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Immunoprecipitation

Immunoprecipitation

Immunoprecipitation, or IP, is a widely used technique that employs protein-antibody interactions to isolate proteins or protein complexes in their native state for studying protein-protein interactions, quaternary structures, or supramolecular complexes. Various modifications of the technique, including chromatin IP, cross-linking IP, and fluorescence IP, are commonly used.
Chromatin Immunoprecipitation
Chromatin immunoprecipitation, also known as ChIP, is used to study protein-DNA or...

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Related Experiment Video

Updated: Sep 15, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.

Måns Zamore¹, Sergio Mosquim Junior¹, Sebastian L Andree¹

¹Department of Immunotechnology, Lund University, SE-22363 Lund, Sweden.

Journal of Proteome Research

|July 14, 2025

Summary

This summary is machine-generated.

Proteomics data can now estimate immune cell composition in bulk samples. This study validates computational methods and introduces an R package for cell deconvolution using proteomics, achieving high accuracy in simulations.

Keywords:

LC-MS/MS cell-type deconvolution immune cells immune infiltration proteomics

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Last Updated: Sep 15, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

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Published on: October 15, 2021

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

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

Published on: November 15, 2017

Area of Science:

Proteomics
Computational Biology
Immunology

Background:

Bulk transcriptomics is common for cell-type deconvolution.
Proteomics data has been underutilized for cell deconvolution.
Estimating cell composition offers biological insights.

Purpose of the Study:

To evaluate computational methods for immune cell deconvolution using bulk proteomics data.
To assess the impact of preprocessing and software on deconvolution accuracy.
To introduce a tool for proteomics-based cell deconvolution.

Main Methods:

Utilized defined immune cell populations and simulated mixtures.
Assessed various preprocessing techniques and software tools.
Developed and validated an R package named proteoDeconv.

Main Results:

Demonstrated the feasibility of cell-type deconvolution using proteomics data.
Achieved Pearson correlations above 0.9 for estimated proportions in simulated mixtures.
Identified optimal parameters for missing value imputation and reference matrix generation.

Conclusions:

Proteomics data is a viable alternative for cell-type deconvolution.
The proteoDeconv R package simplifies proteomics data preprocessing for deconvolution.
This approach enhances the analysis of cell composition in biological samples.