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bioRxiv · 10.64898/2026.04.17.719309

Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro

Miao, Z.·Qu, Y.·Huang, S.·Laux, L.·Peters, S.·Aristel, A.·Zhang, Z.·Niedernhofer, L. J.·McMahon, A.·Kim, J.·Zhang, N.
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Abstract

Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro, a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to disentangle multiple overlapping spatial patterns. CoPro can be applied to single-cell-level spatial transcriptomics datasets, including MERFISH, SeqFISH+, Xenium, and histology-imputed transcriptomic data. We demonstrate the utility of CoPro with data collected from colon, brain, liver, and kidney tissues. In the colon, CoPro separates epithelial differentiation along the crypt axis from spatially localized inflammatory signals. In the aging liver, it identifies multiple aging-associated cellular programs superimposed on anatomical zonation. In the brain, the flexible kernel design enables the decoupling of the gene expression gradient along the dorsal-ventral and medial-lateral axes. In the kidney, CoPro identifies tubule-vasculature coordination that is essential in nephron function. These results demonstrate CoPros utility for analyzing spatial coordination of gene expression in complex tissues and disentangling overlapping biological processes, such as anatomical organization and disease-associated variation.

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Miao, Z., Qu, Y., Huang, S., Laux, L., Peters, S., Aristel, A., Zhang, Z., Niedernhofer, L. J., McMahon, A., Kim, J., Zhang, N.. 2026-04-21. Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro. https://doi.org/10.64898/2026.04.17.719309

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