ArticleNature biotechnology2026
High-dimensional imaging using combinatorial channel multiplexing and deep learning.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Combinatorial group testing for efficient scaling across biological applications.Nature communications · 2026Article
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.bioRxiv : the preprint server for biology · 2026Article
- Spatial and Phenotypic Heterogeneity of ILC Subsets in Mouse Lung Under Type 2 Inflammatory Conditions.European journal of immunology · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- High-dimensional multiplexing through vortex electromagnetic wave manipulation by space-time-coding metasurfaces.Light, science & applications · 2026Article
- Compressed sensing expands the multiplexity of imaging mass cytometry.Nature communications · 2025Article
- Highly accurate image registration for 3D multiplexed cyclic imaging using dense labeling in expandable tissue gels.PLoS biology · 2025Article
- Emerging AI approaches for cancer spatial omics.GigaScience · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
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.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.