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Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD
The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104628/ https://www.ncbi.nlm.nih.gov/pubmed/36762572 http://dx.doi.org/10.1002/advs.202204113 |
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author | Zheng, Zetian Chen, Junyi Chen, Xingjian Huang, Lei Xie, Weidun Lin, Qiuzhen Li, Xiangtao Wong, Ka‐Chun |
author_facet | Zheng, Zetian Chen, Junyi Chen, Xingjian Huang, Lei Xie, Weidun Lin, Qiuzhen Li, Xiangtao Wong, Ka‐Chun |
author_sort | Zheng, Zetian |
collection | PubMed |
description | The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat‐resistant while Gefitinib‐sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. |
format | Online Article Text |
id | pubmed-10104628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101046282023-04-15 Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD Zheng, Zetian Chen, Junyi Chen, Xingjian Huang, Lei Xie, Weidun Lin, Qiuzhen Li, Xiangtao Wong, Ka‐Chun Adv Sci (Weinh) Research Articles The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat‐resistant while Gefitinib‐sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. John Wiley and Sons Inc. 2023-02-10 /pmc/articles/PMC10104628/ /pubmed/36762572 http://dx.doi.org/10.1002/advs.202204113 Text en © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zheng, Zetian Chen, Junyi Chen, Xingjian Huang, Lei Xie, Weidun Lin, Qiuzhen Li, Xiangtao Wong, Ka‐Chun Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title | Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title_full | Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title_fullStr | Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title_full_unstemmed | Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title_short | Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD |
title_sort | enabling single‐cell drug response annotations from bulk rna‐seq using scad |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104628/ https://www.ncbi.nlm.nih.gov/pubmed/36762572 http://dx.doi.org/10.1002/advs.202204113 |
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