ArticleNature communications2024
AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spatium: A Protein Language Foundation Model for Spatial Proteomics.bioRxiv : the preprint server for biology · 2026Article
- NicheProt: Cell-type-resolved proteomics of tissue compartments.Science advances · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications.Precision clinical medicine · 2026Review
- Developing an interpretable machine learning model via SHAP to predict HCC postoperative survival based on tumor immune microenvironment CODEX immunomics and MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression.Nature communications · 2026Article
- Article
- Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression.bioRxiv : the preprint server for biology · 2025Article
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- Cell-cell communication: new insights and clinical implications.Signal transduction and targeted therapy · 2024Review
Corrections and comments
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13 authors.
Funding
Abstract
Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8
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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.