Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains
- Washington University School of Medicine, United States
- University of Oxford, United Kingdom
- Oregon Health and Science University, United States
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 analyse 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 analysing mutant and wild-type cells we decomposed our data into 46 components that identify novel meiotic gene-regulatory programmes, 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.
Data availability
Raw data and processed files for Drop-seq and 10X Genomics experiments are available in GEO under accession number: GSE113293
-
A single-cell atlas of testis gene expression from 5 mouse strainsNCBI Gene Expression Omnibus, GSE113293.
-
Novel Insights into the Downstream Pathways and Targets Controlled by Transcription Factors CREM in the TestisPLoS One, doi:10.1371/journal.pone.0031798.s009.
-
RFX2 Is a Major Transcriptional Regulator of Spermiogenesis.PLoS Genetics, doi:10.1371/journal.pgen.1005368.s011.
Article and author information
Author details
Funding
Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD078641)
- Donald F Conrad
National Institute of Mental Health (R01MH101810)
- Donald F Conrad
Wellcome (098387/Z/12/Z)
- Simon R Myers
Wellcome (109109/Z/15/Z)
- Daniel Wells
European Research Council (617306)
- Jonathan Marchini
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Deborah Bourc'his, Institut Curie, France
Ethics
Animal experimentation: All animal experiments were performed in compliance with the regulations of the Animal Studies Committee at Washington University in St. Louis under approved protocol #20160089.
Version history
- Received: November 28, 2018
- Accepted: June 17, 2019
- Accepted Manuscript published: June 25, 2019 (version 1)
- Version of Record published: July 9, 2019 (version 2)
Copyright
© 2019, Jung et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 9,067
- views
-
- 921
- downloads
-
- 98
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains
eLife 8:e43966.
https://doi.org/10.7554/eLife.43966
Further reading
-
- Computational and Systems Biology
- Developmental Biology
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
