Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Quantitative proteomics is crucial for biological discovery.
• Understanding technical variability is essential for robust study design.
• Identifying sources of variability improves future experimental pipelines.

Purpose of the Study:

• To present a strategy for dissecting technical variability in quantitative liquid chromatography-mass spectrometry (LC-MS) proteomics.
• To apply this strategy to a label-free workflow for human brain tissue.
• To identify the major contributors to technical variability in the workflow.

Main Methods:

• Developed an experimental strategy replicating analyses at different processing stages.
• Divided a label-free LC-MS proteomics pipeline into four components: extraction, digestion, instrumental variance, and instrumental stability.
• Applied the methodology to human brain tissue samples.

Main Results:

• Quantified the contribution of each component to overall technical variability.
• Extraction accounted for the largest variability (72%).
• Instrumental variance (16%), instrumental stability (8.4%), and digestion (3.1%) contributed to a lesser extent.

Conclusions:

• Tissue extraction is the dominant source of variability in this quantitative proteomics workflow.
• The methodology effectively dissects technical variability in LC-MS proteomics.
• The platform demonstrates stability suitable for discovery proteomics studies.