Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation.
View Article and Find Full Text PDFBackground: The Autotaxin (ATX)-lysophosphatidic acid (LPA) axis is involved in decreasing radiation sensitivity of breast tumor cells. This study aims to further elucidate the effect of irradiation on the ATX-LPA axis and cytokine secretion in different breast cancer cell lines to identify suitable breast cancer subtypes for targeted therapies.
Methods: Different breast cancer cell lines (MCF-7 (luminal A), BT-474 (luminal B), SKBR-3 (HER2-positive), MDA-MB-231 and MDA-MB-468 (triple-negative)) and the breast epithelial cell line MCF-10A were irradiated.
Motivation: Cells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within cancer cell lines.
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