Evidence map›Paper›PMID 38871986›Full record

ArticleNature methods2024

CellRank 2: unified fate mapping in multiview single-cell data.

Philipp Weiler, Marius Lange, Michal Klein, Dana Pe'er, Fabian Theis

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers.

0numbers the graph read from it
0cells of the map it votes in
149citing 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

149 citing papers in PubMed.

  1. Review
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  6. Predictive control of human pancreatic cell fate using a digital model ofbioRxiv : the preprint server for biology · 2026
    Article
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  8. Review
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89 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Philipp Weiler *Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-0992-7043
Marius Lange *Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-4846-1266
Michal KleinInstitute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
Dana Pe'erComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Fabian TheisInstitute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany. fabian.theis@helmholtz-munich.de.ORCID http://orcid.org/0000-0002-2419-1943

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748Wellcome Trust
6 · The paper itself

Abstract

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

Indexed as

Cell DifferentiationSingle-Cell AnalysisCell LineageEndodermHematopoiesisHumansOrganoidsSequence Analysis, RNA

Identifiers

PMID38871986
PMCPMC11239496

What OpenQuestion 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 OpenQuestion graph.