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Biology subjects

Damond, N.

Publications and source records attributed to Damond, N..

2 recordsLinked to original sources

Mass Cytometric and Transcriptomic Profiling of Epithelial-Mesenchymal Transitions in Human Mammary Cell Lines

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. Inhibition and elimination of EMT-undergoing cells are therefore promising therapy approaches. However, detecting EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. Here, we profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a FACS surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by chronic exposure to TGF{beta}1 or 4-hydroxytamoxifen, respectively. We observed a spectrum of EMT transition phenotypes in each cell line and the spectrum varied across the time course. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used herein, and for the design of EMT time course experiments. O_TBL View this table: org.highwire.dtl.DTLVardef@10d2b87org.highwire.dtl.DTLVardef@1a4cc67org.highwire.dtl.DTLVardef@67bc95org.highwire.dtl.DTLVardef@8df40dorg.highwire.dtl.DTLVardef@1919e7c_HPS_FORMAT_FIGEXP M_TBL C_TBL Mendeley Data: DOI: 10.17632/pt3gmyk5r2.1 ArrayExpress Data: Accession number E-MTAB-9365

cell biology↗

cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment. Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

bioinformatics↗