This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Proteomics is defined as the system-wide characterization of all the proteins in an organism in terms of their sequence, localization, abundance, post-translational modifications, and biomolecular interactions.
Modern proteomic investigations are increasingly quantitative and comprehensive . Examples include the relative quantification of over 4, proteins in haploid and diploid yeast, which identified the pheromone signaling pathway as enriched in differential abundance  ; determination of site- and time-specific dynamics of more than 6, phosphorylation sites of HeLa cells stimulated with epidermal growth factor  ; and characterization of multiprotein complexes in Saccharomyces cerevisiae , which proposed new cellular roles for proteins .
Such investigations are now successfully utilized in functional biology  ,  , genomics  ,  , and biomedical research . Challenges of proteomic studies stem from the complexity of the proteome and to its broad dynamic range. For example, the human genome contains around 20, protein coding genes. Their translation, combined with splicing or proteolysis, yields an estimated 50,—, proteins, and over 10 million different protein forms can be derived by somatic DNA rearrangements and post-translational modifications .
The abundance of protein species in human plasma spans more than 10 orders of magnitude . Unlike oligonucleotides, proteins cannot be amplified, and therefore the objectives of proteomics are achieved by sensitive and scalable technologies identifying and quantifying proteins . The overall mass spectrometry—based proteomic workflow is summarized in Figure 1.
Mass Spectrometry in the Biological Sciences: A Tutorial by Michael L. Gross - ywarpreses.tk
The workflow requires a tight integration of biological and experimental red and computational and statistical yellow analysis steps. Quantitative proteomic investigations are conducted in the context of biological variation  , technical variation due to sample processing and spectral acquisition, and ambiguities of spectral interpretation. Statistical experimental design  ,  accounts for these sources of variation.
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The first goal of experimental design is to avoid biases  ,  i. The second goal is to ensure efficiency i. The steps of the statistical experimental design are summarized in Figure 2. Statistical experimental design consists of a defining the populations of interest, b randomly selecting biological replicates from the population and optionally matching confounding factors, c randomly allocating biological samples to spectral acquisition and optionally grouping the samples in balanced blocks for joint profiling, and d optionally acquiring technical replicate measurements on the biological samples.
Replication, randomization, and blocking are necessary to avoid biases and maximize the efficiency of the experiment. Mass spectrometry is currently the only technology for protein identification and quantification that is both high-accuracy and high-throughput  — . Next, liquid chromatography LC separates the peptides, and the separated peptides are ionized and further separated by the mass spectrometer according to their mass-to-charge ratio in a mass spectrum MS.
The mass spectra obtained from the same sample at different elution times form an LC-MS run, and intensities of MS peaks, are related to peptide abundance. Label-free quantification requires minimal sample manipulation, and acquires spectra from each sample in a separate mass spectrometry run. Label-based quantification varies in the timing and type of the labeling steps, but always simultaneously profiles two or more biological samples within a run. They are most sensitive, but require an a priori knowledge of the proteins of interest, and of the technological characteristics of their peptides.
Label-free targeted experiments compare intensities of transitions between runs, and label-based experiments within a run. It requires minimal manipulation of the sample, and minimal prior information regarding its composition. However, the workflow has a number of deficiencies. Enzymatic digestion increases the complexity of the mixture. For example, a proteome comprising 5, proteins is expected to yield over , tryptic peptides, and minor cleavage and fragmentations of abundant proteins can obscure major events of low-abundant proteins, complicating the interpretation .
Technical variation can further undermine the identification and the quantification steps. A variety of extensions to this basic workflow have therefore been proposed. Samples with different labels are combined and analyzed by a mass spectrometer within a single LC-MS run. Labeling enables within-run comparisons of protein abundance, and improves the precision of quantification.
Experimental design can further gain efficiency through optimal allocation of samples to the labels, e. However, labeling requires extra sample manipulation and increases the complexity of the sample. The complexity of a biological mixture can be overcome by fractionation  ; however, this severely undermines the throughput. A valuable alternative is selected reaction monitoring SRM also referred to as multiple reaction monitoring, MRM , a targeted workflow where the mass spectrometer isolates a set of pre-defined peptides and their fragments during mass analysis  — .
The resulting peptide-fragment pairs called transitions are used for quantification. Since the isolation is highly specific, SRM enables the most sensitive mass spectrometry—based quantification currently available. As shown in Figure 3 , SRM can be conducted in conjunction with both label-free and label-based workflows.
The drawback of targeted workflows is that they only quantify a priori known proteins, require optimized experimental protocols, and limit the number of measurements per run to a few hundreds. Further technological developments  and optimal experimental designs  will help alleviate these drawbacks.
The computational and statistical analyses of the acquired spectra are illustrated in Figure 4. This has received much attention from both algorithmic and statistical viewpoints  — A predominant approach is the database search, which compares each observed spectrum to the theoretical spectra predicted from a genomic sequence database or to the previously identified experimental spectra in a library  , and reports the best-scoring peptide-spectrum match PSM. Emerging alternatives are de novo identifications and hybrid searches  , .
Analysis of the acquired spectra includes a, b signal processing, c, d significance analysis, and e—h downstream analysis. Methods in a—d must reflect the technological properties of the workflows. Methods in e—h are technology-independent and are similar to the analysis of gene expression microarrays, but their use is affected by uncertainty in protein identities and the incomplete sampling of the proteome.
Statistical characterization of the identifications is necessary, and is now required by most journals . This problem is frequently formalized as controlling the false discovery rate FDR in the list of reported PSMs  , . Representative methods for controlling FDR are two-group models, which view the reported PSMs as a mixture of correct and incorrect identifications  , and methods utilizing decoy databases .
Inference must enable parsimonious results, while maintaining the sensitivity and characterizing the confidence in the identifications. The problem of protein inference is not entirely solved. For example, arguments exist in favor  and against  reporting single-peptide protein identifications, and in favor  and against  the exclusive use of protease-specific peptides.
Label-based workflows with MS quantification e.
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All these tasks can be made difficult by irregular, overlapped, and missing peaks, chromatographic variations between runs, and incomplete and incorrect identifications. As a result, only a subset of the identified proteins is typically quantified .
A variety of signal processing software tools are reviewed in  , and the representative ones are OpenMS  for label-based quantification and MaxQuant  for quantification with SILAC. Targeted SRM experiments sidestep the need for identifying and aligning peaks, and signal processing focuses on peak detection, quantification, and annotation.
However, difficulties can arise with overlapped or suppressed signals or incorrectly calibrated transitions, and computational methods can help filter out poor quality transitions  , . Frequently, sample handling induces differences in the quantitative signals between runs, and global between-run normalization is necessary to distinguish true biological changes from these artifacts. Two common approaches to global normalization are sample-based and control-based. Sample-based normalization, e.
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Control-based normalization in preferred in experiments with few measurements or many biological changes. Typical statistical goals of quantitative proteomics are protein quantification , i. To achieve this, it is often necessary to summarize the quantitative information across all the features that pertain to a protein. The approach involves minimal signal processing; however, it requires specialized statistical modeling, is limited to finding large changes among abundant proteins, and is most successful with mixtures of low complexity, e.
Alternative approaches are based on summarizing signals from quantified spectral peaks. With other technologies such as gene expression microarrays, similar summarization is performed by some form of averaging, e. Unfortunately, averaging fails to produce accurate results in mass spectrometry—based proteomics. Length, charge, and other chemical properties of peptides greatly affect the quality of the signals, and averaging obscures these difference in information content. A more successful summarization requires probabilistic modeling, which represents all features of a protein and characterizes their variation.
A diverse range of such models has been proposed, and there is no single generally accepted procedure. The models differ in using raw or log-transformed intensities, comparing groups in terms of ratios or differences, and using general-purpose  or specialized  classes of statistical models.
Mass Spectrometry in the Biological Sciences: A Tutorial
Important aspects are accurate representation of the experimental design and of within-run groupings of peaks in label-based workflows, treatment of missing data e. In some cases, e. Comparisons at the feature level are then more appropriate; however, they should be adjusted for the overall changes in protein abundance . Given the diversity of experimental designs and analysis steps, all these tasks can rarely be performed in a fully automated fashion, and consultations with statisticians are highly recommended.
The high-throughput nature of proteomic data is similar to that of gene expression microarrays, and many downstream analysis methods can also be applied in proteomics .
In particular, all analyses benefit from data visualization . Unsupervised class discovery helps find functionally related proteins, or biological samples homogeneous with respect to the quantitative protein profiles. Supervised class prediction , e.
Enrichment analysis tests whether pre-specified sets of proteins, e. This is referred to as pathway analysis when the protein set forms a pathway. The analysis investigates hypotheses that are more directly relevant to the biological function, and can help detect small but consistent changes in abundance within the set. Many enrichment analysis methods exist and are systematically reviewed in  ,  , and representative examples are the hypergeometric equivalently, Fisher's exact test and Gene Set Enrichment Analysis GSEA .
A particular challenge in proteomics is to map the protein identitifiers to gene-centric knowledge bases.
A frequently asked question is the correlation between the expression of protein-coding genes and the abundances of the corresponding proteins  — . Many studies reported that in bacteria and uni-cellular eukaryotes, proteins and mRNA exhibit moderate correlation in a steady state Pearson correlation of the order of 0. An even lower correlation has been historically reported for multi-cellular eukaryotes; however, technological improvements now also point to a steady state correlation in human samples of the order of 0.
The moderate correlation of transcript and protein abundance indicates a major role of post-translational regulation in the activity of the cell. Therefore, the best functional insight can be obtained by combining measurements across technologies, and searching for broader groups of genes, proteins, and metabolites forming regulatory relationships  , . Such integrative studies are increasingly appearing  , . They remain challenging, however, due to the complexity of the underlying processes, incomplete sampling of the proteome, uncertainty in protein identities and difficulties of resolving multiple proteomic, genomic, and technological identifiers across platforms.
New specialized methods and algorithms are needed to address these challenges. Despite the challenges, mass spectrometry—based proteomics continues to bring high promise for basic science and clinical research .