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Martin Alice
Research Engineer in AI at CEA
Grenoble, France
Public Documents
1
He2Cl: a 2-step clustering algorithm to characterize cellular heterogeneity from cell...
Alice Martin
and 5 more
October 28, 2024
Cellular heterogeneity is one of the main hallmarks of cancer, referring to the coexistence of different phenotypes with very distinct biological behaviors in single isolates. Automatically detecting single-cell heterogeneity is therefore critical, and can provide important information on cancer initiation, tumour aggressiveness or drug-related resistance. Here we introduce He2Cl, a novel machinelearning algorithm able to detect single cell heterogeneity from the morpho-dynamic analysis of 2D cell cultures. Built on label-free quantitative time-lapse imaging, He2Cl performs a 2-step clustering algorithm for unsupervised classification of sub-phenotypes within a cell population. He2Cl outperforms state-of-the-art clustering models and allows the simultaneous classification of thousands of cell trajectories from their birth to their division, paving the way towards AI-enhanced microscopy for live-cell analysis.