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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Related Experiment Video

Updated: Jun 27, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Aanchal Mongia^1,2, Fatema Tuz Zohora^3,4, Noah G Burget^1,2

¹Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Nature Communications

|May 3, 2024

Summary

This summary is machine-generated.

AnnoSpat accurately identifies cell types and spatial patterns in tissues using neural networks. This tool aids in understanding tissue organization and disease progression, like type 1 diabetes.

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Last Updated: Jun 27, 2025

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Area of Science:

Spatial biology
Computational pathology
Single-cell analysis

Background:

Cellular composition and spatial organization are crucial for organ function and disease.
Spatial single-cell proteomic assays like Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) enable high-throughput analysis of cells within intact tissues.
Challenges remain in accurate cell type annotation and quantifying cell-cell proximity in large-scale spatial datasets.

Purpose of the Study:

To develop AnnoSpat, a novel computational tool for automated cell type identification and spatial pattern analysis in tissues.
To address the unmet need for efficient analysis of atlas-scale spatial single-cell proteomic data.
To apply AnnoSpat to understand pancreatic islet pathobiology in type 1 diabetes.

Main Methods:

Development of AnnoSpat, integrating neural network and point process algorithms.
Application of AnnoSpat to analyze data from IMC and CODEX spatial proteomic assays.
Utilizing AnnoSpat on human pancreas datasets from type 1 diabetic, autoantibody-positive, and healthy donor cohorts.

Main Results:

AnnoSpat demonstrates superior performance in rapid and accurate cell type annotation compared to existing methods.
The tool effectively quantifies cell-cell proximity relationships within tissue microenvironments.
Analysis revealed known islet pathobiology and identified differential dynamics of pancreatic polypeptide (PP) cells and CD8+ T cell infiltration in type 1 diabetes.

Conclusions:

AnnoSpat provides a robust solution for automated cell type annotation and spatial pattern analysis in large-scale spatial single-cell proteomic datasets.
The tool enhances the understanding of tissue cellular architecture and its role in disease.
AnnoSpat facilitates novel insights into the progression of type 1 diabetes by characterizing cellular dynamics within pancreatic islets.