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Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Differential precipitation of proteins (DiffPOP) is a simple technique for fractionating complex protein mixtures. Using stepwise addition of acidified methanol, ten distinct subsets of proteins can be selectively precipitated by centrifugation and identified by mass spectrometry-based proteomics. We have previously shown that the ability of a protein to resist precipitation can be altered by drug binding, which enabled us to identify a novel drug-target interaction. Here, we show that the addition of DiffPOP to a standard LC-MS proteomics workflow results in a three-dimensional separation of peptides that increases protein coverage and peptide identifications. Importantly, DiffPOP reveals solubility differences between proteoforms, potentially providing valuable insights that are typically lost in bottom-up proteomics.

The nuclear envelope (NE) is a subdomain of the ER with prominent roles in nuclear organization, which are largely mediated by its distinctive protein composition. We developed methods to reveal low-abundance transmembrane (TM) proteins concentrated at the NE relative to the peripheral ER. Using label-free proteomics that compared isolated NEs with cytoplasmic membranes, we first identified proteins with apparent NE enrichment. In subsequent authentication, ectopically expressed candidates were analyzed by immunofluorescence microscopy to quantify their targeting to the NE in cultured cells. Ten proteins from a validation set were found to associate preferentially with the NE, including oxidoreductases, enzymes for lipid biosynthesis, and regulators of cell growth and survival. We determined that one of the validated candidates, the palmitoyltransferase Zdhhc6, modifies the NE oxidoreductase Tmx4 and thereby modulates its NE levels. This provides a functional rationale for the NE concentration of Zdhhc6. Overall, our methodology has revealed a group of previously unrecognized proteins concentrated at the NE and additional candidates. Future analysis of these can potentially unveil new mechanistic pathways associated with the NE., (© 2023 Cheng et al.)

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines ., (© 2023. Springer Nature America, Inc.)

The fold and conformation of proteins are key to successful cellular function, but all techniques for protein structure determination are performed in an artificial environment with highly purified proteins. While protein conformations have been solved to atomic resolution and modern protein structure prediction tools rapidly generate near accurate models of proteins, there is an unmet need to uncover the conformations of proteins in living cells. Here, we describe Covalent Protein Painting (CPP), a simple and fast method to infer structural information on protein conformation in cells with a quantitative protein footprinting technology. CPP monitors the conformational landscape of the 3D proteome in cells with high sensitivity and throughput. A key advantage of CPP is its' ability to quantitatively compare the 3D proteomes between different experimental conditions and to discover significant changes in the protein conformations. We detail how to perform a successful CPP experiment, the factors to consider before performing the experiment, and how to interpret the results., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Emerin and lamin B receptor (LBR) are abundant transmembrane proteins of the nuclear envelope that are concentrated at the inner nuclear membrane (INM). Although both proteins interact with chromatin and nuclear lamins, they have distinctive biochemical and functional properties. Here, we have deployed proximity labeling using the engineered biotin ligase TurboID (TbID) and quantitative proteomics to compare the neighborhoods of emerin and LBR in cultured mouse embryonic fibroblasts. Our analysis revealed 232 high confidence proximity partners that interact selectively with emerin and/or LBR, 49 of which are shared by both. These included previously characterized NE-concentrated proteins, as well as a host of additional proteins not previously linked to emerin or LBR functions. Many of these are TM proteins of the ER, including two E3 ubiquitin ligases. Supporting these results, we found that 11/12 representative proximity relationships identified by TbID also were detected at the NE with the proximity ligation assay. Overall, this work presents methodology that may be used for large-scale mapping of the landscape of the INM and reveals a group of new proteins with potential functional connections to emerin and LBR.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org ., (© 2022. Springer Nature Limited.)

Aurora kinase A (AURKA) is a conserved kinase that plays crucial roles in numerous cellular processes. Although AURKA overexpression is frequent in human cancers, its pleiotropic functions and multifaceted regulation present challenges in its therapeutic targeting. Key to overcoming these challenges is to identify and characterize the full range of AURKA interactors, which are often weak and transient. Previous proteomic studies were limited in monitoring dynamic and non-mitotic AURKA interactions. Here, we generate the proximity interactome of AURKA in asynchronous cells, which consists of 440 proteins involving multiple biological processes and cellular compartments. Importantly, AURKA has extensive proximate and physical interactions to centriolar satellites, key regulators of the primary cilium. Loss-of-function experiments identify satellites as negative regulators of AURKA activity, abundance, and localization in quiescent cells. Notably, loss of satellites activates AURKA at the basal body, decreases centrosomal IFT88 levels, and causes ciliogenesis defects. Collectively, our results provide a resource for dissecting spatiotemporal regulation of AURKA and uncover its proteostatic regulation by satellites as a new mechanism for its ciliary functions., (© 2021 The Authors. Published under the terms of the CC BY NC ND 4.0 license.)

A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information on the common TD/MD MS methods and practices in the field. A panel of three mAbs was selected and centrally provided to 20 laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). Various MS instrument platforms and ion dissociation techniques were employed. The present study confirms that TD/MD MS tools are available in laboratories worldwide and provide complementary information to the BU approach that can be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. A primary limitation revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS.

Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.

Proteomics characterization of KAIMRC1 cell line, a naturally immortalized breast cancer cells, is described in comparison to MCF-7 and MDA-MB-231 breast cancer cells. Quantitative proteomics analysis using the tandem mass tag (TMT)-labeled technique in conjunction with the phosphopeptide enrichment method was used to perform comparative profiling of proteins and phosphoproteins in the three cell lines. In total, 673 proteins and 33 Phosphoproteins were differentially expressed among these cell lines. These proteins are involved in several key cellular pathways that include DNA replication and repair, splicing machinery, amino acid metabolism, cellular energy, and estrogen signaling pathway. Many of the differentially expressed proteins are associated with different types of tumors including breast cancer. For validation, 4 highly significant expressed proteins including S-methyl-5'-thioadenosine phosphorylase (MTAP), BTB/POZ domain-containing protein (KCTD12), Poly (ADP-ribose) polymerase 1 (PARP 1), and Prelamin-A/C were subjected to western blotting, and the results were consistent with proteomics analysis. Unlike MCF-7 and MDA-MB-231, KAIMRC1 showed different phospho- and non-phosphoproteomic phenotypes which make it a potential model to study breast cancer.

Technological advances in mass spectrometry have enabled the extensive identification, characterization, and quantification of proteins in any biological system. In disease processes proteins are often altered in response to external stimuli; therefore, proteomics, the large-scale study of proteins and their functions, represents an invaluable tool for understanding the molecular basis of disease. This review highlights the use of mass spectrometry-based proteomics to study the pathogenesis, etiology, and pathology of several neglected tropical diseases (NTDs), a diverse group of disabling diseases primarily associated with poverty in tropical and subtropical regions of the world. While numerous NTDs have been the subject of proteomic studies, this review focuses on Buruli ulcer, dengue, leishmaniasis, and snakebite envenoming. The proteomic studies highlighted provide substantial information on the pathogenic mechanisms driving these diseases; they also identify molecular targets for drug discovery and development and uncover promising biomarkers that can assist in early diagnosis.

In the present study, we describe the proteome of porcine cauda epididymis fluid and spermatozoa by means of Multidimensional Protein Identification Technology (MudPIT). Ten sexually mature healthy boars were surgically castrated and epididymides were dissected to obtain the cauda epididymal content. Polled protein extracts of cauda epididymal fluid (CEF) and spermatozoa (CESperm) were loaded in an Agilent 1100 quaternary HPLC and peptides eluted from the microcapillary column were electro-sprayed directly into a LTQ Orbitrap XL mass spectrometer. Using bioinformatics, identified proteins were classified by their molecular functions, involvement in biological processes and participation in relevant metabolic pathways associated with spermatozoa physiology, fertility potential and protection. A total of 645 proteins were identified in the CEF, with epididymal-specific lipocalin-5, beta-hexosaminidase subunit beta precursor and phosphatidylethanolamine-binding protein 4 being the most abundant proteins found. A total of 2886 proteins were identified in the CESperm proteome with 81 proteins being considered more abundant (spectral counts > 100). CEF and CESperm data were compared and 345 proteins were present in both proteomes. Phosphatidylethanolamine-binding protein 4 precursor was the only protein found most abundant in both CEF and CESperm proteomes. Based on Gene Ontology analysis, we identified CEF and CESperm proteins associated with sperm protection against ROS and immune mediated response, glycosaminoglycan degradation, ubiquitin-proteasome system, metabolic process and maturation, modulation of acrosome reaction and ZP binding and oocyte penetration. These results provide a better comprehension about the molecular process and biological pathways involved in sperm epididymis maturation and establishment of the cauda epididymis sperm reservoir.

Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.

The double membrane nuclear envelope (NE), which is contiguous with the ER, contains nuclear pore complexes (NPCs) - the channels for nucleocytoplasmic transport, and the nuclear lamina (NL) - a scaffold for NE and chromatin organization. Since numerous human diseases linked to NE proteins occur in mesenchyme-derived cells, we used proteomics to characterize NE and other subcellular fractions isolated from mesenchymal stem cells and from adipocytes and myocytes. Based on spectral abundance, we calculated enrichment scores for proteins in the NE fractions. We demonstrated by quantitative immunofluorescence microscopy that five little-characterized proteins with high enrichment scores are substantially concentrated at the NE, with Itprip exposed at the outer nuclear membrane, Smpd4 enriched at the NPC, and Mfsd10, Tmx4, and Arl6ip6 likely residing in the inner nuclear membrane. These proteins provide new focal points for studying the functions of the NE. Moreover, our datasets provide a resource for evaluating additional potential NE proteins.

Progress in proteomics research has led to a demand for powerful analytical tools with high separation efficiency and sensitivity for confident identification and quantification of proteins, posttranslational modifications, and protein complexes expressed in cells and tissues. This demand has significantly increased interest in capillary electrophoresis-mass spectrometry (CE-MS) in the past few years. This review provides highlights of recent advances in CE-MS for proteomics research, including a short introduction to top-down mass spectrometry and native mass spectrometry (native MS), as well as a detailed overview of CE methods. Both the potential and limitations of these methods for the analysis of proteins and peptides in synthetic and biological samples and the challenges of CE methods are discussed, along with perspectives about the future direction of CE-MS. @ 2019 Wiley Periodicals, Inc. Mass Spec Rev 00:1-16, 2019., (© 2019 Wiley Periodicals, Inc.)

Cryptococcus gattii is the causative agent of cryptococcosis infection that can lead to pneumonia and meningitis in immunocompetent individuals. The molecular basis of the pathogenic process and impact on the host biochemistry are poorly understood and remain largely unknown. In this context, a comparative proteomic analysis was performed to investigate the response of the host during an infection caused by C. gattii . Lungs of experimentally infected rats were analyzed by shotgun proteomics to identify differentially expressed proteins induced by C. gattii clinical strain. The proteomic results were characterized using bioinformatic tools, and subsequently, the molecular findings were validated in cell culture and lungs of infected animals. A dramatic change was observed in protein expression triggered by C. gattii infection, especially related to energy metabolism. The main pathways affected include aerobic glycolysis cycle, TCA cycle, and pyrimidine and purine metabolism. Analyses in human lung fibroblast cells confirmed the altered metabolic status found in infected lungs. Thus, it is clear that C. gattii infection triggers important changes in energy metabolism leading to the activation of glycolysis and lactate accumulation in lung cells, culminating in a cancerlike metabolic status known as the Warburg effect. The results presented here provide important insights to better understand C. gattii molecular pathogenesis.

Cutaneous leishmaniasisis is the most common clinical form of leishmaniasis and one of the most relevant neglected diseases. It is known that the progress of the disease is species specific and the host's immune response plays an important role in its outcome. However, the pathways that lead to parasite clearance or survival remain unknown. In this work, skin tissue from mice experimentally infected with L. amazonensis , one of the causative agents of cutaneous leishmaniasis in the Amazon region, L. major , another causative agent of cutaneous leishmaniasis in Africa, the Middle East, China, and India, or lipopolysaccharides from Escherichia coli as an inflammation model were investigated using label-free proteomics to unveil Leishmania -specific protein alterations. Proteomics is a powerful tool to investigate host-pathogen relationships to address biological questions. In this work, proteins from mice skin biopsies were identified and quantified using nano-LC coupled with tandem mass spectrometry analyses. Integrated Proteomics Pipeline was used for peptide/protein identification and quantification. Western blot was used for validation of protein quantification by mass spectrometry, and protein pathways were predicted using Ingenuity Pathway Analysis. In this proteomics study, several proteins were pointed out as hypothetical targets to guide future studies on Leishmania -specific modulation of proteins in the host. We identified hundreds of exclusively modulated proteins after Leishmania spp. infection and 17 proteins that were differentially modulated in the host after L. amazonensis or L. major infection.

The characterization of complex biological systems based on high-throughput protein quantification through mass spectrometry commonly involves differential expression analysis between replicate samples originating from different experimental conditions. Here we present Proteomics INTegrator (PINT), a new user-friendly Web-based platform-independent system to store, visualize, and query proteomics experiment results. PINT provides an extremely flexible query interface that allows advanced Boolean algebra-based data filtering of many different proteomics features such as confidence values, abundance levels or ratios, data set overlaps, sample characteristics, as well as UniProtKB annotations, which are transparently incorporated into the system. In addition, PINT allows developers to incorporate data visualization and analysis tools, such as PSEA-Quant and Reactome pathway analysis, for data set enrichment analysis. PINT serves as a centralized hub for large-scale proteomics data and as a platform for data analysis, facilitating the interpretation of proteomics results and expediting biologically relevant conclusions.

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M. tuberculosis dataset obtained from two biological conditions; we used three dimethyl labels, one serving as an internal control for labeling a mixture of samples from both biological conditions. This internal control certified the proper functioning of our software. Availability: http://patternlabforproteomics.org, freely available for academic use., (Copyright © 2019. Published by Elsevier B.V.)

Leishmania is an obligate intracellular parasite known to modulate the host cell to survive and proliferate. However, the complexity of host-parasite interactions remains unclear. Also, the outcome of the disease has been recognized to be species-specific and dependent on the host's immune responses. Proteomics has emerged as a powerful tool to investigate the host-pathogen interface, allowing us to deepen our knowledge about infectious diseases. Quantification of the relative amount of proteins in a sample can be achieved using label-free proteomics, and for the first time, we have used it to quantify Leishmania-specific protein alterations in macrophages. Protein extracts were obtained and digested, and peptides were identified and quantified using nano-LC coupled with tandem mass spectrometry analyses. Protein expression was validated by Western blot analysis. Integrated Proteomics Pipeline was used for peptide/protein identification and for quantification and data processing. Ingenuity Pathway Analysis was used for network analysis. In this work, we investigated how this intracellular parasite modulates protein expression on a host macrophage by comparing three different Leishmania species- L. amazonensis, one of the causative agents of cutaneous disease in the Amazon region; L. major, another causative agent of cutaneous leishmaniasis in Africa, the Middle East, China, and India; L. infantum, the causative agent of visceral leishmaniasis affecting humans and dogs in Latin America-and lipopolysaccharide stimulated macrophages as an in vitro inflammation model. Our results revealed that Leishmania infection downregulates apoptosis pathways while upregulating the activation of phagocytes/leukocytes and lipid accumulation.

The characteristics of parasitic infections are often tied to host behavior. Although most studies have investigated definitive hosts, intermediate hosts can also play a role in shaping the distribution and accumulation of parasites. This is particularly relevant in larval stages, where intermediate host's behavior could potentially interfere in the molecules secreted by the parasite into the next host during infection. To investigate this hypothesis, we used a proteomic approach to analyze excretion/secretion products (ESP) from Fasciola hepatica newly excysted juveniles (NEJ) derived from two intermediate host species, Lymnaea viatrix and Pseudosuccinea columella. The two analyzed proteomes showed differences in identity, abundance, and functional classification of the proteins. This observation could be due to differences in the biological cycle of the parasite in the host, environmental aspects, and/or host-dependent factors. Categories such as protein modification machinery, protease inhibitors, signal transduction, and cysteine-rich proteins showed different abundance between samples. More specifically, differences in abundance of individual proteins such as peptidyl-prolyl cis-trans isomerase, thioredoxin, cathepsin B, cathepsin L, and Kunitz-type inhibitors were identified. Based on the differences identified between NEJ ESP samples, we can conclude that the intermediate host is a factor influencing the proteomic profile of ESP in F. hepatica., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Mass spectrometry is one of the key technologies of proteomics, and over the last decade important technical advances in mass spectrometry have driven an increased capability for proteomic discovery. In addition, new methods to capture important biological information have been developed to take advantage of improving proteomic tools., Competing Interests: No competing interests were disclosed.No competing interests were disclosed.No competing interests were disclosed.

Mammalian cells are organized into different compartments that separate and facilitate physiological processes by providing specialized local environments and allowing different, otherwise incompatible biological processes to be carried out simultaneously. Proteins are targeted to these subcellular locations where they fulfill specialized, compartment-specific functions. Spatial proteomics aims to localize and quantify proteins within subcellular structures., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

We designed a metaproteomic analysis method (ComPIL) to accommodate the ever-increasing number of sequences against which experimental shotgun proteomics spectra could be accurately and rapidly queried. Our objective was to create these large databases for the analysis of complex metasamples with unknown composition, including those derived from human, animal, and environmental microbiomes. The amount of high-throughput sequencing data has substantially increased since our original database was assembled in 2014. Here, we present a rebuild of the ComPIL libraries comprised of updated publicly disseminated sequence data as well as a modified version of the search engine ProLuCID-ComPIL optimized for querying experimental spectra. ComPIL 2.0 consists of 113 million protein records and roughly 4.8 billion unique tryptic peptide sequences and is 2.3 times the size of our original version. We searched a data set collected on a healthy human gut microbiome proteomic sample and compared the results to demonstrate that ComPIL 2.0 showed a substantial increase in the number of unique identified peptides and proteins compared to the first ComPIL version. The high confidence of protein identification and accuracy demonstrated by the use of ComPIL 2.0 may encourage the method's application for large-scale proteomic annotation of complex protein systems.

Pancreatic β cell physiology changes substantially throughout life, yet the mechanisms that drive these changes are poorly understood. Here, we performed comprehensive in vivo quantitative proteomic profiling of pancreatic islets from juvenile and 1-year-old mice. The analysis revealed striking differences in abundance of enzymes controlling glucose metabolism. We show that these changes in protein abundance are associated with higher activities of glucose metabolic enzymes involved in coupling factor generation as well as increased activity of the coupling factor-dependent amplifying pathway of insulin secretion. Nutrient tracing and targeted metabolomics demonstrated accelerated accumulation of glucose-derived metabolites and coupling factors in islets from 1-year-old mice, indicating that age-related changes in glucose metabolism contribute to improved glucose-stimulated insulin secretion with age. Together, our study provides an in-depth characterization of age-related changes in the islet proteome and establishes metabolic rewiring as an important mechanism for age-associated changes in β cell function., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Phoneutria nigriventer is one of the largest existing true spiders and one of the few considered medically relevant. Its venom contains several neurotoxic peptides that act on different ion channels and chemical receptors of vertebrates and invertebrates. Some of these venom toxins have been shown as promising models for pharmaceutical or biotechnological use. However, the large diversity and the predominance of low molecular weight toxins in this venom have hampered the identification and deep investigation of the less abundant toxins and the proteins with high molecular weight. Here, we combined conventional and next-generation cDNA sequencing with Multidimensional Protein Identification Technology (MudPIT), to obtain an in-depth panorama of the composition of P. nigriventer spider venom. The results from these three approaches showed that cysteine-rich peptide toxins are the most abundant components in this venom and most of them contain the Inhibitor Cysteine Knot (ICK) structural motif. Ninety-eight sequences corresponding to cysteine-rich peptide toxins were identified by the three methodologies and many of them were considered as putative novel toxins, due to the low similarity to previously described toxins. Furthermore, using next-generation sequencing we identified families of several other classes of toxins, including CAPs (Cysteine Rich Secretory Protein-CRiSP, antigen 5 and Pathogenesis-Related 1-PR-1), serine proteinases, TCTPs (translationally controlled tumor proteins), proteinase inhibitors, metalloproteinases and hyaluronidases, which have been poorly described for this venom. This study provides an overview of the molecular diversity of P. nigriventer venom, revealing several novel components and providing a better basis to understand its toxicity and pharmacological activities., Competing Interests: The authors have declared that no competing interests exist.

Measuring proteome response to perturbations is critical for understanding the underlying mechanisms involved. Traditional quantitative proteomic methods are limited by the large numbers of proteins in the proteome and the mass spectrometer's dynamic range. A previous method uses the biorthogonal reagent azidohomoalanine (AHA), a methionine analog, for labeling, enrichment and detection of newly synthesized proteins (NSPs). Newly synthesized AHA proteins can be coupled to biotin via CuAAC-mediated click chemistry and enriched using avidin-based affinity purification. The combination of AHA-mediated NSP labeling with metabolic stable isotope labeling allows quantitation of low-abundant, newly secreted proteins by mass spectrometry (MS). However, the resulting multiplicity of labeling complicates NSP analysis. We developed a new NSP quantification strategy, called HILAQ (heavy isotope-labeled azidohomoalanine quantification), that uses a heavy isotope-labeled AHA molecule to enable NSP labeling, enrichment, identification and quantification. In addition, the AHA-peptide enrichment used in HILAQ improves both the identification and quantification of NSPs over AHA-protein enrichment. Here, we provide a description of the HILAQ method that includes procedures for (i) pulse-labeling and harvesting NSPs; (ii) addition of biotin by click reaction; (iii) protein precipitation; (iv) protein digestion; (v) enrichment of AHA-biotin peptides by NeutrAvidin beads and four-step elution; (vi) MS analysis; and (vii) data analysis for the identification and quantification of NSPs by ProLuCID and pQuant. We demonstrate our HILAQ approach by identifying NSPs from cell cultures, but we anticipate that it can be adapted for applications in animal models. The whole protocol takes ~6 d to complete.

Introduction: Measuring the immediate changes in cells that arise from changing environmental conditions is crucial to understanding the underlying mechanisms involved. These changes can be measured with metabolic stable isotope fully labeled proteomes, but requires looking for changes in the midst of a large background. In addition, labeling efficiency can be an issue in primary and fully differentiated cells. Area covered: Azidohomoalanine (AHA), an analog of methionine, can be accepted by cellular translational machinery and incorporated into newly synthesized proteins (NSPs). AHA-NSPs can be coupled to biotin via CuAAC-mediated click-chemistry and enriched using avidin-based affinity purification. Thus, AHA-containing proteins or peptides can be enriched and efficiently separated from the whole proteome. In this review, we describe the development of mass spectrometry (MS) based AHA strategies and discuss their potential to measure proteins involved in immune response, secretome, gut microbiome, and proteostasis as well as their potential for clinical uses. Expert commentary: AHA strategies have been used to identify synthesis activity and to compare two biological conditions in various biological model organisms. In combination with instrument development, improved sample preparation and fractionation strategies, MS-based AHA strategies have the potential for broad application, and the methods should translate into clinical use.

The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity. In bottom-up proteomics peptides are mapped back to proteins and proteoforms to describe a proteome; however, accurate quantitation of proteoforms is challenging due to incomplete protein sequence coverage and mapping ambiguities. Here, we demonstrate that a new software tool called ProteinClusterQuant (PCQ) can be used to deduce the presence of proteoforms that would have otherwise been missed, as exemplified in a proteomic comparison of two fly species, Drosophila melanogaster and D. virilis. PCQ was used to identify reduced levels of serine/threonine protein kinases PKN1 and PKN4 in CFBE41o - cells compared to HBE41o - cells and to elucidate that shorter proteoforms of full-length caspase-4 and ephrin B receptor are differentially expressed. Thus, PCQ extends current analyses in quantitative proteomics and facilitates finding differentially regulated proteins and proteoforms.

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .

Cellular proteomes are thought to be optimized for function, leaving no room for proteome plasticity and, thus, evolution. However, hybrid animals that result from a viable cross of two different species harbor hybrid proteomes of unknown complexity. We charted the hybrid proteome of a viable cross between Drosophila melanogaster females and Drosophila simulans males with bottom-up proteomics. Developing hybrids harbored 20% novel proteins in addition to proteins that were also present in either parental species. In contrast, adult hybrids and developmentally failing embryos of the reciprocal cross showed less additional proteins (5 and 6%, respectively). High levels of heat shock proteins, proteasome-associated proteins, and proteasomal subunits indicated that proteostasis sustains the expanded complexity of the proteome in developing hybrids. We conclude that increased proteostasis gives way to proteomic plasticity and thus opens up additional space for rapid phenotypic variation during embryonic development.

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.

Niemann-Pick type C (NPC) disease is an inherited, progressive neurodegenerative disorder principally caused by mutations in the NPC1 gene. NPC disease is characterized by the accumulation of unesterified cholesterol in the late endosomes (LE) and lysosomes (Ly) (LE/Ly). Vorinostat, a histone deacetylase inhibitor (HDACi), restores cholesterol homeostasis in fibroblasts derived from NPC patients; however, the exact mechanism by which Vorinostat restores cholesterol level is not known yet. In this study, we performed comparative proteomic profiling of the response of NPC1 I1061T fibroblasts to Vorinostat. After stringent statistical criteria to filter identified proteins, we observed 202 proteins that are differentially expressed in Vorinostat-treated fibroblasts. These proteins are members of diverse cellular pathways including the endomembrane dependent protein folding-stability-degradation-trafficking axis, energy metabolism, and lipid metabolism. Our study shows that treatment of NPC1 I1061T fibroblasts with Vorinostat not only enhances pathways promoting the folding, stabilization and trafficking of NPC1 (I1061T) mutant to the LE/Ly, but alters the expression of lysosomal proteins, specifically the lysosomal acid lipase (LIPA) involved in the LIPA->NPC2->NPC1 based flow of cholesterol from the LE/Ly lumen to the LE/Ly membrane. We posit that the Vorinostat may modulate numerous pathways that operate in an integrated fashion through epigenetic and post-translational modifications reflecting acetylation/deacetylation balance to help manage the defective NPC1 fold, the function of the LE/Ly system and/or additional cholesterol metabolism/distribution pathways, that could globally contribute to improved mitigation of NPC1 disease in the clinic based on as yet uncharacterized principles of cellular metabolism dictating cholesterol homeostasis., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

Microproteins are peptides and small proteins encoded by small open reading frames (smORFs). Newer technologies have led to the recent discovery of hundreds to thousands of new microproteins. The biological functions of a few microproteins have been elucidated, and these microproteins have fundamental roles in biology ranging from limb development to muscle function, highlighting the value of characterizing these molecules. The identification of microprotein-protein interactions (MPIs) has proven to be a successful approach to the functional characterization of these genes; however, traditional immunoprecipitation methods result in the enrichment of nonspecific interactions for microproteins. Here, we test and apply an in situ proximity tagging method that relies on an engineered ascorbate peroxidase 2 (APEX) to elucidate MPIs. The results demonstrate that APEX tagging is superior to traditional immunoprecipitation methods for microproteins. Furthermore, the application of APEX tagging to an uncharacterized microprotein called C11orf98 revealed that this microprotein interacts with nucleolar proteins nucleophosmin and nucleolin, demonstrating the ability of this approach to identify novel hypothesis-generating MPIs.

Activity-based protein profiling (ABPP) serves as a chemical proteomic platform to discover and characterize functional amino acids in proteins on the basis of their enhanced reactivity towards small-molecule probes. This approach, to date, has mainly targeted nucleophilic functional groups, such as the side chains of serine and cysteine, using electrophilic probes. Here we show that 'reverse-polarity' (RP)-ABPP using clickable, nucleophilic hydrazine probes can capture and identify protein-bound electrophiles in cells. Using this approach, we demonstrate that the pyruvoyl cofactor of S-adenosyl-L-methionine decarboxylase (AMD1) is dynamically controlled by intracellular methionine concentrations. We also identify a heretofore unknown modification-an N-terminally bound glyoxylyl group-in the poorly characterized protein secernin-3. RP-ABPP thus provides a versatile method to monitor the metabolic regulation of electrophilic cofactors and discover novel types of electrophilic modifications on proteins in human cells.

The mammalian inner ear (IE) subserves auditory and vestibular sensations via highly specialized cells and proteins. Sensory receptor hair cells (HCs) are necessary for transducing mechanical inputs and stimulating sensory neurons by using a host of known and as yet unknown protein machinery. To understand the protein composition of these unique postmitotic cells, in which irreversible protein degradation or damage can lead to impaired hearing and balance, we analyzed IE samples by tandem mass spectrometry to generate an unbiased, shotgun-proteomics view of protein identities and abundances. By using Pou4f3/eGFP-transgenic mice in which HCs express GFP driven by Pou4f3, we FACS purified a population of HCs to analyze and compare the HC proteome with other IE subproteomes from sensory epithelia and whole IE. We show that the mammalian HC proteome comprises hundreds of uniquely or highly expressed proteins. Our global proteomic analysis of purified HCs extends the existing HC transcriptome, revealing previously undetected gene products and isoform-specific protein expression. Comparison of our proteomic data with mouse and human databases of genetic auditory/vestibular impairments confirms the critical role of the HC proteome for normal IE function, providing a cell-specific pool of candidates for novel, important HC genes. Several proteins identified exclusively in HCs by proteomics and verified by immunohistochemistry map to human genetic deafness loci, potentially representing new deafness genes., Significance Statement: Hearing and balance rely on specialized sensory hair cells (HCs) in the inner ear (IE) to convey information about sound, acceleration, and orientation to the brain. Genetically and environmentally induced perturbations to HC proteins can result in deafness and severe imbalance. We used transgenic mice with GFP-expressing HCs, coupled with FACS sorting and tandem mass spectrometry, to define the most complete HC and IE proteome to date. We show that hundreds of proteins are uniquely identified or enriched in HCs, extending previous gene expression analyses to reveal novel HC proteins and isoforms. Importantly, deafness-linked proteins were significantly enriched in HCs, suggesting that this in-depth proteomic analysis of IE sensory cells may hold potential for deafness gene discovery., (Copyright © 2017 the authors 0270-6474/17/371320-20$15.00/0.)

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Background: Mass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations., Results: Our approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed "Blazmass") to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples., Conclusions: The combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

PSEA-Quant analyzes quantitative mass spectrometry-based proteomics datasets to identify enrichments of annotations contained in repositories such as the Gene Ontology and Molecular Signature databases. It allows users to identify the annotations that are significantly enriched for reproducibly quantified high abundance proteins. PSEA-Quant is available on the Web and as a command-line tool. It is compatible with all label-free and isotopic labeling-based quantitative proteomics methods. This protocol describes how to use PSEA-Quant and interpret its output. The importance of each parameter as well as troubleshooting approaches are also discussed. © 2016 by John Wiley & Sons, Inc., (Copyright © 2016 John Wiley & Sons, Inc.)

Unlabelled: Tarantula spiders, Theraphosidae family, are spread throughout most tropical regions of the world. Despite their size and reputation, there are few reports of accidents. However, like other spiders, their venom is considered a remarkable source of toxins, which have been selected through millions of years of evolution. The present work provides a proteomic overview of the fascinating complexity of the venomous extract of the Grammostola iheringi tarantula, obtained by electrical stimulation of the chelicerae. For analysis a bottom-up proteomic approach Multidimensional Protein Identification Technology (MudPIT) was used. Based on bioinformatics analyses, PepExplorer, a similarity-driven search tool that identifies proteins based on phylogenetically close organisms, a total of 395 proteins were identified in this venomous extract. Most of the identifications (~70%) were classified as predicted (21%), hypothetical (6%) and putative (37%), while a small group (6%) had no predicted function. Identified molecules matched with neurotoxins that act on ions channels; proteases, such as serine proteases, metalloproteinases, cysteine proteinases, aspartic proteinases, carboxypeptidases and cysteine-rich secretory enzymes (CRISP) and some molecules with unknown target. Additionally, non-classical venom proteins were also identified. Up to now, this study represents, to date, the first broad characterization of the composition of G. iheringi venomous extract. Our data provides a tantalizing insight into the diversity of proteins in this venom and their biotechnological potential., Significance: Animal venoms contain a diversity of molecules able to bind to specific cell targets. Due to their biochemical and physiological properties, these molecules are interesting for medical and biotechnological purposes. In this study, a large number of components of the venomous extract of the spider Grammostola iheringi were identified by the MudPIT technique. It was demonstrated that this approach is a sensitive and adequate method to achieve a broad spectrum of information about animal venoms. Using this bottom-up proteomic method, classical and non-classical venom proteins were identified which stimulate new interest in the systematic research of their protein components., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Infestation of phytophagous mite Schizotetranychus oryzae in rice causes critical yield losses. To better understand this interaction, we employed Multidimensional Protein Identification Technology (MudPIT) approach to identify differentially expressed proteins. We detected 18 and 872 unique proteins in control and infested leaves, respectively, along with 32 proteins more abundant in control leaves. S. oryzae infestation caused decreased abundance of proteins related to photosynthesis (mostly photosystem II-related), carbon assimilation and energy production, chloroplast detoxification, defense, and fatty acid and gibberellin synthesis. On the contrary, infestation caused increased abundance of proteins involved in protein modification and degradation, gene expression at the translation level, protein partitioning to different organelles, lipid metabolism, actin cytoskeleton remodeling, and synthesis of jasmonate, amino acid, and molecular chaperones. Our results also suggest that S. oryzae infestation promotes cell-wall remodeling and interferes with ethylene biosynthesis in rice leaves. Proteomic data were positively correlated with enzymatic assays and RT-qPCR analysis. Our findings describe the protein expression patterns of infested rice leaves and suggest that the acceptor side of PSII is probably the major damaged target in the photosynthetic apparatus. These data will be useful in future biotechnological approaches aiming to induce phytophagous mite resistance in rice.

The analysis of protein half-life and degradation dynamics has proven critically important to our understanding of a broad and diverse set of biological conditions ranging from cancer to neurodegeneration. Historically these protein turnover measures have been performed in cells by monitoring protein levels after "pulse" labeling of newly synthesized proteins and subsequent chase periods. Comparing the level of labeled protein remaining as a function of time to the initial level reveals the protein's half-life. In this method we provide a detailed description of the workflow required for the determination of protein turnover rates on a whole proteome scale in vivo. Our approach starts with the metabolic labeling of whole rodents by restricting all the nitrogen in their diet to exclusively nitrogen-15 in the form of spirulina algae. After near complete organismal labeling with nitrogen-15, the rodents are then switched to a normal nitrogen-14 rich diet for time periods of days to years. Tissues are harvested, the extracts are fractionated, and the proteins are digested to peptides. Peptides are separated by multidimensional liquid chromatography and analyzed by high resolution orbitrap mass spectrometry (MS). The nitrogen-15 containing proteins are then identified and measured by the bioinformatic proteome analysis tools Sequest, DTASelect2, and Census. In this way, our metabolic pulse-chase approach reveals in vivo protein decay rates proteome-wide.

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.