Unified single-cell analysis of testis gene regulation and pathology in five mouse strains

Abstract

To fully exploit the potential of single-cell functional genomics in the study of development and disease, robust methods are needed to simplify the analysis of data across samples, time-points and individuals. Here we introduce a model-based factor analysis method, SDA, to analyze a novel 57,600 cell dataset from the testes of wild-type mice and mice with gonadal defects due to disruption of the genes Mlh3, Hormad1, Cul4a or Cnp. By jointly analyzing mutant and wild-type cells we decomposed our data into 46 components that identify novel meiotic gene-regulatory programs, mutant-specific pathological processes, and technical effects, and provide a framework for imputation. We identify, de novo, DNA sequence motifs associated with individual components that define temporally varying modes of gene expression control. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/- and Hormad1-/- mice, an area typically associated with immune privilege.

HELLS and PRDM9 form a Pioneer Complex to Open Chromatin at Meiotic Recombination Hotspots

“…It is possible that this unique combination of histone modifications provides an epigenomic addressing system for recruiting downstream DSBs and recombination factors to hotspots. One putative hotspot epigenomic reader could be ZCWPW1, which has domains predicted to bind H3K4me3 and H3K36me3 and is required for meiotic progression in males (Jung et al, 2019;Li et al, 2019).…”

“…Second, single-cell transcript analysis found that individual cells that co-express Hells and Prdm9 are restricted to leptotene; in contrast, Ino80 is expressed more broadly during meiosis ( Fig. S2) (Jung et al, 2019).…”

ZCWPW1 is recruited to recombination hotspots by PRDM9, and is essential for meiotic double strand break repair

“…Using single cell RNA-sequencing of mouse testis, we identified a set of genes co-expressed in (pre)leptotene cells which are highly enriched for genes involved in meiotic recombination (Jung et al, 2019). Zcwpw1, which ranks 3 rd in this set after Prdm9 (2 nd ), is of unknown function but contains two recognised protein domains: CW and PWWP, shown to individually bind H3K4me3 and H3K36me3 respectively (F. He et al, 2010;Rona et al, 2016).…”

“…Zcwpw1, which ranks 3 rd in this set after Prdm9 (2 nd ), is of unknown function but contains two recognised protein domains: CW and PWWP, shown to individually bind H3K4me3 and H3K36me3 respectively (F. He et al, 2010;Rona et al, 2016). This raises the attractive possibility that ZCWPW1 might recognize and physically associate with the same marks deposited by PRDM9 (Jung et al, 2019).…”

Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

“…To this end, in very high-dimensional scRNA-Seq factorizations and rotations of the gene expression matrices such as principal component analysis (PCA) or its variant singular value decomposition (SVD) [4][5][6] , and in case of multiple samples related canonical correlation analysis (CCA), are applied 7,8 . Easier interpretability of matrix factorization in scRNA-Seq has been achieved through enforcing sparsity of weights in sparse decomposition of arrays (SDA) 9,10 . Non-negative matrix factorization (NMF) and derived methods specifically account for the inherent non-negativity of gene expression by decomposing the expression matrix into matrices with strictly positive values 11 and are accordingly performed in single cell sequencing analysis [12][13][14] .…”