Abstract Cell plasticity operates alongside other sources of cell-to-cell heterogeneity, such as genetic mutations and variation in signaling, together preventing most cancer therapies from being curative. The predominant methods of quantifying tumor-drug response operate on snapshot, population-level measurements and therefore lack evolutionary dynamics, which are particularly critical for dynamic processes such as plasticity. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of single cell phenotypic heterogeneity and state transitions in an unsupervised fashion. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of same measurements in cancer and non-cancer cell populations under various treatments. We find that in each case multiple phenotypically distinct states exist, with significant heterogeneity and unique drug responses. In total, this framework allows for the flexible classification of single cell heterogeneity across lineages.

A lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity

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