Dynamic analysis framework to detect cell division and cell death in live-cell imaging, using signal processing and machine learning
Résumé
The detection of cell division and cell death events in live-cell assays has the potential to produce robust metrics
of drug pharmacodynamics and return a more comprehensive understanding of tumor cells responses to cancer
therapeutic combinations. As cancer drugs may have complex and mixed effects on the biology of the cell,
knowing precisely when cellular events occur in a live-cell experiment allows to study the relative contribution
of different drug effects –such as cytotoxic or cytostatic, on a cell population. Yet, classical methods require
dyes to measure cell viability as an end-point assay, where the proliferation rates can only be estimated when
both viable and dead cells are labeled simultaneously –not to mention that the actual cell division events are
often discarded due to analytical limitations.
Live-cell imaging is a promising cell-based assay to determine drug efficacies, however its main limitation
remains the accuracy and depth of the analyses, to acquire automatic measures of the cellular response
phenotype, making the understanding of drug action on cell populations difficult. In this work, we present a new
algorithmic architecture integrating machine learning, image and signal processing methods to perform dynamic
image analyses of single cell events in time-lapse microscopy experiments of drug pharmacological profiling.
Our event detection method is based on a pattern detection approach on the polarized light entropy making, it
free of any labeling step and exhibiting two distinct patterns for cell division and death events. Our analysis
framework is an open source and adaptable workflow that automatically predicts cellular events (and their times)
from each single cell trajectory, along with other classic cellular features of cell image analyses, as a promising
solution in pharmacodynamics.
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