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This study introduces a new model to track cell state changes using lineage data. It helps understand how cells adapt to therapies and identifies potential therapeutic targets by analyzing cell heterogeneity.

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Area of Science:

  • Cell biology
  • Computational biology
  • Genomics

Background:

  • Cells exhibit diverse molecular and phenotypic states, adapting rapidly to therapeutic stress.
  • Phenotypic plasticity can lead to drug resistance but also offers therapeutic targets.
  • Current population-level drug response assays lack crucial lineage and temporal information.

Purpose of the Study:

  • To develop a modeling approach for analyzing single-cell lineage data to understand cell state switching.
  • To identify and quantify phenotypic heterogeneity and state transitions in cell populations.
  • To provide a framework for understanding and potentially controlling cell state dynamics in response to treatment.

Main Methods:

  • Application of a lineage tree-based hidden Markov model (HMM).
  • Utilizing single-cell lineage data as input for the HMM.
  • Benchmarking the model's classification accuracy with varying dataset sizes.

Main Results:

  • The model successfully classifies cells within experimentally feasible dataset sizes.
  • Analysis revealed multiple distinct phenotypic states with significant heterogeneity in cancer and non-cancer cells.
  • Unique drug responses were observed across different cell states and lineages.

Conclusions:

  • The developed framework enables flexible modeling of single-cell heterogeneity across lineages.
  • It allows for quantification and understanding of cell state switching dynamics.
  • This approach can inform strategies to control cell state switching for therapeutic benefit.