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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: Mar 13, 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

Spatial Proteomics Using S4P.

Ritian Qin^1,2, Fuchu He^1,2, Weijie Qin²

¹School of Life Sciences, Tsinghua University, Beijing, China.

|March 12, 2026

Summary

This summary is machine-generated.

This study introduces a sparse sampling strategy for spatial proteomics (S4P) to map protein distribution in tissues. The method significantly reduces mass spectrometry time while achieving deep proteome coverage and whole-tissue mapping.

Keywords:

Deep learning Image reconstruction Mass spectrometry Sparse sampling Spatial proteomics

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Last Updated: Mar 13, 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

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Published on: October 31, 2025

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Published on: September 13, 2022

Area of Science:

Proteomics
Systems Biology
Biotechnology

Background:

Spatial proteomics is vital for understanding cellular functions in native tissue contexts.
Challenges in spatial proteomics include protein non-amplifiability and mass spectrometry sensitivity limitations.
Existing methods struggle with comprehensive tissue coverage and extensive analysis time.

Purpose of the Study:

To develop an efficient spatial proteomics protocol for whole-tissue mapping.
To overcome limitations of current spatial proteomics techniques.
To enable deep proteome coverage with reduced sampling.

Main Methods:

A novel sparse sampling strategy for spatial proteomics (S4P) was developed.
The strategy combines multi-angle tissue strip microdissection with deep learning-based image reconstruction.
Mass spectrometry was employed for protein identification and quantification.

Main Results:

The S4P method achieved whole-tissue slice coverage for spatial proteomics.
Over 9,000 proteins were mapped in mouse brain tissue at 525 μm resolution.
Mass spectrometry time was reduced by 50%-90% compared to traditional gridding methods.

Conclusions:

S4P significantly reduces sample processing time and mass spectrometry requirements.
The protocol enables deep proteome coverage and centimeter-sized tissue sample analysis.
This advancement facilitates high-resolution spatial protein distribution mapping in complex biological systems.