ReviewFundamental research2026
The evolving landscape of spatial proteomics technologies in the AI age.
Review in Fundamental research, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- Towards Cellular Resolution of Tryptic Peptides in Tissue Sections by MALDI MS Imaging: A Focus on Enzyme Application and Reproducibility.Analytical chemistry · 2026Article
- Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Single-Cell Protein Assays in Context: From 2D to 3D and In Situ Analysis.Annual review of analytical chemistry (Palo Alto, Calif.) · 2026Review
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although single-cell technologies have provided deep insights into cellular heterogeneity and complexity, they fall short in explaining how cells form tissue structures, a crucial aspect for understanding the principles of complex tissues. Recently, spatial transcriptomics has begun to fill this gap, allowing in situ studies of tissues at cellular and subcellular resolution. However, these genomic-level methods primarily provide indirect measurements of cellular states, as most biological processes are controlled by proteins. Therefore, spatial proteomics has the potential to revolutionize our understanding of biological processes, with significant implications for both basic cell biology and clinical applications. In this review, we provide an overview of the recent technical achievements and remaining challenges in spatial proteomics. Specifically, we categorize the techniques into three main types: antibody-based, LC-MS/MS-based, and imaging mass spectrometry-based. We describe each method in detail and discuss its strengths and weaknesses. We also discuss the emerging opportunities of artificial intelligence for spatial proteomics. Finally, we review key issues and suggest future directions for the advancement of spatial proteomics.
Indexed as
Identifiers
What OpenQuestion holds
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.