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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 proteomics...

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Updated: May 14, 2026

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

Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies.

Sarah Samorodnitsky^1,2, Michael C Wu^1,2

¹Public Health Sciences Division, Fred Hutchinson Cancer Center.

Biorxiv : the Preprint Server for Biology

|May 13, 2026

Summary

This study introduces TOASTER, a novel method for spatial proteomics analysis. TOASTER bypasses cell segmentation to directly link protein expression patterns to patient outcomes, improving accuracy and power.

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Last Updated: May 14, 2026

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

Biomedical Imaging
Computational Biology
Proteomics

Background:

Multiplexed spatial proteomics generates high-resolution tissue images, but pre-processing for cell segmentation and phenotyping can be error-prone.
Existing methods often overlook direct protein expression levels and rely on potentially inaccurate cell arrangements.
Emerging research indicates topological analysis of spatial proteomics data may offer greater analytical power.

Purpose of the Study:

To develop a novel method, TOASTER, for analyzing spatial proteomics data.
To circumvent the limitations of cell segmentation and phenotyping in spatial proteomics.
To associate continuous spatial protein expression directly with patient-level outcomes.

Main Methods:

TOASTER utilizes topological data analysis to characterize topological features in spatial protein expression.
It employs an adaptation of the Nelson-Aalen cumulative hazard function to summarize topological structure.
The method associates this summary with outcomes using functional data analysis, gridwise testing, or kernel association testing.

Main Results:

Simulations demonstrate TOASTER improves statistical power and controls Type I error rates.
The method shows robustness even with image artifacts like gaps or tears.
Application to triple-negative breast cancer data revealed associations between protein expression topology and immunotherapy response.

Conclusions:

TOASTER offers a powerful, segmentation-free approach for spatial proteomics.
It effectively links spatial protein expression patterns to patient outcomes.
This method enhances the analysis of complex biological systems and has implications for precision medicine.