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Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical

Sudhir Srivastava1,2, Michael Merchant3,4, Anil Rai1

  • 1Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.

Journal of Proteomics & Bioinformatics
|March 10, 2020
PubMed
Summary
This summary is machine-generated.

Formalin-fixed paraffin embedded (FFPE) tissue storage and one-step extraction methods improve proteomics data quality. Imputation is superior to data exclusion for handling missing values in quantitative mass spectrometry analysis.

Keywords:
ANOVAImputationProteinsTechnical variabilityTissue extractionTissue storage

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Quantitative proteomics using liquid chromatography-mass spectrometry (LC-MS) faces challenges with missing values and technical variability.
  • Human kidney biopsy material was used to assess technical effects on quantitative MS data.

Purpose of the Study:

  • To investigate the impact of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on LC-MS proteomics data.
  • To evaluate strategies for handling data heterogeneity and missing values (MVs).

Main Methods:

  • Comparison of two TSMs: frozen (FR) and formalin-fixed paraffin embedded (FFPE).
  • Evaluation of three TEMs: MAX, TX followed by MAX, and SDS followed by MAX.
  • Application of analysis of variance (ANOVA) to model sources of variability and assess data analysis strategies.

Main Results:

  • FFPE TSM demonstrated superior performance compared to FR TSM.
  • The one-step MAX TEM was more effective than two-step TEMs.
  • Imputation methods proved more effective than excluding proteins with MVs or using unbalanced designs.

Conclusions:

  • FFPE storage and single-step MAX extraction are recommended for improved proteomics data quality.
  • Data imputation is the preferred strategy for managing missing values in quantitative proteomics datasets, outperforming data exclusion or unbalanced analysis.