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Related Concept Videos

Proteomics01:33

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 proteomics...

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Related Experiment Video

Updated: Jul 3, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Graphical and interactive spatial proteomics image analysis workflow.

Pritpal Singh1, Jocelyn H Wright2, Kimberly S Smythe2

  • 1School of Engineering and Technology, University of Washington Tacoma, WA, USA.

Gigabyte (Hong Kong, China)
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

We developed a flexible, interactive spatial proteomic image analysis workflow. This tool enables reproducible, customized analysis of protein expression and localization in tissues, aiding biomedical research.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial proteomics offers insights into protein expression and localization within cells and tissues.
  • Existing spatial proteomic analysis workflows often lack integration, flexibility, and interactive visualization capabilities.
  • Biomedical researchers require robust, reproducible, and customizable imaging analysis workflows.

Purpose of the Study:

  • To present a modular, interactive, and containerized spatial proteomic image analysis workflow.
  • To empower researchers with a tool for reproducible and customizable complex spatial proteomic data analysis.
  • To facilitate visualization and interpretation of spatial proteomic data.

Main Methods:

  • The workflow integrates cell segmentation, unsupervised clustering with optional batch correction, and cluster validation.
  • A form-based graphical interface allows for single-click execution, customization, and interactive adjustment of analysis steps.
  • Containerization ensures reproducibility and flexibility across different datasets and parameters.

Main Results:

  • The workflow successfully performs multi-step spatial proteomic analyses, including cell type clustering and visualization.
  • Demonstrated functionality on human normal tonsil and colorectal cancer tissues using high-plex immunohistochemistry.
  • The interactive interface allows for dynamic adjustment of image processing and analysis parameters.

Conclusions:

  • The presented workflow provides an end-to-end solution for spatial proteomic image analysis.
  • Its modular and interactive design enhances reproducibility and customization for biomedical researchers.
  • This tool facilitates deeper understanding of protein expression and localization in complex biological samples.