Capybara: A Computational Tool to Measure Cell Identity and Fate Transitions

Measuring cell identity in development, disease, and reprogramming is challenging as cell types and states are in continual transition. Here, we present Capybara, a computational tool to classify discrete cell identity and intermediate "hybrid" cell states, supporting a metric to quantify cell fate transition dynamics. We validate hybrid cells using experimental lineage tracing data to demonstrate the multi-lineage potential of these intermediate cell states. We apply Capybara to diagnose shortcomings in several cell engineering protocols, identifying hybrid states in cardiac reprogramming and off-target identities in motor neuron programming, which we alleviate by adding exogenous signaling factors. Further, we establish a putative in vivo correlate for induced endoderm progenitors. Together, these results showcase the utility of Capybara to dissect cell identity and fate transitions, prioritizing interventions to enhance the efficiency and fidelity of stem cell engineering.