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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: Sep 9, 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

Spatial Multiplexing and Omics.

Julienne L Carstens^1,2, Santhoshi N Krishnan^3,4, Arvind Rao^3,4,5,6,7

¹Department of Medicine, Division of Hematology & Oncology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.

Nature Reviews. Methods Primers

|August 29, 2025

Summary

This summary is machine-generated.

This primer explores multi-omics and spatial technologies for biological research. It emphasizes integrating spatial data across scales to solve physiological mysteries and foster innovation.

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Last Updated: Sep 9, 2025

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Published on: July 6, 2022

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Biological sciences
Physiological research
Multi-omics and spatial biology

Background:

Understanding physiological processes requires integrating who, what, when, and where information.
Multi-omics approaches identify cellular/molecular identities, while longitudinal data captures temporal aspects.
Spatial dimensions are crucial for a complete understanding of biological systems.

Purpose of the Study:

To review current technologies for multi-scale biological quantification, focusing on spatial data integration.
To provide a conceptual framework for comparing and understanding major spatial technologies.
To encourage cross-disciplinary collaboration by uniting disparate spatial platforms and biological scales.

Main Methods:

Discussion of experimental design considerations: targeted vs. untargeted approaches, sample types, and biological scales.
Overview of four main classes of molecule detection technologies.
Exploration of spatial analytics, including questioning approaches, analytical platforms, image segmentation, and sampling.

Main Results:

Highlights the importance of retaining spatial data for comprehensive biological analysis.
Presents an apples-to-apples comparison of major spatial technologies.
Identifies considerations for reproducibility, limitations, and future outlook in spatial biology.

Conclusions:

Integrating spatial dimensions across biological scales is key to advancing physiological research.
A unified conceptual framework and technology comparison can drive innovation in spatial biology.
Fostering cross-disciplinary dialogue is essential for leveraging the full potential of spatial multi-omics.