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STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets
Single-cell RNA sequencing is a powerful tool to examine cellular heterogeneity, novel markers and target genes, and therapeutic mechanisms in human cancers and animal models. Here, we analyzed single-cell RNA sequencing data of T cells obtained from multiple mouse tumor models by PCA-based subclust...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Rockefeller University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077174/ https://www.ncbi.nlm.nih.gov/pubmed/33900375 http://dx.doi.org/10.1084/jem.20201329 |
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author | Bhatt, Dev Kang, Boxi Sawant, Deepali Zheng, Liangtao Perez, Kristy Huang, Zhiyu Sekirov, Laura Wolak, Dan Huang, Julie Y. Liu, Xian DeVoss, Jason Manzanillo, Paolo S. Pierce, Nathan Zhang, Zemin Symons, Antony Ouyang, Wenjun |
author_facet | Bhatt, Dev Kang, Boxi Sawant, Deepali Zheng, Liangtao Perez, Kristy Huang, Zhiyu Sekirov, Laura Wolak, Dan Huang, Julie Y. Liu, Xian DeVoss, Jason Manzanillo, Paolo S. Pierce, Nathan Zhang, Zemin Symons, Antony Ouyang, Wenjun |
author_sort | Bhatt, Dev |
collection | PubMed |
description | Single-cell RNA sequencing is a powerful tool to examine cellular heterogeneity, novel markers and target genes, and therapeutic mechanisms in human cancers and animal models. Here, we analyzed single-cell RNA sequencing data of T cells obtained from multiple mouse tumor models by PCA-based subclustering coupled with TCR tracking using the STARTRAC algorithm. This approach revealed various differentiated T cell subsets and activation states, and a correspondence of T cell subsets between human and mouse tumors. STARTRAC analyses demonstrated peripheral T cell subsets that were developmentally connected with tumor-infiltrating CD8(+) cells, CD4(+) Th1 cells, and T reg cells. In addition, large amounts of paired TCRα/β sequences enabled us to identify a specific enrichment of paired public TCR clones in tumor. Finally, we identified CCR8 as a tumor-associated T reg cell marker that could preferentially deplete tumor-associated T reg cells. We showed that CCR8-depleting antibody treatment provided therapeutic benefit in CT26 tumors and synergized with anti–PD-1 treatment in MC38 and B16F10 tumor models. |
format | Online Article Text |
id | pubmed-8077174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Rockefeller University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80771742021-12-07 STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets Bhatt, Dev Kang, Boxi Sawant, Deepali Zheng, Liangtao Perez, Kristy Huang, Zhiyu Sekirov, Laura Wolak, Dan Huang, Julie Y. Liu, Xian DeVoss, Jason Manzanillo, Paolo S. Pierce, Nathan Zhang, Zemin Symons, Antony Ouyang, Wenjun J Exp Med Technical Advances and Resources Single-cell RNA sequencing is a powerful tool to examine cellular heterogeneity, novel markers and target genes, and therapeutic mechanisms in human cancers and animal models. Here, we analyzed single-cell RNA sequencing data of T cells obtained from multiple mouse tumor models by PCA-based subclustering coupled with TCR tracking using the STARTRAC algorithm. This approach revealed various differentiated T cell subsets and activation states, and a correspondence of T cell subsets between human and mouse tumors. STARTRAC analyses demonstrated peripheral T cell subsets that were developmentally connected with tumor-infiltrating CD8(+) cells, CD4(+) Th1 cells, and T reg cells. In addition, large amounts of paired TCRα/β sequences enabled us to identify a specific enrichment of paired public TCR clones in tumor. Finally, we identified CCR8 as a tumor-associated T reg cell marker that could preferentially deplete tumor-associated T reg cells. We showed that CCR8-depleting antibody treatment provided therapeutic benefit in CT26 tumors and synergized with anti–PD-1 treatment in MC38 and B16F10 tumor models. Rockefeller University Press 2021-04-26 /pmc/articles/PMC8077174/ /pubmed/33900375 http://dx.doi.org/10.1084/jem.20201329 Text en © 2021 Amgen Inc http://www.rupress.org/terms/https://creativecommons.org/licenses/by-nc-sa/4.0/This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/). |
spellingShingle | Technical Advances and Resources Bhatt, Dev Kang, Boxi Sawant, Deepali Zheng, Liangtao Perez, Kristy Huang, Zhiyu Sekirov, Laura Wolak, Dan Huang, Julie Y. Liu, Xian DeVoss, Jason Manzanillo, Paolo S. Pierce, Nathan Zhang, Zemin Symons, Antony Ouyang, Wenjun STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title | STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title_full | STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title_fullStr | STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title_full_unstemmed | STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title_short | STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets |
title_sort | startrac analyses of scrnaseq data from tumor models reveal t cell dynamics and therapeutic targets |
topic | Technical Advances and Resources |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077174/ https://www.ncbi.nlm.nih.gov/pubmed/33900375 http://dx.doi.org/10.1084/jem.20201329 |
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