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TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing

Single-cell RNA-sequencing technologies are ideally placed to resolve intratumoral heterogeneity. However, the lack of coverage across key mutation hotspots has precluded the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a p...

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Detalles Bibliográficos
Autores principales: Rodriguez-Meira, Alba, O’Sullivan, Jennifer, Rahman, Haseeb, Mead, Adam J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757016/
https://www.ncbi.nlm.nih.gov/pubmed/33377019
http://dx.doi.org/10.1016/j.xpro.2020.100125
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author Rodriguez-Meira, Alba
O’Sullivan, Jennifer
Rahman, Haseeb
Mead, Adam J.
author_facet Rodriguez-Meira, Alba
O’Sullivan, Jennifer
Rahman, Haseeb
Mead, Adam J.
author_sort Rodriguez-Meira, Alba
collection PubMed
description Single-cell RNA-sequencing technologies are ideally placed to resolve intratumoral heterogeneity. However, the lack of coverage across key mutation hotspots has precluded the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a protocol for TARGETed high-sensitivity single-cell mutational analysis with extremely low allelic dropout rates, parallel RNA SEQuencing, and cell-surface proteomics. Here, we present a detailed step-by-step protocol for TARGET-seq, including troubleshooting tips, approaches for automation, and methods for high-throughput multiplexing of libraries. For complete details on the use and execution of this protocol, please refer to Rodriguez-Meira et al. (2019).
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spelling pubmed-77570162020-12-28 TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing Rodriguez-Meira, Alba O’Sullivan, Jennifer Rahman, Haseeb Mead, Adam J. STAR Protoc Protocol Single-cell RNA-sequencing technologies are ideally placed to resolve intratumoral heterogeneity. However, the lack of coverage across key mutation hotspots has precluded the correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a protocol for TARGETed high-sensitivity single-cell mutational analysis with extremely low allelic dropout rates, parallel RNA SEQuencing, and cell-surface proteomics. Here, we present a detailed step-by-step protocol for TARGET-seq, including troubleshooting tips, approaches for automation, and methods for high-throughput multiplexing of libraries. For complete details on the use and execution of this protocol, please refer to Rodriguez-Meira et al. (2019). Elsevier 2020-10-21 /pmc/articles/PMC7757016/ /pubmed/33377019 http://dx.doi.org/10.1016/j.xpro.2020.100125 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Rodriguez-Meira, Alba
O’Sullivan, Jennifer
Rahman, Haseeb
Mead, Adam J.
TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title_full TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title_fullStr TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title_full_unstemmed TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title_short TARGET-Seq: A Protocol for High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing
title_sort target-seq: a protocol for high-sensitivity single-cell mutational analysis and parallel rna sequencing
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757016/
https://www.ncbi.nlm.nih.gov/pubmed/33377019
http://dx.doi.org/10.1016/j.xpro.2020.100125
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