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Related Concept Videos

Proteomics01:33

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 proteomics...

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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A framework for quality control in quantitative proteomics.

Kristine A Tsantilas1, Gennifer E Merrihew1, Julia E Robbins1

  • 1Department of Genome Sciences, University of Washington, Washington 98195, United States.

Biorxiv : the Preprint Server for Biology
|April 22, 2024
PubMed
Summary
This summary is machine-generated.

Implementing adaptable quality control (QC) measures for bottom-up proteomics ensures data reliability. This integrated approach assesses sample preparation, system function, and quantitative analysis for robust results.

Keywords:
DDADIAPRMliquid chromatographymass spectrometryproteomicsquality controlquantitative resultssample preparationsystem suitability

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Bottom-up proteomics workflows require rigorous quality control (QC) to ensure data reproducibility and accuracy.
  • Variability in sample preparation, instrument performance, and quantitative analysis can compromise proteomics data integrity.
  • A systematic approach to QC is essential throughout the entire proteomics workflow, from experimental design to data analysis.

Purpose of the Study:

  • To present adaptable quality control (QC) measures for bottom-up proteomics.
  • To demonstrate the application of QC strategies for assessing sample preparation, system function, and quantitative analysis.
  • To provide a framework for ensuring high-quality proteomics data collection and analysis.

Main Methods:

  • Utilized system suitability samples measured longitudinally with targeted methods across three instrument platforms.
  • Incorporated internal quality controls (QCs) at protein and peptide levels for sample preparation and system failure differentiation.
  • Employed external QC samples for consistency verification during batch correction and normalization.
  • Integrated rapid analysis software (Skyline), longitudinal QC metrics (AutoQC), and data deposition platforms (PanoramaWeb).

Main Results:

  • System suitability samples effectively identified severe system failures and tracked instrument function over extended periods.
  • Internal QCs differentiated between sample preparation issues and system failures.
  • External QCs confirmed the consistency and quantitative potential of experimental results.
  • The integrated QC approach facilitated rapid quality assessment and optimized data collection.

Conclusions:

  • An integrated quality control strategy is crucial for reliable bottom-up proteomics.
  • The proposed QC measures enhance the assessment of sample preparation, system performance, and quantitative accuracy.
  • This approach enables efficient use of instrument time for generating high-quality proteomics data.
  • The presented methods serve as a valuable starting point for laboratories seeking to improve proteomics data quality.