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

Cell plasticity, or the ability of cells within a population to reversibly alter their phenotype, is an important feature of tissue homeostasis during processes such as wound healing and cancer. Plasticity operates alongside other sources of cell-to-cell heterogeneity such as genetic mutations and variation in signaling. Ultimately these processes prevent 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 tree-based adaptation of a hidden Markov model (tHMM) that employs single cell lineages as input to learn the characteristic patterns of single cell heterogeneity and state transitions in an unsupervised fashion. This model enables single cell classification based on the phenotype of individual cells and their relatives for improved specificity in pinpointing the structure and dynamics of variability in drug response. Integrating this model with a modular interface for defining observed phenotypes allows the model to easily be adapted to any phenotype measured in single cells. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths (G1 and S/G2) as our observed phenotypes on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of cell fate and phase duration in cancer cell populations treated with chemotherapies to determine the number of distinct subpopulations. In total, this tHMM framework allows for the flexible classification of single cell heterogeneity across lineages.