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MaxQuant is a platform for mass spectrometry-based proteomics data analysis. It includes a peptide database search engine, called Andromeda, and expanding capability to handle data from most quantitative proteomics experiments. MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org .

Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

Key Points Advances in mass spectrometry-based proteomics have enabled the global identification and characterization of thousands of acetylation sites, ushering in the age of acetylomics. Acetylation intersects with cellular metabolism at multiple levels: metabolic intermediates regulate acetyltransferases and deacetylases, acetylation can affect metabolic pathways, and metabolites may directly acetylate proteins through non-enzymatic mechanisms. Acetylation regulates protein function by affecting protein interactions with nucleic acids and with other proteins, the catalytic activity of proteins, and protein localization. Emerging data indicate that other types of acylations, such as succinylation and glutarylation, are also linked to metabolic activity and are regulated by sirtuin-class deacylases. Recent technical advances are expanding our understanding of how lysine acetylation, as well as other metabolite-sensitive acylations, regulates various cellular processes. Emerging findings point to new functions for different acylations and deacylating enzymes, and clarify the intricate link between lysine acetylation and cellular metabolism. Lysine acetylation is a conserved protein post-translational modification that links acetyl-coenzyme A metabolism and cellular signalling. Recent advances in the identification and quantification of lysine acetylation by mass spectrometry have increased our understanding of lysine acetylation, implicating it in many biological processes through the regulation of protein interactions, activity and localization. In addition, proteins are frequently modified by other types of acylations, such as formylation, butyrylation, propionylation, succinylation, malonylation, myristoylation, glutarylation and crotonylation. The intricate link between lysine acylation and cellular metabolism has been clarified by the occurrence of several such metabolite-sensitive acylations and their selective removal by sirtuin deacylases. These emerging findings point to new functions for different lysine acylations and deacylating enzymes and also highlight the mechanisms by which acetylation regulates various cellular processes.

A data-independent acquisition (DIA) mass spectrometry approach, ultradefinition (UD)MS E , offers high reproducibility and improved proteome coverage over alternative DIA and data-dependent acquisition workflows. We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MS E . This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MS E and high-definition (HD) MS E data-independent acquisition techniques. UDMS E provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MS E , and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMS E data.

A triple-stage mass spectrometry (MS3)-based method is used to remove ratio interference, resulting in accurate, large-scale, multiplexed quantitative proteomics measurements using isobaric labeling. Also in this issue, Wenger et al . provide a different solution to the same problem. Quantitative mass spectrometry–based proteomics is highly versatile but not easily multiplexed. Isobaric labeling strategies allow mass spectrometry–based multiplexed proteome quantification; however, ratio distortion owing to protein quantification interference is a common effect. We present a two-proteome model (mixture of human and yeast proteins) in a sixplex isobaric labeling system to fully document the interference effect, and we report that applying triple-stage mass spectrometry (MS3) almost completely eliminates interference.

Key Points Our understanding of cellular function depends on exquisite knowledge of all of the molecular components acting in a system. Mass spectrometry (MS)-based proteomics has matured immensely in the last decade, allowing quantitative system-wide analysis of the proteome, including post-translational modifications (PTMs), protein–protein interactions and cellular localization. Quantification of the entire set of proteins expressed in a complex biological system (for example, mammalian cells) is now possible with a high sensitivity and in a reasonable amount of time. With the availability of genomic information, the massive capacity for peptide identification by MS is being used to annotate gene sequences and to find new protein-coding genes and splicing variants. In combination with new approaches to isolate specific PTMs, MS-based studies are revealing a much higher order of proteome complexity in which most proteins are modified by several PTMs that crosstalk in intricate mechanisms to regulate protein function. Protein affinity strategies allow purification of candidate proteins and their interacting partners, which are subsequently identified by MS. These studies describe, with a high degree of detail, dynamic and context-specific protein–protein interaction networks and protein complexes. The improvements in sensitivity, robustness and high-throughput of MS-based proteomics now permits applications in the clinical field, including the possibility of discovering disease-related biomarkers and screening molecular targets of candidate drugs. Despite their complexity and dynamics, proteomes are starting to be comprehensively characterized; this has been made possible particularly by various technical advances in mass spectrometry. This Review highlights how proteomic studies are contributing to our understanding of various cellular processes in health and disease. Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.

During normal ageing a low rate of division of pre-existing cardiomyocytes, rather than progenitor cells, is responsible for cardiomyocyte genesis; this process is increased fourfold during myocardial infarction. Heart-cell replacement in the adult There is controversy in the cardiovascular field regarding the extent of existing cardiomyocyte turnover versus progenitor cell contributions during mammalian cardiac homeostasis throughout adult life. This study takes a novel approach to settling the debate by using a combination of prospective labelling with the nitrogen-15 isotope and genetic pulse–chase to measure DNA synthesis in the adult mammalian heart. The authors conclude that during normal ageing, preexisting cardiomyocytes are the dominant source of cardiomyocyte replacement in normal mammalian myocardial homeostasis, a process that is increased fivefold during recovery from myocardial infarction. Although recent studies have revealed that heart cells are generated in adult mammals, the frequency of generation and the source of new heart cells are not yet known. Some studies suggest a high rate of stem cell activity with differentiation of progenitors to cardiomyocytes 1 . Other studies suggest that new cardiomyocytes are born at a very low rate 2 , 3 , 4 , and that they may be derived from the division of pre-existing cardiomyocytes. Here we show, by combining two different pulse–chase approaches—genetic fate-mapping with stable isotope labelling, and multi-isotope imaging mass spectrometry—that the genesis of cardiomyocytes occurs at a low rate by the division of pre-existing cardiomyocytes during normal ageing, a process that increases adjacent to areas of myocardial injury. We found that cell cycle activity during normal ageing and after injury led to polyploidy and multinucleation, but also to new diploid, mononucleate cardiomyocytes. These data reveal pre-existing cardiomyocytes as the dominant source of cardiomyocyte replacement in normal mammalian myocardial homeostasis as well as after myocardial injury.

The role of targeted mass spectrometry technology in the field of clinical proteomics is discussed in this Perspective. Targeted mass spectrometry (MS) is becoming widely used in academia and in pharmaceutical and biotechnology industries for sensitive and quantitative detection of proteins, peptides and post-translational modifications. Here we describe the increasing importance of targeted MS technologies in clinical proteomics and the potential key roles these techniques will have in bridging biomedical discovery and clinical implementation.

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ∼48 h: 1 h for system setup, 8–10 h for sample preparation, 34 h for UPLC-MS analysis and 2–3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

In this Review, the authors provide a guide through to the different steps involved in selected reaction monitoring as well as discuss its applications. Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that is emerging in the field of proteomics as a complement to untargeted shotgun methods. SRM is particularly useful when predetermined sets of proteins, such as those constituting cellular networks or sets of candidate biomarkers, need to be measured across multiple samples in a consistent, reproducible and quantitatively precise manner. Here we describe how SRM is applied in proteomics, review recent advances, present selected applications and provide a perspective on the future of this powerful technology.