Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Sing...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2020-01-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/51254 |
| _version_ | 1818019860821573632 |
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| author | Christopher A Jackson Dayanne M Castro Giuseppe-Antonio Saldi Richard Bonneau David Gresham |
| author_facet | Christopher A Jackson Dayanne M Castro Giuseppe-Antonio Saldi Richard Bonneau David Gresham |
| author_sort | Christopher A Jackson |
| collection | DOAJ |
| description | Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions. |
| first_indexed | 2024-04-14T07:57:18Z |
| format | Article |
| id | doaj.art-21850d8c56ad4b6cb1b3e482f06ce76e |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-14T07:57:18Z |
| publishDate | 2020-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-21850d8c56ad4b6cb1b3e482f06ce76e2022-12-22T02:04:59ZengeLife Sciences Publications LtdeLife2050-084X2020-01-01910.7554/eLife.51254Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environmentsChristopher A Jackson0https://orcid.org/0000-0002-8769-2710Dayanne M Castro1Giuseppe-Antonio Saldi2Richard Bonneau3https://orcid.org/0000-0003-4354-7906David Gresham4https://orcid.org/0000-0002-4028-0364Center For Genomics and Systems Biology, New York University, New York, United States; Department of Biology, New York University, New York, United StatesDepartment of Biology, New York University, New York, United StatesDepartment of Biology, New York University, New York, United StatesCenter For Genomics and Systems Biology, New York University, New York, United States; Department of Biology, New York University, New York, United States; Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, United States; Center For Data Science, New York University, New York, United States; Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, United StatesCenter For Genomics and Systems Biology, New York University, New York, United States; Department of Biology, New York University, New York, United StatesUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.https://elifesciences.org/articles/51254single cell RNA sequencinggene regulatory networkstranscription factors |
| spellingShingle | Christopher A Jackson Dayanne M Castro Giuseppe-Antonio Saldi Richard Bonneau David Gresham Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments eLife single cell RNA sequencing gene regulatory networks transcription factors |
| title | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| title_full | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| title_fullStr | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| title_full_unstemmed | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| title_short | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| title_sort | gene regulatory network reconstruction using single cell rna sequencing of barcoded genotypes in diverse environments |
| topic | single cell RNA sequencing gene regulatory networks transcription factors |
| url | https://elifesciences.org/articles/51254 |
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