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

Abstract

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.

Patterns of interdivision time correlations reveal hidden cell cycle factors

“…Family relations -such as daughter, grandmother, cousin cells etc. -encode inheritance patterns, and correlations between these related cells have been used to understand the dynamics of cell populations [32,33] (Figure 1c). Several stochastic models have been proposed to explain interdivision time correlation patterns.…”

Live Cell Lineage Tracing of Dormant Cancer Cells

“…[37] Also, non-degradable hydrogels immobilize cells in the microenvironment and dormancy-capable cells can become dormant in such system due to the physical confinement. [38] In sum, the cell lineage tree approach is a good model to study single cell heterogeneity [15] and this analysis method can be applied to variety of cells with various culturing environment. Gross et al used the cell lineage tree model to capture cell cycle dynamics upon drug application, which includes drug-specific effects on cell cycle phase and single cell responses.…”

“…In sum, the cell lineage tree approach is a good model to study single cell heterogeneity [15] and this analysis method can be applied to variety of cells with various culturing environment. Gross et al .…”

“…In order to track cell plasticity with greater accuracy, Laura Heiser’s group modified an existing Markov model to better classify cells according to their heterogeneous phenotypes across lineages. [15] We used this model to create cell lineage trees and analyzed individual cell proliferation and death. This method can solve the problems that other commonly used cell proliferation assays have.…”

Live Cell Lineage Tracing of Dormant Cancer Cells

“…In order to track cell plasticity with greater accuracy, the Heiser lab developed a method to classify cells according to their heterogeneous phenotypes across lineages. [ 18 ] We adapted this model to create cell lineage trees and analyze individual cell proliferation and death. This method solves several problems with other commonly used cell proliferation assays.…”