Machine learning inference of continuous cell state transitions during myoblast differentiation

ABSTRACT: Cells modify their internal organization during continuous state-transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our model, trained using motility and actin intensity features from single cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 hours post-induction, followed by fusion ~3 hours later. Co-inhibition of ERK1/2 and p38 led t

SUBMITTER: Amit Shakarchy 

PROVIDER: S-SCDT-10_1038-S44320-024-00010-3 | biostudies-other |

REPOSITORIES: biostudies-other