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RNA splicing programs define tissue compartments and cell types at single-cell resolution
The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for dis...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563012/ https://www.ncbi.nlm.nih.gov/pubmed/34515025 http://dx.doi.org/10.7554/eLife.70692 |
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author | Olivieri, Julia Eve Dehghannasiri, Roozbeh Wang, Peter L Jang, SoRi de Morree, Antoine Tan, Serena Y Ming, Jingsi Ruohao Wu, Angela Quake, Stephen R Krasnow, Mark A Salzman, Julia |
author_facet | Olivieri, Julia Eve Dehghannasiri, Roozbeh Wang, Peter L Jang, SoRi de Morree, Antoine Tan, Serena Y Ming, Jingsi Ruohao Wu, Angela Quake, Stephen R Krasnow, Mark A Salzman, Julia |
author_sort | Olivieri, Julia Eve |
collection | PubMed |
description | The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically. |
format | Online Article Text |
id | pubmed-8563012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-85630122021-11-04 RNA splicing programs define tissue compartments and cell types at single-cell resolution Olivieri, Julia Eve Dehghannasiri, Roozbeh Wang, Peter L Jang, SoRi de Morree, Antoine Tan, Serena Y Ming, Jingsi Ruohao Wu, Angela Quake, Stephen R Krasnow, Mark A Salzman, Julia eLife Computational and Systems Biology The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically. eLife Sciences Publications, Ltd 2021-09-13 /pmc/articles/PMC8563012/ /pubmed/34515025 http://dx.doi.org/10.7554/eLife.70692 Text en © 2021, Olivieri et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Olivieri, Julia Eve Dehghannasiri, Roozbeh Wang, Peter L Jang, SoRi de Morree, Antoine Tan, Serena Y Ming, Jingsi Ruohao Wu, Angela Quake, Stephen R Krasnow, Mark A Salzman, Julia RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title | RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title_full | RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title_fullStr | RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title_full_unstemmed | RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title_short | RNA splicing programs define tissue compartments and cell types at single-cell resolution |
title_sort | rna splicing programs define tissue compartments and cell types at single-cell resolution |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563012/ https://www.ncbi.nlm.nih.gov/pubmed/34515025 http://dx.doi.org/10.7554/eLife.70692 |
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