Tissueformer: extending single-cell foundation models to predict population-level phenotypes

Benjamin, Ari S, Zador, Anthony (June 2026) Tissueformer: extending single-cell foundation models to predict population-level phenotypes. BMC Bioinformatics, 27 (1). p. 172. ISSN 1471-2105

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Abstract

BACKGROUND: Single-cell RNA sequencing technologies have enabled unprecedented insights into gene expression and opened new pathways for diagnostics and tissue annotation. At present, most computational approaches for interpreting single-cell data predict labels or properties based on isolated single-cell transcriptomic profiles. This approach overlooks the cellular composition within a sample, which is often critical for inferring tissue identity or other sample-level phenotypes. RESULTS: To address this limitation, we introduce TissueFormer, a Transformer-based neural network that infers population-level labels from groups of single-cell RNA profiles while retaining single-cell resolution. We applied TissueFormer to two tasks: predicting COVID-19 severity from single-cell RNA sequencing of blood samples, and predicting cortical area identity from spatial transcriptomic data in mouse brains. TissueFormer outperformed single-cell foundation models and machine learning methods applied to pseudobulk and cell type composition. CONCLUSIONS: TissueFormer's higher performance promises more accurate diagnostics and enables the automated construction of high-resolution brain region maps in individual mice directly from spatial transcriptomic data. Applied to mice with developmental perturbations to visual input, these maps revealed a significant reduction in predicted visual cortex area, illustrating how individual differences in neuroanatomy can be quantified. More broadly, TissueFormer provides a framework for predicting any population-level phenotypes which are influenced by cellular diversity and tissue-level organization.

Item Type: Paper
Subjects: bioinformatics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
organism description > animal
organism description > animal > mammal
organism description > animal > mammal > rodent > mouse
organism description > animal > mammal > rodent
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:
Communities: CSHL labs > Zador lab
CSHL Post Doctoral Fellows
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 4 June 2026
Date Deposited: 17 Aug 2026 12:46
Last Modified: 17 Aug 2026 12:46
PMCID: PMC13466306
Related URLs:
URI: https://repository.cshl.edu/id/eprint/42293

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