Computational Tools for Quantifying Concordance in Single-Cell Fate.

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.

Analysis of cardiac stem cell self-renewal dynamics in serum-free medium by single cell lineage tracking.

Cardiac colony forming unit-fibroblasts (cCFU-F) are a population of stromal cells residing within the SCA1+/PDGFRα+/CD31- fraction of adult mouse hearts, and which have functional characteristics akin to bone marrow mesenchymal stem cells. We hypothesise that they participate in cardiac homeostasis and repair through their actions as lineage progenitors and paracrine signaling hubs. However, cCFU-F are rare and there are no specific markers for these cells, making them challenging to study. cCFU can self-renew in vitro, although the common use of serum has made it difficult to identify cytokines that maintain lineage identity and self-renewal ability. Cell heterogeneity is an additional confounder as cCFU-F cultures are metastable. Here, we address these limitations by identifying serum-free medium (SFM) for growth, and by using cCFU-F isolated from PdgfraGFP/+ mice to record fate outcomes, morphology and PDGFRα expression for hundreds of single cells over time. We show that SFM supplemented with basic fibroblast growth factor, transforming growth factor-ß and platelet-derived growth factor, enhanced cCFU-F colony formation and long-term self-renewal, while maintaining cCFU-F potency. cCFU-F cultured in SFM maintained a higher proportion of PDGFRα+ cells, a marker of self-renewing cCFU-F, by increasing Pdgfra-GFP+ divisions and reducing the probability of spontaneous myofibroblast differentiation.

Quantifying intrinsic and extrinsic control of single-cell fates in cancer and stem/progenitor cell pedigrees with competing risks analysis.

The molecular control of cell fate and behaviour is a central theme in biology. Inherent heterogeneity within cell populations requires that control of cell fate is studied at the single-cell level. Time-lapse imaging and single-cell tracking are powerful technologies for acquiring cell lifetime data, allowing quantification of how cell-intrinsic and extrinsic factors control single-cell fates over time. However, cell lifetime data contain complex features. Competing cell fates, censoring, and the possible inter-dependence of competing fates, currently present challenges to modelling cell lifetime data. Thus far such features are largely ignored, resulting in loss of data and introducing a source of bias. Here we show that competing risks and concordance statistics, previously applied to clinical data and the study of genetic influences on life events in twins, respectively, can be used to quantify intrinsic and extrinsic control of single-cell fates. Using these statistics we demonstrate that 1) breast cancer cell fate after chemotherapy is dependent on p53 genotype; 2) granulocyte macrophage progenitors and their differentiated progeny have concordant fates; and 3) cytokines promote self-renewal of cardiac mesenchymal stem cells by symmetric divisions. Therefore, competing risks and concordance statistics provide a robust and unbiased approach for evaluating hypotheses at the single-cell level.