Evidence map›Paper›PMID 39566551›Full record

ArticleNature2025

A cell atlas foundation model for scalable search of similar human cells.

Graham Heimberg, Tony Kuo, Daryle J DePianto, Omar Salem, Tobias Heigl, Nathaniel Diamant, Gabriele Scalia, Tommaso Biancalani, Shannon J Turley, Jason R Rock and 4 more

Abstract read
In one paragraph

Article in Nature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing 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

81 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Ancestry Matters in Decoding Immunity.Immunology and cell biology · 2026
    Article
  15. Article
  16. Organism-scale annotation with Pan-human Azimuth.bioRxiv : the preprint server for biology · 2026
    Article
  17. Article
  18. Article
  19. Article
  20. Article

21 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

14 authors.

Graham Heimberg *Biology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA. heimberg@gene.com.ORCID 0009-0002-6493-5721
Tony Kuo *Roche Informatics, F. Hoffmann-La Roche, Mississauga, Ontario, Canada.
Daryle J DePiantoDepartment of Immunology Discovery, Genentech, San Francisco, CA, USA.
Omar SalemBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA.ORCID 0009-0005-1514-831X
Tobias HeiglDepartment of Immunology Discovery, Genentech, San Francisco, CA, USA.ORCID 0000-0002-9591-8345
Nathaniel DiamantBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA.ORCID 0000-0002-1738-304X
Gabriele ScaliaBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA.
Tommaso BiancalaniBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA.ORCID 0000-0001-9104-9755
Shannon J TurleyDepartment of Immunology Discovery, Genentech, San Francisco, CA, USA.
Jason R RockDepartment of Immunology Discovery, Genentech, San Francisco, CA, USA.
Héctor Corrada BravoBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA.
Josh KaminkerOMNI Bioinformatics, gRED Computational Sciences, Genentech, San Francisco, CA, USA. kaminker@gene.com.ORCID 0000-0001-5067-9275
Jason A Vander HeidenBiology Research, AI Development, gRED Computational Sciences, Genentech, San Francisco, CA, USA. vanderheiden.jason@gene.com.ORCID 0000-0002-1474-310X
Aviv RegevResearch and Early Development, Genentech, San Francisco, CA, USA. regev.aviv@gene.com.ORCID 0000-0003-3293-3158

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. Mining these growing atlases could reveal cell-disease associations, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease

Indexed as

Atlases as TopicSingle-Cell AnalysisFibroblastsHumansLung Diseases, InterstitialMacrophages

Identifiers

PMID39566551
PMCPMC11864978

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.