Authors: Simon Sugár | László Drahos | Károly Vékey
Source: Journal of Mass Spectrometry (article, 2023-02-07)
Field: Chemistry — Spectroscopy

Quantitative proteomics I.: Concept, design, and planning of quantitative proteomics experiments Quantitative proteomics is one of the most widespread applications of mass spectrometry. We have planned a series of short tutorials to help orient PhD students and young post-docs mostly focused on label-free experiments. This is not designed to be a comprehensive textbook, but an open-ended series discussing diverse issues. In this first tutorial we summarize major aspects of study design. In the second, we will discuss MaxQuant, a widely used software for evaluating LC-MS data in terms of protein abundances. The following two issues will deal with selecting proteins which can be reasonably quantified in an experiment, and how to deal with the often-missing values in protein abundance tables. The first step before designing an experiment is to determine the concept of the project. We must decide very clearly on what the objective is, and subsequently what questions need to be asked and what kind of answers we expect. Only after this can we design the experiments comprehensively. This might seem trivial, but incorrectly formulated experimental questions may lead to erroneous inferences, or to much lower impact and significance than hoped for. After conceptualizing the project, several things must be thoroughly examined regarding the experimental questions before further planning can commence. First, our questions should be testable and the experiments to be performed should be selected accordingly, for example, it must be decided if relative or absolute quantitation is required. Second, they should be defined before doing any experimental work. This is crucial to avoid false positives, for example, to employ FDR control correctly when performing multiple statistical tests. Third, response variables (typically protein abundances) should be reliably measurable, for example, their abundances should be over the limit of quantitation. Finally, it must be assessed whether we are focusing on the correct explanatory variables. This is especially important to avoid mistaking correlation for causality, for example, the age of patients should be considered when risk factors are evaluated. A pilot study is exploratory in nature. It is primarily used to characterize effect sizes (the standardized difference) between group means. Although analyzing more samples improves statistical power (the probability of obtaining true positive results), it also requires more effort (time and cost). In practice there is usually a compromise between these; ideally a pilot study should include 10, 15, or 25 samples per group if effect sizes are large, medium, or small, respectively.3 Because effect sizes are usually not known beforehand, they need to be approximated based on prior knowledge of the sample or species under investigation. Pilot studies are often sufficient to point research in novel directions and to push back frontiers of our knowledge. Most publications using quantitative proteomics are pilot studies and are within the range of the financial power of universities and academia. Their major limitation lies in medical research, primarily due to their inability to account for the heterogeneity of human population. The experimental and technical design of a study involves careful planning of the sample preparation, analysis, data handling, and statistical analysis. First, it has to be decided if mass spectrometry based proteomics is the best experimental approach. For this, the possibilities and limitations of MS-based quantitative proteomics need to be understood. It is most frequently based on the measurement of LC-MS peak intensities or areas; however, this can be affected by several factors apart from analyte concentrations, for example, matrix effects, instrument parameters, and differences in ionization efficiencies. Because of these, there are two fundamental approaches to quantitation: relative and absolute quantitation. In relative quantitation two (or more) samples are compared to each other to determine the ratio of different analytes between them, which can be done with the use of stable isotope labeling (chemical, proteolytic, or metabolic labeling) or without it (label-free quantitation). On the other hand, in absolute quantitation protein concentrations (e.g., the concentration of an antibody species in human plasma) can be determined for individual samples with the help of stable isotope labeled internal standards. Although labeling-based strategies (both for relative and absolute quantitation) are more accurate and reproducible than label-free quantitation, they are more expensive, labor-intensive, and not applicable in every circumstance (e.g., for the analysis of human tissue samples). As for the majority of scientific projects that require quantitative proteomics label-free quantitation is sufficient, it is the method of choice in most cases. There is a multitude of experimental methods used in proteomics (sample preparation, HPLC-MS analysis, and data handling);4, 5 selecting and optimizing these is the main task of the analytical chemist. Discussing, or even listing these is out of the scope of this tutorial. Although the method of choice influences the planning process, there are universal concepts needed to be taken into account. The most important parts are the testing of the suitability of the system6—usually through preliminary studies; the randomization of sample preparation and analysis steps7—helping to reduce batch effects; and the implementation of experimental controls,8 which then can be used to monitor the state of the system during the experiments. The latter is especially important to track and minimize unwanted variations introduced into the data during the different steps of the analysis. For example, one may add standards (exogenous proteins or peptides) to the samples to monitor experimental errors, and use quality control samples to show system integrity. Data analysis and statistical methods used can also be tested for robustness via sensitivity analyses, which can provide information on how much the results are affected by the use of different methods. This is becoming more and more important in light of the large number of emerging data analysis solutions available.9 Designing and thoroughly planning a scientific project is an essential part of any project. Incorrectly formulated experimental questions or inadequately planned experiments can make the conclusions of the study invalid regardless of how well other aspects have been executed. Therefore, we recommend investing considerable time and effort into thoroughly planning every part of a project, and revising it several times before its initiation. This short tutorial is by no means comprehensive, but can serve as a starting point or a sort of guidebook summarizing the most important aspects to be considered before starting any experimental work. Data sharing is not applicable as no new data were generated or the article describes entirely theoretical research. Data science ; False positive paradox ; Computer science ; Quantitative proteomics ; Proteomics ; Chemistry ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications