ArticleNature methods2026
Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- 3D ex vivo platform for high-throughput immunotherapy screening in relapsed/refractory lymphoma patients.HemaSphere · 2026Article
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Highly multiplexed immunofluorescence imaging visualizes and quantifies protein levels at single-cell resolution in intact tissues at low cost and high scalability. Analysis of these data involves multiple steps with many method and parameter choices that must be adapted to the data and analytical objectives. There is an unmet need for a toolbox that offers flexible end-to-end coverage of the workflow. Here we present 'spatialproteomics', a Python package that addresses these challenges. Spatialproteomics enables the processing and analysis of large imaging data, including steps such as segmentation, image processing and cell-type classification, while synchronizing shared coordinates across data modalities. We demonstrate spatialproteomics on images of reactive lymph nodes and B cell non-Hodgkin lymphomas from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of how cell type composition and spatial distribution vary across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process Gigapixel whole-slide images.
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.