Modulation of keratocyte phenotype by collagen fibril nanoarchitecture in membranes for corneal repair. Effects of substrate stiffness on cell morphology, cytoskeletal structure, and adhesion. Robust and automated detection of subcellular morphological motifs in 3D microscopy images. Single-cell morphology encodes metastatic potential. Systematic morphological profiling of human gene and allele function via cell painting. Quantitative morphological signatures define local signaling networks regulating cell morphology. Simultaneously defining cell phenotypes, cell cycle, and chromatin modifications at single-cell resolution. Functional interplay between the cell cycle and cell phenotypes. Evolution of cellular morpho-phenotypes in cancer metastasis. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours.
CELLPROFILER CELL AREA SOFTWARE
Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( ).
However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Quantification of cell morphology has seen tremendous advances in recent years. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Environ Int 149:106411īray MA, Carpenter AE (2018) Quality control for high-throughput imaging experiments using machine learning in cellprofiler.Cell morphology encodes essential information on many underlying biological processes. Kornhuber M et al (2021) The E-Morph assay: identification and characterization of environmental chemicals with estrogenic activity based on quantitative changes in cell-cell contact organization of breast cancer cells. Jones TR et al (2008) CellProfiler analyst: data exploration and analysis software for complex image-based screens. Nat Methods 9(7):676–682Ĭarpenter AE et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Schindelin J et al (2012) Fiji: an open-source platform for biological-image analysis. Cell Res 19(2):156–172īischoff P et al (2020) Estrogens determine adherens junction organization and E-cadherin clustering in breast cancer cells via amphiregulin. Xu J, Lamouille S, Derynck R (2009) TGF-beta-induced epithelial to mesenchymal transition. Cold Spring Harb Perspect Biol 1(6):a003129 Nat Rev Mol Cell Biol 11(7):502–514īerx G, van Roy F (2009) Involvement of members of the cadherin superfamily in cancer. Harris TJ, Tepass U (2010) Adherens junctions: from molecules to morphogenesis.
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