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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 6, 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

Generalized cell phenotyping for spatial proteomics with language-informed vision models.

Xuefei Julie Wang¹, Rohit Dilip², Yuval Bussi¹

¹Division of Biology and Biological Engineering, Caltech, Pasadena, CA.

Biorxiv : the Preprint Server for Biology

|November 28, 2024

Summary

This summary is machine-generated.

We developed a new AI model for cell phenotyping in spatial proteomics, improving accuracy and generalizability across diverse datasets. This approach enhances automated analysis for multiplexed imaging data.

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Spatial proteomics enables cell-level analysis but struggles with dataset generalization.
Existing methods face challenges with varying marker panels and data heterogeneity.

Purpose of the Study:

To develop a novel, generalizable cell phenotyping approach for spatial proteomics.
To create a language-informed vision model that adapts to diverse datasets and marker panels.

Main Methods:

Utilized a transformer with channel-wise attention for a language-informed vision model.
Trained the model on a diverse dataset with cell type labels from literature and the NIH Human BioMolecular Atlas Program (HuBMAP).

Main Results:

Demonstrated robust performance across various cell types, tissues, and imaging modalities.
Achieved superior accuracy and generalizability compared to existing methods through comprehensive benchmarking.

Conclusions:

The novel approach significantly advances automated spatial proteomics analysis.
Offers a generalizable and scalable solution for cell phenotyping in multiplexed imaging data.