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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational t...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784859/ https://www.ncbi.nlm.nih.gov/pubmed/29227470 http://dx.doi.org/10.1038/nbt.4042 |
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author | Kang, Hyun Min Subramaniam, Meena Targ, Sasha Nguyen, Michelle Maliskova, Lenka McCarthy, Elizabeth Wan, Eunice Wong, Simon Byrnes, Lauren Lanata, Cristina Gate, Rachel Mostafavi, Sara Marson, Alexander Zaitlen, Noah Criswell, Lindsey A Ye, Chun Jimmie |
author_facet | Kang, Hyun Min Subramaniam, Meena Targ, Sasha Nguyen, Michelle Maliskova, Lenka McCarthy, Elizabeth Wan, Eunice Wong, Simon Byrnes, Lauren Lanata, Cristina Gate, Rachel Mostafavi, Sara Marson, Alexander Zaitlen, Noah Criswell, Lindsey A Ye, Chun Jimmie |
author_sort | Kang, Hyun Min |
collection | PubMed |
description | Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN-β and perform eQTL analysis on 23 pooled samples. |
format | Online Article Text |
id | pubmed-5784859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-57848592018-06-11 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation Kang, Hyun Min Subramaniam, Meena Targ, Sasha Nguyen, Michelle Maliskova, Lenka McCarthy, Elizabeth Wan, Eunice Wong, Simon Byrnes, Lauren Lanata, Cristina Gate, Rachel Mostafavi, Sara Marson, Alexander Zaitlen, Noah Criswell, Lindsey A Ye, Chun Jimmie Nat Biotechnol Article Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN-β and perform eQTL analysis on 23 pooled samples. 2017-12-11 2018-01 /pmc/articles/PMC5784859/ /pubmed/29227470 http://dx.doi.org/10.1038/nbt.4042 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Kang, Hyun Min Subramaniam, Meena Targ, Sasha Nguyen, Michelle Maliskova, Lenka McCarthy, Elizabeth Wan, Eunice Wong, Simon Byrnes, Lauren Lanata, Cristina Gate, Rachel Mostafavi, Sara Marson, Alexander Zaitlen, Noah Criswell, Lindsey A Ye, Chun Jimmie Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title | Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title_full | Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title_fullStr | Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title_full_unstemmed | Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title_short | Multiplexed droplet single-cell RNA-sequencing using natural genetic variation |
title_sort | multiplexed droplet single-cell rna-sequencing using natural genetic variation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784859/ https://www.ncbi.nlm.nih.gov/pubmed/29227470 http://dx.doi.org/10.1038/nbt.4042 |
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