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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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Updated: Dec 18, 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

Moving Profiling Spatial Proteomics Beyond Discrete Classification.

Oliver M Crook¹, Tom Smith¹, Mohamed Elzek¹

¹Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK.

|June 20, 2020

Summary

This summary is machine-generated.

Spatial proteomics reveals protein locations within cells. This study critiques current methods, highlighting challenges in tracking dynamic protein movement and suggesting Bayesian modeling for improved analysis of spatial proteomes.

Keywords:

organelle protein localization

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Last Updated: Dec 18, 2025

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

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Published on: July 6, 2022

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Published on: November 15, 2017

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

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

Published on: April 18, 2025

Area of Science:

Cell biology
Proteomics
Bioinformatics

Background:

The spatial subcellular proteome is dynamic, influenced by molecular cues and post-translational modifications.
Protein compartmentalization and dynamics enable diverse cellular functions.
Spatial proteomics is key for identifying protein localization and dynamics.

Purpose of the Study:

To critique existing analytical approaches in spatial proteomics.
To identify challenges in analyzing multi-localization and dynamic protein relocalization.
To propose advanced statistical frameworks for spatial proteomics.

Main Methods:

Critique of current analytical methods in spatial proteomics.
Identification of limitations in analyzing protein multi-localization and dynamics.
Proposal for Bayesian modeling as a superior analytical approach.

Main Results:

Current analytical methods for spatial proteomics have limitations.
Multi-localization and dynamic relocalization present significant analytical challenges.
Bayesian modeling offers clear benefits over existing methods for spatial proteomics data.

Conclusions:

Robust statistical frameworks, particularly Bayesian modeling, are needed to address current limitations in spatial proteomics.
Addressing these challenges will enhance the utility of spatial proteomics for biological discovery.
Continued development in analytical processing is crucial for advancing the field of spatial proteomics.