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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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High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research

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From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network

Hanxi Xiao¹, Aaron Rosen¹, Prabal Chhibbar¹

¹Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.

Human Vaccines & Immunotherapeutics

|December 15, 2023

Summary

This summary is machine-generated.

High-throughput omic technologies generate large-scale immune profiles for vaccines and immunotherapies. Systems approaches integrate multi-omic data to reveal immunity mechanisms and therapeutic effects.

Keywords:

Systems immunology machine learning networks therapeutics vaccines

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Area of Science:

Immunology and Systems Biology
High-throughput omics
Translational medicine

Background:

Growing use of omic technologies for biological response profiling.
Need for systems approaches to understand complex biological interactions.
Development of novel vaccines, immunotherapies, and stem-cell therapies.

Purpose of the Study:

To summarize experimental data modalities for generating large-scale immune profiles.
To discuss analytical approaches for integrating multi-omic data.
To gain insights into immunity and therapy-induced immunomodulation.

Main Methods:

Characterization of genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome.
Application of systems approaches to integrate data across scales.
Utilizing machine learning and network analysis for data interpretation.

Main Results:

Multi-omic profiling enables comprehensive analysis of biological responses.
Systems approaches reveal intricate interplay between biological layers.
Machine learning and network methods facilitate insight generation.

Conclusions:

High-throughput omics provide powerful tools for immune profiling.
Integrated systems approaches are crucial for understanding complex biological systems.
These methods advance the study of immunity and therapeutic interventions.