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.
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.
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DOI：
10.3389/fgene.2022.894597
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发表时间：
2022
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
细胞系是生命科学研究中最常用的模型系统之一，因为它们提供可重复的高通量测试。细胞培养物的分化因细胞系而异，并且在某些情况下，可导致群体内的功能修饰。尽管研究越来越依赖于这些体外模型系统，但细胞系内的异质性尚未得到彻底研究。在这里，我们利用高通量单细胞测定来研究已知在培养中分化的逗号-1D小鼠细胞系。使用scRNASeq和定制的单细胞表型测定，我们在基因组和功能水平上解决了参考细胞系内的克隆异质性。我们对5，195个测序细胞的转录组进行了内聚分析，其中85.3%的总读数成功映射到mm 10 -3.0.0参考基因组。在多个基因表达分析管道中，观察到管腔和肌上皮谱系。深度差异基因表达分析显示，8个亚群确定为管腔祖细胞，管腔分化，肌上皮分化，成纤维细胞亚群，提示功能聚类内每个谱系。在整个群体中检测到已发表的乳腺干细胞（MaSC）标志物Epcam、Cd 49 f和Sca-1的基因表达，其中116个（2.23%）测序细胞表达所有三种标志物。为了深入了解功能异质性，分离具有模式化MaSC标志物表达的细胞，并通过定制的单细胞高通量测定法进行表型研究。生长动力学的比较证明了每个细胞簇内的功能异质性，同时也说明了当前细胞分离方法的显著局限性。我们概述了我们的新型自动化细胞鉴定平台的上游使用-在单细胞培养之前使用-用于减少细胞应激和改善稀有细胞鉴定和捕获。通过复合单细胞管道，我们更好地揭示了逗号-1D内的异质性，以识别具有特定功能特征的亚群。
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.
影响因子：
3.7
作者：
Komori R;Kobayashi T;Matsuo H;Kino K;Miyazawa H
通讯作者：
Miyazawa H