Motivation Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions. Results We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population. Availability and implementation Freely available for download at http://github.com/nsuhasj/Transcompp. Supplementary information Supplementary data are available at Bioinformatics online.

Cancer plasticity contributes to tumorigenesis, tumor progression, and drug resistance. Plasticity has traditionally been attributed to stem‐like cells, but recent studies used markers of stemness and differentiation to purify specific phenotypes from a mixed population of cancer cells by flow cytometry, and found that both differentiated and dedifferentiated phenotypes could revert over time to a heterogeneous equilibrium mixture, through stochastic transitions. A given instance of heterogeneity can in theory arise either from multiple static phenotypes or from transitions between non‐static phenotypes, and therefore, understanding plasticity requires measuring transition rates.We developed a Markov‐modeling computational approach to analyze time‐series intensity data from flow cytometry experiments, and to quantify phenotypic change in terms of cell state transitions. The software employs optimization and novel statistical bootstrap methods to compute best‐fit transition probabilities (and associated dispersion measures or confidence intervals) for stochastic transitions between multiple cell states.Applying this method to the MCF10A model of breast cancer provided quantification of stochastic transitions as follows. Cells were grown in different culture conditions: basal medium supplemented with the components of MCF10a complete growth medium (insulin, EGF, hydrocortisone, cholera toxin), given individually or together. For each condition, cells were purified into populations of epithelial‐like CD44+CD24+ cells and stem‐like CD44+CD24− cells. The purified populations were cultured for 12 days, and the population fraction of each state was measured using flow cytometry on days 4, 8 and 12 post‐sorting. Analysis using Markov models showed that Hydrocortisone (HC), and Cholera toxin (CTX) cause the largest changes in transition rates, compared with control (basal media). CTX drove the equilibrium toward a more differentiated CD24+ phenotype. In contrast, HC supplementation drove toward the CD24− phenotype, which showed increased mammosphere formation and an aggressive mRNA profile. The bootstrapping method showed these transition rate changes to be statistically significant.In conclusion, we developed a novel algorithm to estimate transition probabilities from FACS data, and to make statistical comparisons between transition probabilities. Applying our method to a cell culture model of triple‐negative breast cancer showed that exposure to hydrocortisone increased the rate of cell transitions toward a more malignant phenotype, suggesting that chronic exposure to the stress hormone cortisol might be particularly deleterious. This software will be released open‐source and is currently available by request.Support or Funding Information Singapore Ministry of Health's National Medical Research Council (NMRC) under its Open Fund Individual Research Grant scheme (OFIRG15nov062). National Research Foundation (NRF), Prime Minister's Office, Singapore, under its CREATE programme, Singapore‐MIT Alliance for Research and Technology (SMART) BioSystems and Micromechanics (BioSyM) IRG This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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