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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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Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

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Integrated workflow for analysis of immune enriched spatial proteomic data with IMmuneCite.

Arianna Barbetta¹, Sarah Bangerth¹, Jason T C Lee¹

¹Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA.

Scientific Reports

|March 19, 2025

Summary

This summary is machine-generated.

IMmuneCite is a new computational tool that precisely identifies 32 immune cell phenotypes from spatial proteomics data. This framework enhances immune microenvironment analysis across species and conditions.

Keywords:

Immune microenvironment Informatics pipeline Single cell proteomics Spatial biology

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Area of Science:

Immunology
Computational Biology
Proteomics

Background:

Spatial proteomics offers single-cell resolution for tissue analysis.
Accurate cell segmentation and phenotyping remain significant challenges.
Complex immune landscapes require specialized computational tools.

Purpose of the Study:

Introduce IMmuneCite, a computational framework for spatial proteomics.
Improve the accuracy of immune cell phenotype identification and dataset creation.
Facilitate high-fidelity analysis of the immune microenvironment.

Main Methods:

Developed IMmuneCite for comprehensive image pre-processing and single-cell dataset creation.
Applied the framework to human and murine liver tissue spatial proteomics data.
Validated its ability to identify discrete immune cell phenotypes and reduce nonbiological clusters.

Main Results:

IMmuneCite identified 32 discrete immune cell phenotypes in human liver samples.
The framework successfully reduced nonbiological cell clusters caused by marker co-localization.
Demonstrated versatility across species (human, murine) and antibody panels.
Enabled deep characterization of immune microenvironments in liver transplantation and cancer models.

Conclusions:

IMmuneCite is a user-friendly, integrated computational platform for immune microenvironment investigation.
It ensures creation of immune-focused, spatially resolved single-cell proteomic datasets.
Facilitates high-fidelity, biologically relevant analyses across different species.