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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>TissueFormer\u2019s 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-026-06490-4","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T12:26:07Z","timestamp":1780575967000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tissueformer: extending single-cell foundation models to predict population-level phenotypes"],"prefix":"10.1186","volume":"27","author":[{"given":"Ari S.","family":"Benjamin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Zador","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"issue":"6","key":"6490_CR1","doi-asserted-by":"publisher","first-page":"dju124","DOI":"10.1093\/jnci\/dju124","volume":"106","author":"AJ Templeton","year":"2014","unstructured":"Templeton AJ, McNamara MG, \u0160eruga B, Vera-Badillo FE, Aneja P, Oca\u00f1a A, et al. 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