Designing biomedical proteomics experiments: state-of-the-art and future perspectives

Abstract

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery

“…The first category of software exclusively quantifies peptides and proteins that were identified via MS/MS database searches prior to any statistical analysis to identify differential (poly)peptides. This is in contrast to the second category of software that quantifies yet unannotated LC-MS signals first from which differential features are detected that are subsequently annotated by MS/MS database search information [ 11 ]. The most common workflow is to first identify the peptides and subsequently quantify the LC-MS signals of these peptides [ 34 ].…”

“…Too small sample sizes, poorly defined research questions, incorrectly justified statistical analysis, statistical overfitting, lack of instrumental standardization, and validation costs are several causes for this phenomenon [ 8 , 9 , 10 ]. Multiple reviews are available on how to address these individual challenges but do not discuss how choices made in one component of the biomarker discovery process influence the decisions for another component [ 1 , 4 , 11 , 12 , 13 , 14 , 15 , 16 ]. This review aims to discuss chemometrical and statistical aspects of the complete biomarker discovery process for clinical proteomics.…”

The Human Proteome Organization–Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry

“…These di erent types of QC samples are not mutually exclusive; instead how they are used is closely linked to the experimental design [29], as they are each able to measure speci c performance characteristics, and they should be used in combination. One consideration is how many QC samples of each type should be used; another is how to interleave them with experimental samples [4].…”

Computational quality control tools for mass spectrometry proteomics

“…To anticipate this evolution, a shift to “quality by design” is now taking place . This means that the “designing and developing formulations and manufacturing processes ensure a predefined product quality.” As such, QA consists of multiple aspects of which quality control (QC) is an essential component, but other elements such as a careful experimental design are equally vital.…”