Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions

Area of Science:

• Cell biology
• Computational biology
• Cancer research

Background:

• Cell population dynamics are influenced by stochastic plasticity, involving rare single-cell phenotype transitions.
• Quantifying these transition rates is challenging due to simultaneous bidirectional transitions and asymmetric proliferation.
• Existing methods require time-intensive experiments and complex analysis.

Purpose of the Study:

• To develop a computational method for accurately quantifying stochastic cell-state transition rates.
• To analyze cell plasticity and population-level changes in cancer cell lines and patient-derived cells.
• To validate the predictive power of computed transition rates for long-term cell population trajectories.

Main Methods:

• Developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm.
• Utilized optimization and resampling techniques to compute best-fit rates and statistical intervals for stochastic transitions.
• Applied the algorithm to time-series datasets of purified cancer cell subpopulations under various culture conditions.

Main Results:

• Identified that hydrocortisone and cholera toxin shift cell population equilibrium towards stem-like or non-stem states in MCF10CA1a cells.
• Demonstrated that Transcompp can accurately compute transition rates from short-term experiments.
• Showed that these computed rates predict long-term cell population trajectories and equilibrium convergence in patient-derived cells.

Conclusions:

• Transcompp provides a robust method for quantifying cellular plasticity and stochastic transition rates.
• The algorithm facilitates a deeper understanding of cell population dynamics and equilibrium shifts.
• This approach has implications for predicting cancer cell behavior and response to treatments.