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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 22, 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

A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.

Sarah Samorodnitsky^1,2, Katie Campbell³, Antoni Ribas³

¹Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle 98109, USA.

Bioinformatics (Oxford, England)

|July 1, 2024

Summary

This summary is machine-generated.

We developed a new method, SPatial Omnibus Test (SPOT), to analyze immune cell spatial clustering in tumors. SPOT improves the analysis of spatial patterns and their relation to patient outcomes, offering clinical insights.

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Last Updated: Jun 22, 2025

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Published on: April 18, 2025

Area of Science:

Computational Biology
Cancer Research
Immunology

Background:

Spatial proteomics is crucial for understanding the tumor immune microenvironment.
Analyzing spatial clustering of immune cells can provide clinical insights.
Current methods require pre-specifying a radius, lacking a standardized approach.

Purpose of the Study:

To introduce a novel statistical method, the SPatial Omnibus Test (SPOT), for analyzing spatial clustering in the tumor immune microenvironment.
To evaluate the association between spatial patterns and patient outcomes across a range of radii.
To provide a robust and flexible tool for spatial analysis in cancer research.

Main Methods:

SPOT analyzes spatial clustering across multiple candidate radii.
It evaluates the association between spatial summaries and outcomes, adjusting for confounders.
Results are aggregated using the Cauchy combination test to yield an omnibus P-value.

Main Results:

Simulations confirm SPOT controls type I error rates.
SPOT demonstrates enhanced statistical power compared to alternative methods.
The method was successfully applied to ovarian and lung cancer datasets.

Conclusions:

SPOT offers a comprehensive approach to analyzing spatial immune cell organization in tumors.
The method provides a robust P-value for the overall degree of spatial association.
SPOT has potential applications in clinical outcome prediction and cancer research.