Computational Tools for Quantifying Concordance in Single-Cell Fate.

Abstract

Cells are dynamic biological systems that interact with each other and their surrounding environment. Understanding how cell extrinsic and intrinsic factors control cell fate is fundamental to many biological experiments. However, due to transcriptional heterogeneity or microenvironmental fluctuations, cell fates appear to be random. Individual cells within well-defined subpopulations vary with respect to their proliferative potential, survival, and lineage potency. Therefore, methods to quantify fate outcomes for heterogeneous populations that consider both the stochastic and deterministic features of single-cell dynamics are required to develop accurate models of cell growth and differentiation. To study random versus deterministic cell behavior, one requires a probabilistic modelling approach to estimate cumulative incidence functions relating the probability of a cell's fate to its lifetime and to model the deterministic effect of cell environment and inheritance, i.e., nature versus nurture. We have applied competing risks statistics, a branch of survival statistics, to quantify cell fate concordance from cell lifetime data. Competing risks modelling of cell fate concordance provides an unbiased, robust statistical modelling approach to model cell growth and differentiation by estimating the effect of cell extrinsic and heritable factors on the cause-specific cumulative incidence function.