Abstract: Mass spectrometry is unmatched in its versatility for studying practically any aspect of the proteome. Because the foundations of mass spectrometry-based proteomics are complex and span multiple scientific fields, proteomics can be perceived as having a high barrier to entry. This tutorial is intended to be an accessible illustrated guide to the technical details of a relatively simple quantitative proteomic experiment. An attempt is made to explain the relevant concepts to those with limited knowledge of mass spectrometry and a basic understanding of proteins. An experimental overview is provided, from the beginning of sample preparation to the analysis of protein group quantities, with explanations of how the data are acquired, processed, and analyzed. A selection of advanced topics is briefly surveyed and works for further reading are cited. To conclude, a brief discussion of the future of proteomics is given, considering next-generation protein sequencing technologies that may complement mass spectrometry to create a fruitful future for proteomics.

Abstract: Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Abstract: Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enables a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry, cofractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interface, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Abstract: Current clinical tools for breast cancer (BC) diagnosis are insufficient but liquid biopsy of different bodily fluids has recently emerged as a minimally invasive strategy that provides a real-time snapshot of tumour biomarkers for early diagnosis, active surveillance of progression, and post-treatment recurrence. Extracellular vesicles (EVs) are nano-sized membranous structures 50-1000 nm in diameter that are released by cells into biological fluids. EVs contain proteins, nucleic acids, and lipids which play pivotal roles in tumourigenesis and metastasis through cell-to-cell communication. Proteins and miRNAs from small EVs (sEV), which range in size from 50-150 nm, are being investigated as a potential source for novel BC biomarkers using mass spectrometry-based proteomics and next-generation sequencing. This review covers recent developments in sEV isolation and single sEV analysis technologies and summarises the sEV protein and miRNA biomarkers identified for BC diagnosis, prognosis, and chemoresistance. The limitations of current sEV biomarker research are discussed along with future perspective applications.

Abstract: Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Abstract: Abstract Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

Abstract: Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Abstract: A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.

Abstract: The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Abstract: Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.

Abstract: Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling. DecryptM profiling of 31 cancer drugs in 13 cell lines demonstrates the broad applicability of the approach. The resulting 1.8 million dose-response curves are provided as an interactive molecular resource in ProteomicsDB. Description Decrypting drug actions through proteomics Many cancer drugs aim to inhibit abnormally active proteins that drive the growth of tumors, but it is often not understood just how cancer cells react to these drugs. Zecha et al. devised a proteomic technology called DecryptM that can measure the time- and dose-dependent response of thousands of proteins and reveal changes in response to small-molecule or antibody-based drugs. Application of DecryptM to 31 drugs generated millions of measurements. These data helped to place protein modifications into new functional contexts, created fingerprints of drug response, and provided insights into how certain drugs kill cancerous blood cells. The DecryptM data are available as a community resource for use in future basic biology, drug discovery, and clinical research. —PNK Dose-dependent effects of drugs on post-translational modifications reveal insights into their actions. INTRODUCTION Most drugs act on proteins and engage cellular pathways regulated by protein posttranslational modifications (PTMs), such as phosphorylation, acetylation, or ubiquitinylation, to exert their therapeutic effects. Because polypharmacology is common, it is important to characterize drugs on a proteome-wide scale to understand all their mechanisms of action. RATIONALE There is a lack of dose- and time-dependent drug characterization at the level of proteins and PTMs, arguably the most important characteristics of drug action in any biological context. To address this gap, this study presents a quantitative proteomic approach called decryptM, which is able to assess drug target and pathway engagement as well as cellular mechanism of action by measuring thousands of PTM responses in a dose- and time-resolved fashion. RESULTS DecryptM profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides, all of which can be explored in ProteomicsDB (www.proteomicsdb.org/decryptM). The observed close coherence between drug-target affinity and drug-PTM modulation potency enabled placing functionally uncharacterized PTM sites into known pathways and thus decrypting them on the grounds of guilt by association. Examples include previously uncharacterized phosphorylation sites linking chemotherapeutic drugs to the DNA damage response, regulated phosphorylation sites indicating breakdown of oncogenic signaling in response to phosphatase and kinase inhibitors, or activation of the unfolded protein response upon proteasome inhibition. Similarly, decryptM profiles enabled the identification of previously unknown substrates of kinases as well as lysine acetyltransferases and deacetylases. Each drug appeared to leave a cell line–specific decryptM signature that may constitute a pharmacodynamic marker for target and pathway engagement, identify points of conversion of signaling axes, or distinguish closely related compounds. DecryptM analysis of therapeutic anti-HER2 antibodies revealed differences in their mechanisms of action. Whereas pertuzumab cuts off the HER3–mitogen-activated protein kinase (MAPK) and HER3-PI3K/AKT signaling axis in breast cancer cells, trastuzumab had no effect on any of the phosphoproteomes investigated. By contrast, the anti-CD20 antibody rituximab massively activates signaling in B cells. The collective decryptM and functional evidence supports a model in which rituximab binds to CD20, located in lipid rafts alongside the B cell receptor complex, and leads to strong activation of the MAPK and calcineurin–nuclear factor of activated T cells (NFAT) axis, tipping the signaling balance toward apoptotic cell death. CONCLUSION The examples presented in this study illustrate the potential of decryptM to characterize drugs’ mechanisms of action, generate drug-specific PTM signatures, study resistance mechanisms, and place drug-regulated PTM sites of unclear significance into a functional context. The workflow developed supports decryptM profiling at scale and should be extendable to any molecule that modulates cellular activity by affecting PTMs or protein expression. This may include G protein–coupled receptor (GPCR) ligands, cytokines, chemokines, cofactors, metabolites, biologics, peptides, and hormones among many other factors. In the future, decryptM profiles may also serve to monitor and potentially predict drug responses in vivo. Looking further, we envision that matching decryptM profiles of cancer drugs with PTM profiles of cancer patients will become important for evidence-based treatment recommendations by molecular tumor boards. Schematic representation of the decryptM approach. Following the dose-dependent treatment of cancer cells with molecularly targeted drugs, quantitative mass spectrometry can record the dose-response characteristics of thousands of posttranslationally modified peptides in parallel. Such drug profiles can be used to place previously uncharacterized modification sites into a functional context and elucidate the cellular mechanism of action of a given drug. EC50, half-maximal effective concentration; Ac, acetylation; GG, ubiquitinylation; P, phosphorylation.

Abstract: Recently, a novel technology was published, utilizing the strengths of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) and immunohistochemistry (IHC), achieving highly multiplexed, targeted imaging of biomolecules in tissue. This new technique, called MALDI-IHC, opened up workflows to target molecules of interest using MALDI-MSI that are usually targeted by standard IHC. In this paper, the utility of targeted MALDI-IHC and its complementarity with untargeted on-tissue bottom-up spatial proteomics is explored using breast cancer tissue. Furthermore, the MALDI-2 effect was investigated and demonstrated to improve MALDI-IHC. Formalin-fixed paraffin-embedded (FFPE) human breast cancer tissue sections were stained for multiplex MALDI-IHC with six photocleavable mass-tagged (PC-MT) antibodies constituting a breast cancer antibody panel (CD20, actin-αSM, HER2, CD68, vimentin, and panCK). K-means spatial clusters were created based on the MALDI-IHC images and cut out using laser-capture microdissection (LMD) for further untargeted LC-MS-based bottom-up proteomics analyses. Numerous peptides could be tentatively assigned to multiple proteins, of which three proteins were also part of the antibody panel (vimentin, keratins, and actin). Post-ionization with MALDI-2 showed an increased intensity of the PC-MTs and suggests options for the development of new mass-tags. Although the on-tissue digestion covered a wider range of proteins, the MALDI-IHC allowed for easy and straightforward identification of proteins that were not detected in untargeted approaches. The combination of the multiplexed MALDI-IHC with image-guided proteomics showed great potential to further investigate diseases by providing complementary information from the same tissue section and without the need for customized instrumentation.

Abstract: The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

Abstract: Single-cell methodologies and technologies have started a revolution in biology which until recently has primarily been limited to deep sequencing and imaging modalities. With the advent and subsequent torrid development of single-cell proteomics over the last 5 years, despite the fact that proteins cannot be amplified like transcripts, it has now become abundantly clear that it is a worthy complement to single-cell transcriptomics. In this review, we engage in an assessment of the current state of the art of single-cell proteomics including workflow, sample preparation techniques, instrumentation, and biological applications. We investigate the challenges associated with working with very small sample volumes and the acute need for robust statistical methods for data interpretation. We delve into what we believe is a promising future for biological research at single-cell resolution and highlight some of the exciting discoveries that already have been made using single-cell proteomics, including the identification of rare cell types, characterization of cellular heterogeneity, and investigation of signaling pathways and disease mechanisms. Finally, we acknowledge that there are a number of outstanding and pressing problems that the scientific community vested in advancing this technology needs to resolve. Of prime importance is the need to set standards so that this technology becomes widely accessible allowing novel discoveries to be easily verifiable. We conclude with a plea to solve these problems rapidly so that single-cell proteomics can be part of a robust, high-throughput, and scalable single-cell multi-omics platform that can be ubiquitously applied to elucidating deep biological insights into the diagnosis and treatment of all diseases that afflict us.

Abstract: Abstract Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

Abstract: Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.

Abstract: The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi‐dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label‐free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label‐free single‐cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Abstract: Proteomic studies for Alzheimer’s disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson’s disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD. Description Proteomics of brain, CSF, and plasma identifies markers that distinguish sporadic AD from autosomal dominant AD (ADAD) and TREM2 risk carriers. Editor’s summary Biomarkers for Alzheimer’s disease (AD) are important for diagnosis and treatment monitoring. Sung and colleagues performed a proteomic study using brain, cerebrospinal fluid (CSF), and plasma samples from a cohort of patients with sporadic AD or autosomal dominant AD (ADAD), TREM2 risk carriers, and healthy individuals. The authors identified proteins that could be used to classify patients depending on disease status. Proteins cross-validated in brain, CSF, and plasma were more likely to be also replicated in external datasets, indicating that across-tissue and fluid validation of disease markers could serve as an alternative when external validation is not possible. —Daniela Neuhofer

Abstract: Despite producing a panoply of potential cancer-specific targets, the proteogenomic characterization of human tumors has yet to demonstrate value for precision cancer medicine. Integrative multi-omics using a machine-learning network identified master kinases responsible for effecting phenotypic hallmarks of functional glioblastoma subtypes. In subtype-matched patient-derived models, we validated PKCδ and DNA-PK as master kinases of glycolytic/plurimetabolic and proliferative/progenitor subtypes, respectively, and qualified the kinases as potent and actionable glioblastoma subtype-specific therapeutic targets. Glioblastoma subtypes were associated with clinical and radiomics features, orthogonally validated by proteomics, phospho-proteomics, metabolomics, lipidomics and acetylomics analyses, and recapitulated in pediatric glioma, breast and lung squamous cell carcinoma, including subtype specificity of PKCδ and DNA-PK activity. We developed a probabilistic classification tool that performs optimally with RNA from frozen and paraffin-embedded tissues, which can be used to evaluate the association of therapeutic response with glioblastoma subtypes and to inform patient selection in prospective clinical trials.

Abstract: Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories.Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics.Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.