Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.

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 models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5–14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes. Synopsis: The prediction certainty of machine learning classification models can be used as a continuous measurement to quantitatively monitor single cell state transitions, as demonstrated for myoblast differentiation during muscle fiber formation. Live imaged single myoblast continuous differentiation states are computationally derived from motility and actin dynamics. The model distinguishes between cells that differentiated but failed to fuse to predict molecules specifically involved in fusion, as well as changes in actin dynamics. Mass spectrometry supports these in silico predictions and suggests novel fusion and maturation regulators downstream of differentiation. p38 is essential for the transition from terminal differentiation to fusion. The prediction certainty of machine learning classification models can be used as a continuous measurement to quantitatively monitor single cell state transitions, as demonstrated for myoblast differentiation during muscle fiber formation. [ABSTRACT FROM AUTHOR]