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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 12, 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

Cross-level Cross-Scale Inference and Imputation of Single-cell Spatial Proteomics.

You Wu¹, Lei Xie^1,2,3

¹Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, USA.

Research Square

|August 6, 2025

Summary

This summary is machine-generated.

scProSpatial is a deep learning framework that reconstructs single-cell spatial proteomics from scRNA-seq data. It overcomes limitations in current omics technologies, enabling broader biological insights.

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Last Updated: Sep 12, 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

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

Published on: November 20, 2018

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

Area of Science:

Genomics
Proteomics
Computational Biology

Background:

Single-cell and spatial omics technologies have advanced biological research but face challenges.
Limitations include batch effects, lack of multi-modal measurements, limited protein coverage, and poor generalization.
Insufficient spatial context at single-cell resolution hinders understanding molecular drivers.

Purpose of the Study:

Introduce scProSpatial, a unified deep learning framework.
Infer and impute high-fidelity single-cell spatial proteomics from scRNA-seq.
Address limitations of current experimental omics methods.

Main Methods:

Developed scProSpatial, a multi-modal, multi-scale deep learning framework.
Framework infers and imputes spatial proteomics from single-cell RNA sequencing (scRNA-seq) data.
Utilized comprehensive evaluations and a case study in metastatic breast cancer.

Main Results:

scProSpatial accurately predicts spatial protein abundances without shared transcriptomics features.
Expanded protein coverage by 50 times compared to existing methods.
Demonstrated robust generalization to out-of-distribution scenarios.

Conclusions:

scProSpatial effectively overcomes key challenges in single-cell spatial proteomics.
The framework facilitates cross-level and cross-scale multi-omics integration.
Enables deeper insights into complex biological systems, such as metastatic breast cancer.