Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single cell lineages.

ABSTRACT:

Motivation

In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.

Results

We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones (SCCs) derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quanti

SUBMITTER: Chan TJ 

PROVIDER: S-EPMC9825265 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature