we will discuss the current state of single-cell technologies and how they can best be utilized to advance reproductive toxicological studies. We will then discuss case studies in two model organisms (Caenorhabditis elegans and mouse) studying different environmental exposures (alcohol and e-cigarettes respectively) to highlight the value of single-cell and single-nucleus approaches for reproductive biology and reproductive toxicology.

scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study, regions which are most enriched in GO terms related to neurogenesis, the clinical phenotype of AD, and SNPs identified in AD-associated GWAS.