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Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line

Cell lines are one of the most frequently implemented model systems in life sciences research as they provide reproducible high throughput testing. Differentiation of cell cultures varies by line and, in some cases, can result in functional modifications within a population. Although research is inc...

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Autores principales: Dave, Arpit, Nekritz, Erin, Charytonowicz, Daniel, Beaumont, Michael, Smith, Melissa, Beaumont, Kristin, Silva, Jose, Sebra, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237515/
https://www.ncbi.nlm.nih.gov/pubmed/36630696
http://dx.doi.org/10.3389/fgene.2022.894597
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author Dave, Arpit
Nekritz, Erin
Charytonowicz, Daniel
Beaumont, Michael
Smith, Melissa
Beaumont, Kristin
Silva, Jose
Sebra, Robert
author_facet Dave, Arpit
Nekritz, Erin
Charytonowicz, Daniel
Beaumont, Michael
Smith, Melissa
Beaumont, Kristin
Silva, Jose
Sebra, Robert
author_sort Dave, Arpit
collection PubMed
description Cell lines are one of the most frequently implemented model systems in life sciences research as they provide reproducible high throughput testing. Differentiation of cell cultures varies by line and, in some cases, can result in functional modifications within a population. Although research is increasingly dependent on these in vitro model systems, the heterogeneity within cell lines has not been thoroughly investigated. Here, we have leveraged high throughput single-cell assays to investigate the Comma-1D mouse cell line that is known to differentiate in culture. Using scRNASeq and custom single-cell phenotype assays, we resolve the clonal heterogeneity within the referenced cell line on the genomic and functional level. We performed a cohesive analysis of the transcriptome of 5,195 sequenced cells, of which 85.3% of the total reads successfully mapped to the mm10-3.0.0 reference genome. Across multiple gene expression analysis pipelines, both luminal and myoepithelial lineages were observed. Deep differential gene expression analysis revealed eight subclusters identified as luminal progenitor, luminal differentiated, myoepithelial differentiated, and fibroblast subpopulations—suggesting functional clustering within each lineage. Gene expression of published mammary stem cell (MaSC) markers Epcam, Cd49f, and Sca-1 was detected across the population, with 116 (2.23%) sequenced cells expressing all three markers. To gain insight into functional heterogeneity, cells with patterned MaSC marker expression were isolated and phenotypically investigated through a custom single-cell high throughput assay. The comparison of growth kinetics demonstrates functional heterogeneity within each cell cluster while also illustrating significant limitations in current cell isolation methods. We outlined the upstream use of our novel automated cell identification platform—to be used prior to single-cell culture—for reduced cell stress and improved rare cell identification and capture. Through compounding single-cell pipelines, we better reveal the heterogeneity within Comma-1D to identify subpopulations with specific functional characteristics.
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spelling pubmed-92375152022-06-29 Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line Dave, Arpit Nekritz, Erin Charytonowicz, Daniel Beaumont, Michael Smith, Melissa Beaumont, Kristin Silva, Jose Sebra, Robert Front Genet Genetics Cell lines are one of the most frequently implemented model systems in life sciences research as they provide reproducible high throughput testing. Differentiation of cell cultures varies by line and, in some cases, can result in functional modifications within a population. Although research is increasingly dependent on these in vitro model systems, the heterogeneity within cell lines has not been thoroughly investigated. Here, we have leveraged high throughput single-cell assays to investigate the Comma-1D mouse cell line that is known to differentiate in culture. Using scRNASeq and custom single-cell phenotype assays, we resolve the clonal heterogeneity within the referenced cell line on the genomic and functional level. We performed a cohesive analysis of the transcriptome of 5,195 sequenced cells, of which 85.3% of the total reads successfully mapped to the mm10-3.0.0 reference genome. Across multiple gene expression analysis pipelines, both luminal and myoepithelial lineages were observed. Deep differential gene expression analysis revealed eight subclusters identified as luminal progenitor, luminal differentiated, myoepithelial differentiated, and fibroblast subpopulations—suggesting functional clustering within each lineage. Gene expression of published mammary stem cell (MaSC) markers Epcam, Cd49f, and Sca-1 was detected across the population, with 116 (2.23%) sequenced cells expressing all three markers. To gain insight into functional heterogeneity, cells with patterned MaSC marker expression were isolated and phenotypically investigated through a custom single-cell high throughput assay. The comparison of growth kinetics demonstrates functional heterogeneity within each cell cluster while also illustrating significant limitations in current cell isolation methods. We outlined the upstream use of our novel automated cell identification platform—to be used prior to single-cell culture—for reduced cell stress and improved rare cell identification and capture. Through compounding single-cell pipelines, we better reveal the heterogeneity within Comma-1D to identify subpopulations with specific functional characteristics. Frontiers Media S.A. 2022-06-14 /pmc/articles/PMC9237515/ /pubmed/36630696 http://dx.doi.org/10.3389/fgene.2022.894597 Text en Copyright © 2022 Dave, Nekritz, Charytonowicz, Beaumont, Smith, Beaumont, Silva and Sebra. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Dave, Arpit
Nekritz, Erin
Charytonowicz, Daniel
Beaumont, Michael
Smith, Melissa
Beaumont, Kristin
Silva, Jose
Sebra, Robert
Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title_full Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title_fullStr Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title_full_unstemmed Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title_short Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line
title_sort integration of single-cell transcriptomics with a high throughput functional screening assay to resolve cell type, growth kinetics, and stemness heterogeneity within the comma-1d cell line
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237515/
https://www.ncbi.nlm.nih.gov/pubmed/36630696
http://dx.doi.org/10.3389/fgene.2022.894597
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