Related Experiment Video
Updated: Sep 2, 2026

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
Published on: October 4, 2024
CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies
Moises Di Sante1, Melissa Pezzotti2, Julius Zimmermann2
1Synthetic Physiology Laboratory, University of Pavia, Pavia, Italy. moises.disante@unipv.it.
Abstract:
Cell-cycle progression is a major source of variability in live-cell phenotyping, yet imaging workflows still lack a general way to account for it alongside structural and functional readouts. Here we show CALIPERS (Cell-cycle-Aware Live-cell Imaging for Phenotyping Experiments and Regeneration Studies), an integrated framework that pairs a spectrally redesigned FUCCI reporter with continuous cell-cycle inference and flexible delivery strategies in human cells and stem-cell models. In epithelial cells, CALIPERS simultaneously images actin, tubulin, or calcium dynamics, phase-locks migration and proliferation, triggers mitosis-aware smart microscopy, and recovers fast calcium signals. In induced pluripotent stem-cell workflows, lentiviral and safe-harbor versions span pluripotent, lineage-restricted, and cardiac models. In cardiac organoids, CALIPERS tracks cell-cycle exit, tissue compaction, and calcium onset, and distinguishes productive proliferation from multinucleation and endoreplication. By making cell-cycle state an explicit, measurable variable, CALIPERS helps triage candidate regenerative interventions across diverse live-imaging assays.

