A method for assessing and maintaining the reproducibility of mass spectrometric analyses of complex samples (original) (raw)

Sampling and analytical strategies for biomarker discovery using mass spectrometry

BioTechniques, 2006

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample ret...

Protein mass spectra data analysis for clinical biomarker discovery: a global review

Briefings in Bioinformatics, 2011

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers. Efficient pre-processing is an essential pre-requisite for retrieving meaningful proteomic biological information from raw spectra and reaching meaningful clinical conclusions. The identification and validation of pertinent biomarkers requires large, well-designed studies. Methodology improvement would benefit from a tight collaboration between biostatisticians, computer scientists, biologists and clinicians.

Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery

BMC Bioinformatics, 2009

Background: Mass spectrometry-based biomarker discovery has long been hampered by the difficulty in reconciling lists of discriminatory peaks identified by different laboratories for the same diseases studied. We describe a multi-statistical analysis procedure that combines several independent computational methods. This approach capitalizes on the strengths of each to analyze the same high-resolution mass spectral data set to discover consensus differential mass peaks that should be robust biomarkers for distinguishing between disease states.

Using data-independent, high resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications

PROTEOMICS - Clinical Applications, 2015

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Applications of Mass Spectrometry for Clinical Diagnostics: The Influence of Turnaround Time

Analytical Chemistry, 2019

This critical review discusses how the need for reduced clinical turnaround times has influenced chemical instrumentation. We focus on the development of modern mass spectrometry (MS) and its application in clinical diagnosis. With increased functionality that takes advantage of novel front-end modifications and computational capabilities, MS can now be used for non-traditional clinical analyses, including applications in clinical microbiology for bacteria differentiation and in surgical operation rooms. We summarize here recent developments in the field that have enabled such capabilities, which include miniaturization for point-of-care testing, direct complex mixture analysis via ambient ionization, chemical imaging and profiling, and systems integration.

Mass Spectrometry Technology for Protein Biomarker Discovery

Imaging Journal of Clinical and Medical Sciences, 2016

Human tissues and organs could be affected depending on pathological condition of diseases. Proteins are basic functioning molecules of cells accordingly to modifi cation intensities. Protein modifi cation can be different in cellular interaction, localization, activity, protein concentration, and co-/posttranslational. Protein biomarker discovery has covered some subtitles or points such as differentially expressed proteins disease specifi c protein isomers and abnormal protein activity [1,2].

Mining whole-sample mass spectrometry proteomics data for biomarkers - An overview

Expert Systems With Applications, 2009

Biomarkers are proteins or other components of a clinical sample whose measured intensity alters in response to a biological change such as an infection or disease, and which may therefore be useful for prediction and diagnosis. Proteomics is the science of discovering, identifying and understanding such components using tools such as mass spectrometry. In this paper we aim to provide a concise overview of designing and conducting an MS proteomics study in such a way as to allow statistical analysis that may lead to the discovery of novel markers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practice for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its advantage in speed and low cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum designed to pinpoint novel biomarkers for bovine tuberculosis.

Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation

Methods in molecular biology (Clifton, N.J.), 2017

Blood protein measurements are used frequently in the clinic in the assessment of patient health. Nevertheless, there remains the need for new biomarkers with better diagnostic specificities. With the advent of improved technology for bioanalysis and the growth of biobanks including collections from specific disease risk cohorts, the plasma proteome has remained a target of proteomics research toward the characterization of disease-related biomarkers. The following protocol presents a workflow for serum/plasma proteomics including details of sample preparation both with and without immunoaffinity depletion of the most abundant plasma proteins and methodology for selected reaction monitoring mass spectrometry validation.

Analysis of mass spectral serum profiles for biomarker selection

Bioinformatics/computer Applications in The Biosciences, 2005

Motivation: Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. Results: The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum.