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Multicondition and multimodal temporal profile inference during mouse embryonic development.

Ran Zhang1,2, Chengxiang Qiu1, Galina Filippova3

  • 1Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.

Genome Research
|August 14, 2025
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Summary
This summary is machine-generated.

Sunbear integrates diverse single-cell data across time, enabling imputation and alignment of cellular profiles. This framework reveals developmental insights and predicts gene expression dynamics, even with missing data.

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Single-cell measurements offer insights into cellular dynamics but face challenges due to disruptive sampling and mismatched time points.
  • Integrating multimodal (e.g., scRNA-seq, scATAC-seq) and multicondition (e.g., sex, batch) single-cell data across time is complex.

Purpose of the Study:

  • To introduce Sunbear, a joint modeling framework for integrating multicondition and multimodal single-cell profiles over time.
  • To enable imputation of temporal profile changes, alignment of profiles across datasets and modalities, and extrapolation of missing data.

Main Methods:

  • Development of a novel joint modeling framework named Sunbear.
  • Application of Sunbear to analyze mouse embryonic development datasets.
  • Utilizing Sunbear for imputation, alignment, and extrapolation tasks.

Main Results:

  • Sunbear successfully integrated multicondition and multimodal single-cell data across time.
  • The framework revealed sex-biased transcription patterns during mouse embryonic development.
  • Dynamic relationships between epigenetic priming and transcription were predicted for cells lacking multimodal profiles.

Conclusions:

  • Sunbear provides a powerful approach for analyzing complex single-cell time-series data.
  • The framework facilitates the projection of single-cell snapshots into comprehensive multimodal and multicondition views of cellular trajectories.
  • Sunbear enhances our understanding of developmental processes and gene regulation by overcoming data integration challenges.