PreprintCited by 41Single-cell and spatial transcriptomicsGene expression and cancer classificationBioinformatics and Genomic Networks

Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages

Open access
Heimberg, Graham, Kuo, Tony, DePianto, Daryle, Heigl, Tobias, Diamant, Nathaniel, Salem, Omar, Scalia, Gabriele, Biancalani, Tommaso +6 more·2023·bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell state in vitro . SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.

Integrity signals

Registry

Crossref · 10.1101/2023.07.18.549537

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57 of 57 references verified0 citation errors · 0 not checkable

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  • The exponential increase in throughput of measurement technologies has led to collection of large and growing reference datasets within and across different cell and tissue systems, with data doubling every 6 months for the past several years.

    “Experimentally, the exponential increase in the throughput of measurement technologies has led to the collection of large and growing reference datasets within and across different cell and tissue systems,23–25 with data doubling every 6 months for the past several years,26”

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Authors
Heimberg, G., Kuo, T., DePianto, D., Heigl, T., Diamant, N., Salem, O., Scalia, G., Biancalani, T., Turley, S., Rock, J., Bravo, H. C., Kaminker, J., Heiden, J. A. V., & Regev, A.
Affiliations
  • Genentech (US)
  • Roche (Canada) (CA)
Corresponding author
  • Heimberg, Graham
  • Heiden, Jason A. Vander
  • Regev, Aviv
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2023-07-19
OpenAlex
W4384821687
Cited by
40
References
57
Citations by year
2026: 6 · 2025: 7 · 2024: 17 · 2023: 9
Type
posted content
Language
en
Open access
Licence
cc-by-nc-nd
Locations
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
metric learning, single-cell RNA sequencing, Human Cell Atlas, cell type annotation, interstitial lung disease, fibrotic diseases, 3D hydrogels, in vitro disease model, foundation models, data integration

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