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Marker-free characterization of full-length transcriptomes of single live circulating tumor cells
The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their spars...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977151/ https://www.ncbi.nlm.nih.gov/pubmed/36414416 http://dx.doi.org/10.1101/gr.276600.122 |
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author | Poonia, Sarita Goel, Anurag Chawla, Smriti Bhattacharya, Namrata Rai, Priyadarshini Lee, Yi Fang Yap, Yoon Sim West, Jay Bhagat, Ali Asgar Tayal, Juhi Mehta, Anurag Ahuja, Gaurav Majumdar, Angshul Ramalingam, Naveen Sengupta, Debarka |
author_facet | Poonia, Sarita Goel, Anurag Chawla, Smriti Bhattacharya, Namrata Rai, Priyadarshini Lee, Yi Fang Yap, Yoon Sim West, Jay Bhagat, Ali Asgar Tayal, Juhi Mehta, Anurag Ahuja, Gaurav Majumdar, Angshul Ramalingam, Naveen Sengupta, Debarka |
author_sort | Poonia, Sarita |
collection | PubMed |
description | The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion. |
format | Online Article Text |
id | pubmed-9977151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-99771512023-07-01 Marker-free characterization of full-length transcriptomes of single live circulating tumor cells Poonia, Sarita Goel, Anurag Chawla, Smriti Bhattacharya, Namrata Rai, Priyadarshini Lee, Yi Fang Yap, Yoon Sim West, Jay Bhagat, Ali Asgar Tayal, Juhi Mehta, Anurag Ahuja, Gaurav Majumdar, Angshul Ramalingam, Naveen Sengupta, Debarka Genome Res Method The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion. Cold Spring Harbor Laboratory Press 2023-01 /pmc/articles/PMC9977151/ /pubmed/36414416 http://dx.doi.org/10.1101/gr.276600.122 Text en © 2023 Poonia et al.; Published by Cold Spring Harbor Laboratory Press https://creativecommons.org/licenses/by-nc/4.0/This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Method Poonia, Sarita Goel, Anurag Chawla, Smriti Bhattacharya, Namrata Rai, Priyadarshini Lee, Yi Fang Yap, Yoon Sim West, Jay Bhagat, Ali Asgar Tayal, Juhi Mehta, Anurag Ahuja, Gaurav Majumdar, Angshul Ramalingam, Naveen Sengupta, Debarka Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title | Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title_full | Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title_fullStr | Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title_full_unstemmed | Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title_short | Marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
title_sort | marker-free characterization of full-length transcriptomes of single live circulating tumor cells |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977151/ https://www.ncbi.nlm.nih.gov/pubmed/36414416 http://dx.doi.org/10.1101/gr.276600.122 |
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