Imputing single-cell protein abundance in multiplex tissue imaging

Raphael Kirchgaessner, Cameron Watson, Allison Creason, Kaya Keutler and Jeremy Goecks ()
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Raphael Kirchgaessner: Oregon Health & Science University
Cameron Watson: Oregon Health & Science University
Allison Creason: Oregon Health & Science University
Kaya Keutler: Oregon Health & Science University
Jeremy Goecks: Oregon Health & Science University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using multiplex tissue imaging data from a breast cancer cohort. We evaluate regularized linear regression, gradient-boosted trees, and deep learning autoencoders, incorporating spatial context to enhance imputation accuracy. Our models achieve mean absolute errors between 0.05–0.3 on a [0,1] scale, closely approximating ground truth values. Using imputed data, we classify single cells as pre- or post-treatment, demonstrating their biological relevance. These findings establish the feasibility of imputing missing protein abundance, highlight the advantages of spatial information, and support machine learning as a powerful tool for improving single-cell tissue imaging.

Date: 2025
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DOI: 10.1038/s41467-025-59788-x

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