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Peptide Identification Using Tandem Mass Spectrometry

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Cell-Lineage Guided Mass Spectrometry Proteomics in the Developing Frog Embryo

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Published on: April 21, 2022

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data.

Pang-Ning Teng^1,2, Joshua P Schaaf^1,2, Tamara Abulez^1,2

¹Gynecologic Cancer Center of Excellence and the Women's Health Integrated Research Center, Annandale, VA 22003, USA.

|March 5, 2024

Summary

This summary is machine-generated.

This study shows that protein data can accurately estimate cancer cell admixture, similar to gene expression data. Optimized protein signatures are now available for researchers to analyze bulk tumor proteomic data.

Keywords:

Computational bioinformatics Proteomics Transcriptomics

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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:

Cancer Research
Proteomics
Transcriptomics
Bioinformatics

Background:

Multi-omic studies reveal poor correlation between transcript and protein levels in cancer tissues.
Existing cell admixture deconvolution tools primarily rely on transcriptomic data.
Accurate estimation of cell type proportions (cell admixture) is crucial for understanding tumor microenvironment heterogeneity.

Purpose of the Study:

To evaluate the utility of protein abundance data for estimating cell admixture in cancer.
To develop and optimize protein-based signatures for cell type proportion estimation.
To benchmark these protein signatures using bulk tumor proteomic data from high-grade serous ovarian cancer (HGSOC) patients.

Main Methods:

Analysis of proteome and transcriptome data from contrived cell admixtures (tumor, stroma, immune cells).
Utilized laser microdissection to harvest cells from HGSOC tumor microenvironments.
Applied two deconvolution algorithms (ESTIMATE, ConsensusTME) using co-quantified transcript and protein data.
Developed and validated protein-based signatures for cell admixture estimation.

Main Results:

Co-quantified transcripts and proteins demonstrated comparable performance (r ≥ 0.63) in estimating stroma and immune cell admixture using standard deconvolution algorithms.
Optimized protein signatures effectively estimated cell admixture proportions from bulk tumor proteomic data.
Benchmarking was performed on proteomic data from over 150 HGSOC patients.

Conclusions:

Protein abundance data is a viable and effective alternative to transcriptomic data for estimating cell admixture in cancer.
Developed protein signatures provide a valuable resource for deconvolution analysis of bulk tumor proteomic datasets.
These findings facilitate a deeper understanding of tumor microenvironment composition using proteomic approaches.