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Biomarkers for detecting and discriminating early stages of the disease could contribute to an improved understanding of the associated etiology and pathogenicity, while informing new therapies and prevention targets 2, 3. For example, type 1 diabetes mellitus can be diagnosed by measuring blood glucose concentration, but the disease is known to be preceded by immunological changes sometimes years before clinical manifestation. Even for diseases with well-established diagnostic assays, such as diabetes, the discovery of new prognostic biomarkers can enable further studies on disease development and progression. More than 20,000 diseases have been reported to affect humans 1, of which only a small portion are supported by accurate, sensitive and specific diagnostic tests. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies. 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. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. This tutorial discusses important steps for designing and implementing a liquid-chromatographyвЂ“mass-spectrometry-based biomarker discovery study.

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. Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers.

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