Abstract
Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each protein, limiting throughput and scalability. Here, we present combinatorial multiplexing (CombPlex), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of measured proteins. Every protein can be imaged in several channels and every channel contains agglomerated images of several proteins. These combinatorically compressed images are then decompressed to individual protein images using deep learning. We achieve accurate reconstruction when compressing the stains of 22 proteins to five imaging channels. We demonstrate the approach both in fluorescence microscopy and in mass-based imaging and show successful application across multiple tissues and cancer types. CombPlex can escalate the number of proteins measured by any imaging modality, without the need for specialized instrumentation.
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Data availability
Experimental data can be found on the BioImage Archive (https://www.ebi.ac.uk/biostudies/studies/S-BIAD873). A list of reagents and instruments can be found in Supplementary Table 1 under ‘key resources’.
Code availability
The code for CombPlex can be found on GitHub (https://github.com/KerenLab/CombPlex).
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Acknowledgements
L.K. is supported by the Enoch, Azrieli, Sharon Levine and Rising Tide foundations and grants funded by the Schwartz/Reisman Collaborative Science Program, the European Research Council (948811) and the Israel Science Foundation (2481/20, 3830/21) within the Israel Precision Medicine Partnership program. I.M. is supported by an EU Horizon 2020 MSCA Individual Fellowship (890733). S.B. is a Robin Chemers Neustein AI Fellow and acknowledges funds from the Carolito Stiftung and the Nvidia Applied Research Accelerator Program. C.M.S. is supported by the European Research Council (CAR-TIME, 101116768), the German Research Foundation (INST 37/1228-1, INST 37/1302-1 FUGG and Germany’s Excellence Strategy EXC 2180-390900677), the Swiss Life Jubiläumsstiftung, the Mach-Gaensslen Stiftung Schweiz, the American Society of Hematology Research Restart Award and the State of Baden-Württemberg within the Centers for Personalized Medicine Baden-Württemberg. O.E. is supported by the Israel Cancer Research Foundation. Phenocycler imaging was made possible thanks to the support of de Picciotto Cancer Cell Observatory, in memory of Wolfgang and Ruth Lesser. This research was partially supported by the Israeli Council for Higher Education through the Weizmann Data Science Research Center.
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L.K. designed the study and supervised the work. Experiments were performed by R.B.-U. Validation experiments were performed by R.B.-U., O.E. and A.R. with support from C.M.S., I.G., T.M.S., Y.A. and Y.B. L.B.S. led the algorithmic development. L.B.S. and D.S. performed the analyses and computational experiments with supervision from S.B. O.B.-T. conceptualized the deep learning approach. R.B.-U. developed the method for multitag conjugation. I.M., N.M. and T.K.H. provided the training data. R.B.-U., L.B.S. and D.S. generated the figures. L.K. wrote the paper with support from R.B.-U., L.B.S., O.E., D.S., S.B. and all other coauthors.
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C.M.S. is a scientific advisor to AstraZeneca and is on the scientific advisory board of, has stock options in and has received research funding from Enable Medicine, all outside the current work. R.B.-U., L.B.S., O.B.-T. and L.K. are authors of patent P-621615-IL on combinatorial staining for multiplexed imaging. The remaining authors declare no competing interests.
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Supplementary Figs. 1–10, with legends.
Supplementary Table 1
List of all reagents and antibodies used in this study, including the key resources.
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Ben-Uri, R., Ben Shabat, L., Shainshein, D. et al. High-dimensional imaging using combinatorial channel multiplexing and deep learning. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02585-0
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DOI: https://doi.org/10.1038/s41587-025-02585-0
