Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types
Abstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tis...
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| Format: | Article |
| Language: | English |
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BMC
2020-11-01
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| Series: | Biology of Sex Differences |
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| Online Access: | http://link.springer.com/article/10.1186/s13293-020-00335-2 |
| _version_ | 1818210644648787968 |
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| author | Tianyuan Lu Jessica C. Mar |
| author_facet | Tianyuan Lu Jessica C. Mar |
| author_sort | Tianyuan Lu |
| collection | DOAJ |
| description | Abstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities. |
| first_indexed | 2024-12-12T05:19:53Z |
| format | Article |
| id | doaj.art-f7125051f2ba4050bbbead509afc14ac |
| institution | Directory Open Access Journal |
| issn | 2042-6410 |
| language | English |
| last_indexed | 2024-12-12T05:19:53Z |
| publishDate | 2020-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Biology of Sex Differences |
| spelling | doaj.art-f7125051f2ba4050bbbead509afc14ac2022-12-22T00:36:39ZengBMCBiology of Sex Differences2042-64102020-11-0111112010.1186/s13293-020-00335-2Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell typesTianyuan Lu0Jessica C. Mar1Australian Institute for Bioengineering and Nanotechnology, The University of QueenslandAustralian Institute for Bioengineering and Nanotechnology, The University of QueenslandAbstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.http://link.springer.com/article/10.1186/s13293-020-00335-2Single cell RNA sequencingSex dimorphismDifferential expressionDifferential distributionCell clusterTranscription regulatory network |
| spellingShingle | Tianyuan Lu Jessica C. Mar Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types Biology of Sex Differences Single cell RNA sequencing Sex dimorphism Differential expression Differential distribution Cell cluster Transcription regulatory network |
| title | Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types |
| title_full | Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types |
| title_fullStr | Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types |
| title_full_unstemmed | Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types |
| title_short | Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types |
| title_sort | investigating transcriptome wide sex dimorphism by multi level analysis of single cell rna sequencing data in ten mouse cell types |
| topic | Single cell RNA sequencing Sex dimorphism Differential expression Differential distribution Cell cluster Transcription regulatory network |
| url | http://link.springer.com/article/10.1186/s13293-020-00335-2 |
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