Evidence map›Paper›PMID 40813249›Full record

ArticleGenome research2025

Multicondition and multimodal temporal profile inference during mouse embryonic development.

Ran Zhang, Chengxiang Qiu, Galina N Filippova, Gang Li, Jay Shendure, Jean-Philippe Vert, Xinxian Deng, William Stafford Noble, Christine M Disteche

Abstract read
In one paragraph

Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Ran ZhangDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Chengxiang QiuDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Galina N FilippovaDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA.
Gang LiDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Jay ShendureDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Jean-Philippe VertOwkin, New York, New York 10010, USA.
Xinxian DengDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA; cdistech@uw.edu wnoble@uw.edu dengx2@uw.edu.ORCID 0000-0002-5007-218X
William Stafford NobleDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; cdistech@uw.edu wnoble@uw.edu dengx2@uw.edu.ORCID 0000-0001-7283-4715
Christine M DistecheDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA; cdistech@uw.edu wnoble@uw.edu dengx2@uw.edu.

Funding

UW 4-Dimensional Genomic Organization of Mammalian Embryogenesis CenterUM1HG011586 · NHGRI · UNIVERSITY OF WASHINGTON · PI DISTECHE, CHRISTINE M., NOBLE, WILLIAM STAFFORD · 2020 to 2024
$10.3M
X chromosome regulation and role in aneuploidyR35GM131745 · NIGMS · UNIVERSITY OF WASHINGTON · PI Christine M. Disteche · 2019 to 2026
$4.1M
Semi-supervised cross-modality translation forsingle-cell genomics and proteomicsR00HG013343 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Ran Zhang · 2025 to 2026
$497k
NHGRI NIH HHS R00 HG013343NHGRI NIH HHS UM1 HG011586NIGMS NIH HHS R35 GM131745
6 · The paper itself

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.

Indexed as

Embryonic DevelopmentSingle-Cell AnalysisAnimalsEpigenesis, GeneticFemaleGene Expression ProfilingGene Expression Regulation, DevelopmentalMaleMiceTranscriptome

Identifiers

PMID40813249
PMCPMC12487814

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.