학술논문
Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation
Document Type
Report
Author
Nakayasu, Ernesto S.; Gritsenko, Marina; Piehowski, Paul D.; Gao, Yuqian; Orton, Daniel J.; Schepmoes, Athena A.; Fillmore, Thomas L.; Frohnert, Brigitte I.; Rewers, Marian; Krischer, Jeffrey P.; Ansong, Charles; Suchy-Dicey, Astrid M.; Evans-Molina, Carmella; Qian, Wei-Jun; Webb-Robertson, Bobbie-Jo; Metz, Thomas O.
Source
Nature Protocols. August 2021, Vol. 16 Issue 8, p3737, 24 p.
Subject
Language
English
ISSN
1754-2189
Abstract
Author(s): Ernesto S. Nakayasu [sup.1] , Marina Gritsenko [sup.1] , Paul D. Piehowski [sup.1] , Yuqian Gao [sup.1] , Daniel J. Orton [sup.1] , Athena A. Schepmoes [sup.1] , Thomas [...]
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies. Mass-spectrometry-based proteomics is a powerful approach for discovering disease biomarkers. This tutorial provides advice on the study design, including cohort selection, evaluating statistical power, blinding and randomization, and quality control.
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies. Mass-spectrometry-based proteomics is a powerful approach for discovering disease biomarkers. This tutorial provides advice on the study design, including cohort selection, evaluating statistical power, blinding and randomization, and quality control.