COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Robert T Heussner, Cameron F Watson, Christopher Z Eddy, Kunlun Wang, Eric M Cramer, Allison L Creason, Gordon B Mills and Young Hwan Chang

PLOS Computational Biology, 2025, vol. 21, issue 8, 1-22

Abstract: Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5μm-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTI’s panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.Author summary: Multiplex tissue imaging (MTI) allows researchers to study tissue samples by measuring many proteins in individual cells while preserving their spatial organization, providing critical insights into the tumor microenvironment — the complex system of cells and structures surrounding tumors that influences cancer progression and treatment. However, current MTI platforms are limited in the number of biomarkers that can be stained simultaneously on a single tissue section due to panel size restrictions or assay incompatibilities. To expand the number of proteins measured, researchers often apply different panels to consecutive thin tissue sections. While image registration can align these sections, integrating them at the single-cell level remains challenging due to biological heterogeneity and differences in cell composition across slices. Existing methods often fail to fully incorporate both molecular and spatial information. To address this, we developed COEXIST, a computational algorithm that combines shared molecular profiles with spatial information to match cells across serial sections, enabling more complete and accurate tissue profiling. COEXIST improves MTI utility, enhances single-cell resolution, and offers opportunities for integrating MTI with spatial transcriptomics for deeper biological insight.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013325

DOI: 10.1371/journal.pcbi.1013325

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