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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
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A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells
1School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China.
Journal of Pharmaceutical and Biomedical Analysis
|April 30, 2022
Summary
Data independent acquisition (DIA) is effective for analyzing scarce samples. Optimized DIA workflows, particularly using DIA-NN with gas-phase fractionation libraries, enable robust proteomic analysis of limited cell quantities.
Area of Science:
- Proteomics
- Mass Spectrometry
- Systems Biology
Background:
- Data independent acquisition (DIA) offers high reproducibility and throughput for proteomic analysis.
- Current DIA methods often require large sample amounts and extensive fractionation for library generation.
- Analyzing limited samples presents a challenge for conventional DIA approaches.
Purpose of the Study:
- To evaluate the utility of DIA for analyzing samples with limited quantities.
- To compare different software tools and library generation strategies for DIA analysis of scarce samples.
- To establish an optimized DIA workflow for clinical applications using limited cell input.
Main Methods:
- Systematic comparison of eight DIA analysis pipelines using DIA-NN, Spectronaut, and EncyclopeDIA software.
- Generation of experiment-specific, FASTA database, and gas-phase fractionation (GPF) libraries.
- Analysis of HEK293T cell tryptic peptides across various sample loads (500 ng down to 4 ng) and peripheral blood mononuclear cells (PBMCs).
Main Results:
- DIA-NN combined with GPF-based libraries demonstrated superior performance in protein identification and retention time calibration for low-input samples.
- The optimized workflow successfully quantified 3179 protein groups from 5000 PBMCs stimulated with lipopolysaccharide (LPS).
- Functional analysis of PBMC data revealed significant activation of key signaling pathways, including endocytosis, NF-kappa B, and T cell receptor signaling.
Conclusions:
- DIA is a practical technique for proteomic analysis of scarce samples.
- The developed DIA workflow is adaptable for biomarker discovery and immune status evaluation in clinical settings.
- This approach holds promise for monitoring drug responses, particularly in immune-related diseases.
