Multicondition and multimodal temporal profile inference during mouse embryonic development

The emergence of single-cell time-series data sets enables modeling of changes in various types of cellular profiles over time. However, because of the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here, we propose a joint modeling framework, Sunbear, for integrating multicondition and multimodal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-data set and multimodal profiles across time, and extrapolate single-cell profiles in a missing modality. We apply Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multimodal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multimodal and multicondition views of cellular trajectories.

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Publication Details

Journal
Genome Research
Published
2025-08-14
DOI
https://doi.org/10.1101/gr.279997.124
Primary Topic
Single-cell and spatial transcriptomics
Type
article

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article

Multicondition and multimodal temporal profile inference during mouse embryonic development

Chengxiang Qiu, Xinxian Deng, Galina N. Filippova, William Stafford Noble et al.
Genome Research
Single-cell and spatial transcriptomics
article

Multicondition and multimodal temporal profile inference during mouse embryonic development

Chengxiang Qiu, Xinxian Deng, Galina N. Filippova, William Stafford Noble, Jean‐Philippe Vert, Christine M. Distèche, Jay Shendure, Ran Zhang, Gang Li
article en

Abstract

The emergence of single-cell time-series data sets enables modeling of changes in various types of cellular profiles over time. However, because of the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here, we propose a joint modeling framework, Sunbear, for integrating multicondition and multimodal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-data set and multimodal profiles across time, and extrapolate single-cell profiles in a missing modality. We apply Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multimodal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multimodal and multicondition views of cellular trajectories.

Genome ResearchVol. 35(10)
Howard Hughes Medical Institute (US), University of Washington (US), Faculty of 1000 (United States) (US), Brotman Baty Institute (US), Allen Institute for Cell Science (US), Seattle University (US)
National Institutes of Health, National Institute of General Medical Sciences
Single-cell and spatial transcriptomics
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