Cell shape

KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens

KCML is an intelligent system that allow inference of systematic Inference of multi-scale gene funcitons from genetic screens. It annotate genes with GO terms based on phenotypic similarity. As these annotations are data driven they provide more tissue type-specific gene funcitons.

Studying mechanotransduction by image-omics

Development of an image-omic pipeline for inference of signaling networks linking the shape of breast cells to their transcriptional activities. Through this pipeline we identified genes that are predictive of the outcome of breast cancer patients.

Cell shape and the microenvironment regulate nuclear translocation of NF-kB in breast epithelial and tumor cells

Here we show that the physical shape of the cell significantly impact its response to inflammation as reflected by NF-kappaB activation. We measured a total of 77 cell shape and environmental features, such as how close a cell is to its neighbors. To assess shape we measured features like how round the cells were, the ratio of their length to their width, plus the extent of their protrusions and "ruffliness". Mesenchymal-like breast cancer cells had more NF-kappaB in their nuclei, tended to be larger and more "ruffly", with many more sharp protrusions than epithelial-like cells, which are normally softer-edged and rounder.