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Identification of Breast Cancer Stem Cell Related Genes Using Functional Cellular Assays Combined With Single-Cell RNA Sequencing in MDA-MB-231 Cells.

Emma Jonasson | Salim Ghannoum | Emma Persson | Joakim Karlsson | Thomas Kroneis | Erik Larsson | Göran Landberg | Anders Ståhlberg
Frontiers in genetics | 2019

Breast cancer tumors display different cellular phenotypes. A growing body of evidence points toward a population of cancer stem cells (CSCs) that is important for metastasis and treatment resistance, although the characteristics of these cells are incomplete. We used mammosphere formation assay and label-retention assay as functional cellular approaches to enrich for cells with different degree of CSC properties in the breast cancer cell line MDA-MB-231 and performed single-cell RNA sequencing. We clustered the cells based on their gene expression profiles and identified three subpopulations, including a CSC-like population. The cell clustering into these subpopulations overlapped with the cellular enrichment approach applied. To molecularly define these groups, we identified genes differentially expressed between the three subpopulations which could be matched to enriched gene sets. We also investigated the transition process from CSC-like cells into more differentiated cell states. In the CSC population we found 14 significantly upregulated genes. Some of these potential breast CSC markers are associated to reported stem cell properties and clinical survival data, but further experimental validation is needed to confirm their cellular functions. Detailed characterization of CSCs improve our understanding of mechanisms for tumor progression and contribute to the identification of new treatment targets.

Pubmed ID: 31191614

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HTSeq (tool)

RRID:SCR_005514

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software Python package that provides infrastructure to process data from high-throughput sequencing assays. While the main purpose of HTSeq is to allow you to write your own analysis scripts, customized to your needs, there are also a couple of stand-alone scripts for common tasks that can be used without any Python knowledge.

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RRID:SCR_008452

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RRID:SCR_008988

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RRID:SCR_012773

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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RRID:CVCL_0062

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