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Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space.

Heyrim Cho1,2, Ya-Huei Kuo3, Russell C Rockne4,2

  • 1Department of Mathematics, University of California Riverside, Riverside, CA, USA.

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|July 8, 2022
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Summary

This study compares graph and multi-dimensional models for analyzing single-cell RNA sequencing data in hematopoiesis. The models help simulate cell state transitions and predict disease emergence, aiding in understanding acute myeloid leukemia.

Keywords:
cell state evolutionhematopoeisisnext generation sequencing datapartial differential equationphenotype structured models

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Single-cell sequencing generates vast data, driving computational tool development.
  • Few mathematical models exist to leverage single-cell data for biological insights.
  • Hematopoiesis serves as a model system for studying cell differentiation and disease.

Purpose of the Study:

  • To compare two distinct cell state geometries for mathematical modeling of cell state transitions.
  • To apply these models to single-cell RNA sequencing data from hematopoiesis.
  • To evaluate the strengths and weaknesses of graph-based versus continuous space models.

Main Methods:

  • Utilized partial differential equations on a graph representing intermediate cell states.
  • Applied partial differential equations on a multi-dimensional continuous cell state-space.
  • Modeled hematopoiesis to simulate cell state dynamics and disease pathogenesis.

Main Results:

  • Demonstrated the application of calibrated models to mathematically perturb normal hematopoiesis.
  • Simulated and predicted the emergence of novel cell states in acute myeloid leukemia.
  • Provided a comparative analysis of the graph and multi-dimensional model approaches.

Conclusions:

  • Mathematical modeling of cell state geometries is crucial for interpreting single-cell sequencing data.
  • Both graph and multi-dimensional models offer unique advantages for studying cell transitions.
  • These modeling approaches can advance our understanding of developmental processes and diseases like AML.