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In situ 10-cell RNA sequencing in tissue and tumor biopsy samples

Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with a...

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Autores principales: Singh, Shambhavi, Wang, Lixin, Schaff, Dylan L., Sutcliffe, Matthew D., Koeppel, Alex F., Kim, Jungeun, Onengut-Gumuscu, Suna, Park, Kwon-Sik, Zong, Hui, Janes, Kevin A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426952/
https://www.ncbi.nlm.nih.gov/pubmed/30894605
http://dx.doi.org/10.1038/s41598-019-41235-9
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author Singh, Shambhavi
Wang, Lixin
Schaff, Dylan L.
Sutcliffe, Matthew D.
Koeppel, Alex F.
Kim, Jungeun
Onengut-Gumuscu, Suna
Park, Kwon-Sik
Zong, Hui
Janes, Kevin A.
author_facet Singh, Shambhavi
Wang, Lixin
Schaff, Dylan L.
Sutcliffe, Matthew D.
Koeppel, Alex F.
Kim, Jungeun
Onengut-Gumuscu, Suna
Park, Kwon-Sik
Zong, Hui
Janes, Kevin A.
author_sort Singh, Shambhavi
collection PubMed
description Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with an improved preamplification procedure enabling RNA sequencing of 10 microdissected cells. This in situ 10-cell RNA sequencing (10cRNA-seq) can exploit fluorescent reporters of cell type in genetically engineered mice and is compatible with freshly cryoembedded clinical biopsies from patients. Through recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies and a 50% detection sensitivity of ~45 copies per 10-cell reaction. By using small pools of microdissected cells, 10cRNA-seq improves technical per-cell reliability and sensitivity beyond existing approaches for single-cell RNA sequencing (scRNA-seq). Detection of low-abundance transcripts by 10cRNA-seq is comparable to random 10-cell groups of scRNA-seq data, suggesting no loss of gene recovery when cells are isolated in situ. Combined with existing approaches to deconvolve small pools of cells, 10cRNA-seq offers a reliable, unbiased, and sensitive way to measure cell-state heterogeneity in tissues and tumors.
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spelling pubmed-64269522019-03-28 In situ 10-cell RNA sequencing in tissue and tumor biopsy samples Singh, Shambhavi Wang, Lixin Schaff, Dylan L. Sutcliffe, Matthew D. Koeppel, Alex F. Kim, Jungeun Onengut-Gumuscu, Suna Park, Kwon-Sik Zong, Hui Janes, Kevin A. Sci Rep Article Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with an improved preamplification procedure enabling RNA sequencing of 10 microdissected cells. This in situ 10-cell RNA sequencing (10cRNA-seq) can exploit fluorescent reporters of cell type in genetically engineered mice and is compatible with freshly cryoembedded clinical biopsies from patients. Through recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies and a 50% detection sensitivity of ~45 copies per 10-cell reaction. By using small pools of microdissected cells, 10cRNA-seq improves technical per-cell reliability and sensitivity beyond existing approaches for single-cell RNA sequencing (scRNA-seq). Detection of low-abundance transcripts by 10cRNA-seq is comparable to random 10-cell groups of scRNA-seq data, suggesting no loss of gene recovery when cells are isolated in situ. Combined with existing approaches to deconvolve small pools of cells, 10cRNA-seq offers a reliable, unbiased, and sensitive way to measure cell-state heterogeneity in tissues and tumors. Nature Publishing Group UK 2019-03-20 /pmc/articles/PMC6426952/ /pubmed/30894605 http://dx.doi.org/10.1038/s41598-019-41235-9 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Singh, Shambhavi
Wang, Lixin
Schaff, Dylan L.
Sutcliffe, Matthew D.
Koeppel, Alex F.
Kim, Jungeun
Onengut-Gumuscu, Suna
Park, Kwon-Sik
Zong, Hui
Janes, Kevin A.
In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title_full In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title_fullStr In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title_full_unstemmed In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title_short In situ 10-cell RNA sequencing in tissue and tumor biopsy samples
title_sort in situ 10-cell rna sequencing in tissue and tumor biopsy samples
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426952/
https://www.ncbi.nlm.nih.gov/pubmed/30894605
http://dx.doi.org/10.1038/s41598-019-41235-9
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