Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

Evaluating Proteomics Imputation Methods with Improved Criteria.

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows.

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching.

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

Tandem Mass Spectrometry-Based Amyloid Typing Using Manual Microdissection and Open-Source Data Processing.

OBJECTIVES: Standard implementations of amyloid typing by liquid chromatography-tandem mass spectrometry use capabilities unavailable to most clinical laboratories. To improve accessibility of this testing, we explored easier approaches to tissue sampling and data processing. METHODS: We validated a typing method using manual sampling in place of laser microdissection, pairing the technique with a semiquantitative measure of sampling adequacy. In addition, we created an open-source data processing workflow (Crux Pipeline) for clinical users. RESULTS: Cases of amyloidosis spanning the major types were distinguishable with 100% specificity using measurements of individual amyloidogenic proteins or in combination with the ratio of λ and κ constant regions. Crux Pipeline allowed for rapid, batched data processing, integrating the steps of peptide identification, statistical confidence estimation, and label-free protein quantification. CONCLUSIONS: Accurate mass spectrometry-based amyloid typing is possible without laser microdissection. To facilitate entry into solid tissue proteomics, newcomers can leverage manual sampling approaches in combination with Crux Pipeline and related tools.

Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

ppx: Programmatic Access to Proteomics Data Repositories.

The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can be used as either a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published data set with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows, and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at https://github.com/wfondrie/ppx.

Subzero, saline incubations of Colwellia psychrerythraea reveal strategies and biomarkers for sustained life in extreme icy environments.

Colwellia psychrerythraea is a marine psychrophilic bacterium known for its remarkable ability to maintain activity during long-term exposure to extreme subzero temperatures and correspondingly high salinities in sea ice. These microorganisms must have adaptations to both high salinity and low temperature to survive, be metabolically active, or grow in the ice. Here, we report on an experimental design that allowed us to monitor culturability, cell abundance, activity and proteomic signatures of C. psychrerythraea strain 34H (Cp34H) in subzero brines and supercooled sea water through long-term incubations under eight conditions with varying subzero temperatures, salinities and nutrient additions. Shotgun proteomics found novel metabolic strategies used to maintain culturability in response to each independent experimental variable, particularly in pathways regulating carbon, nitrogen and fatty acid metabolism. Statistical analysis of abundances of proteins uniquely identified in isolated conditions provide metabolism-specific protein biosignatures indicative of growth or survival in either increased salinity, decreased temperature, or nutrient limitation. Additionally, to aid in the search for extant life on other icy worlds, analysis of detected short peptides in -10°C incubations after 4 months identified over 500 potential biosignatures that could indicate the presence of terrestrial-like cold-active or halophilic metabolisms on other icy worlds.

mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments.

Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semisupervised algorithms to learn models directly from the data sets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results were reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large data set and use the learned model to evaluate the small-scale experiment. We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" that is trained anew for each data set. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semisupervised algorithms to small-scale experiments.

Rapid Microbial Identification and Antibiotic Resistance Detection by Mass Spectrometric Analysis of Membrane Lipids.

Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship. Current technologies employed in routine diagnostics are based on bacterial culture followed by morphological trait differentiation and biochemical testing, which can be time-consuming and labor-intensive. With advances in mass spectrometry (MS) for clinical diagnostics, the U.S. Food and Drug Administration has approved two microbial identification platforms based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of microbial proteins. We recently reported a novel and complementary approach by comparing MALDI-TOF mass spectra of microbial membrane lipid fingerprints to identify ESKAPE pathogens. However, this lipid-based approach used a sample preparation method that required more than a working day from sample collection to identification. Here, we report a new method that extracts lipids efficiently and rapidly from microbial membranes using an aqueous sodium acetate (SA) buffer that can be used to identify clinically relevant Gram-positive and -negative pathogens and fungal species in less than an hour. The SA method also has the ability to differentiate antibiotic-susceptible and antibiotic-resistant strains, directly identify microbes from biological specimens, and detect multiple pathogens in a mixed sample. These results should have positive implications for the manner in which bacteria and fungi are identified in general hospital settings and intensive care units.

Pathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipids.

With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.

LRP1 (Low-Density Lipoprotein Receptor-Related Protein 1) Regulates Smooth Muscle Contractility by Modulating Ca2+ Signaling and Expression of Cytoskeleton-Related Proteins.

Objective- Mutations affecting contractile-related proteins in the ECM (extracellular matrix), microfibrils, or vascular smooth muscle cells can predispose the aorta to aneurysms. We reported previously that the LRP1 (low-density lipoprotein receptor-related protein 1) maintains vessel wall integrity, and smLRP1-/- mice exhibited aortic dilatation. The current study focused on defining the mechanisms by which LRP1 regulates vessel wall function and integrity. Approach and Results- Isometric contraction assays demonstrated that vasoreactivity of LRP1-deficient aortic rings was significantly attenuated when stimulated with vasoconstrictors, including phenylephrine, thromboxane receptor agonist U-46619, increased potassium, and L-type Ca2+ channel ligand FPL-64176. Quantitative proteomics revealed proteins involved in actin polymerization and contraction were significantly downregulated in aortas of smLRP1-/- mice. However, studies with calyculin A indicated that although aortic muscle from smLRP1-/- mice can contract in response to calyculin A, a role for LRP1 in regulating the contractile machinery is not revealed. Furthermore, intracellular calcium imaging experiments identified defects in calcium release in response to a RyR (ryanodine receptor) agonist in smLRP1-/- aortic rings and cultured vascular smooth muscle cells. Conclusions- These results identify a critical role for LRP1 in modulating vascular smooth muscle cell contraction by regulating calcium signaling events that potentially protect against aneurysm development.

Role of the LDL Receptor-Related Protein 1 in Regulating Protease Activity and Signaling Pathways in the Vasculature.

Aortic aneurysms represent a significant clinical problem as they largely go undetected until a rupture occurs. Currently, an understanding of mechanisms leading to aneurysm formation is limited. Numerous studies clearly indicate that vascular smooth muscle cells play a major role in the development and response of the vasculature to hemodynamic changes and defects in these responses can lead to aneurysm formation. The LDL receptor-related protein 1 (LRP1) is major smooth muscle cell receptor that has the capacity to mediate the endocytosis of numerous ligands and to initiate and regulate signaling pathways. Genetic evidence in humans and mouse models reveal a critical role for LRP1 in maintaining the integrity of the vasculature. Understanding the mechanisms by which this is accomplished represents an important area of research, and likely involves LRP1's ability to regulate levels of proteases known to degrade the extracellular matrix as well as its ability to modulate signaling events.

Identification of the ESKAPE pathogens by mass spectrometric analysis of microbial membrane glycolipids.

Rapid diagnostics that enable identification of infectious agents improve patient outcomes, antimicrobial stewardship, and length of hospital stay. Current methods for pathogen detection in the clinical laboratory include biological culture, nucleic acid amplification, ribosomal protein characterization, and genome sequencing. Pathogen identification from single colonies by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of high abundance proteins is gaining popularity in clinical laboratories. Here, we present a novel and complementary approach that utilizes essential microbial glycolipids as chemical fingerprints for identification of individual bacterial species. Gram-positive and negative bacterial glycolipids were extracted using a single optimized protocol. Extracts of the clinically significant ESKAPE pathogens: E nterococcus faecium, S taphylococcus aureus, K lebsiella pneumoniae, A cinetobacter baumannii, P seudomonas aeruginosa, and E nterobacter spp. were analyzed by MALDI-TOF-MS in negative ion mode to obtain glycolipid mass spectra. A library of glycolipid mass spectra from 50 microbial entries was developed that allowed bacterial speciation of the ESKAPE pathogens, as well as identification of pathogens directly from blood bottles without culture on solid medium and determination of antimicrobial peptide resistance. These results demonstrate that bacterial glycolipid mass spectra represent chemical barcodes that identify pathogens, potentially providing a useful alternative to existing diagnostics.

Assessment of the Therapeutic Potential of Persimmon Leaf Extract on Prediabetic Subjects.

Dietary supplements have exhibited myriads of positive health effects on human health conditions and with the advent of new technological advances, including in the fields of proteomics, genomics, and metabolomics, biological and pharmacological activities of dietary supplements are being evaluated for their ameliorative effects in human ailments. Recent interests in understanding and discovering the molecular targets of phytochemical-gene-protein-metabolite dynamics resulted in discovery of a few protein signature candidates that could potentially be used to assess the effects of dietary supplements on human health. Persimmon (Diospyros kaki) is a folk medicine, commonly used as dietary supplement in China, Japan, and South Korea, owing to its different beneficial health effects including anti-diabetic implications. However, neither mechanism of action nor molecular biomarkers have been discovered that could either validate or be used to evaluate effects of persimmon on human health. In present study, Mass Spectrometry (MS)-based proteomic studies were accomplished to discover proteomic molecular signatures that could be used to understand therapeutic potentials of persimmon leaf extract (PLE) in diabetes amelioration. Saliva, serum, and urine samples were analyzed and we propose that salivary proteins can be used for evaluating treatment effectiveness and in improving patient compliance. The present discovery proteomics study demonstrates that salivary proteomic profile changes were found as a result of PLE treatment in prediabetic subjects that could specifically be used as potential protein signature candidates.

Triple SILAC quantitative proteomic analysis reveals differential abundance of cell signaling proteins between normal and lung cancer-derived exosomes.

Exosomes are 30-100 nm sized membrane vesicles released by cells into the extracellular space that mediate intercellular communication via transfer of proteins and other biological molecules. To better understand the role of these microvesicles in lung carcinogenesis, we employed a Triple SILAC quantitative proteomic strategy to examine the differential protein abundance between exosomes derived from an immortalized normal bronchial epithelial cell line and two non-small cell lung cancer (NSCLC) cell lines harboring distinct activating mutations in the cell signaling molecules: Kirsten rat sarcoma viral oncogene homolog (KRAS) or epidermal growth factor receptor (EGFR). In total, we were able to quantify 721 exosomal proteins derived from the three cell lines. Proteins associated with signal transduction, including EGFR, GRB2 and SRC, were enriched in NSCLC exosomes, and could actively regulate cell proliferation in recipient cells. This study's investigation of the NSCLC exosomal proteome has identified enriched protein cargo that can contribute to lung cancer progression, which may have potential clinical implications in biomarker development for patients with NSCLC. BIOLOGICAL SIGNIFICANCE: The high mortality associated with lung cancer is a result of late-stage diagnosis of the disease. Current screening techniques used for early detection of lung cancer lack the specificity for accurate diagnosis. Exosomes are nano-sized extracellular vesicles, and the increased abundance of select protein cargo in exosomes derived from cancer cells may be used for diagnostic purposes. In this paper, we applied quantitative proteomic analysis to elucidate abundance differences in exosomal protein cargo between two NSCLC cell lines with distinctive oncogene mutations and an immortalized normal bronchial epithelial cell line. This study revealed proteins associated with cell adhesion, the extracellular matrix, and a variety of signaling molecules were enriched in NSCLC exosomes. The present data reveals a protein profile associated with NSCLC exosomes that suggests a role these vesicles have in the progression of lung carcinogenesis, as well as identifies several promising candidates that could be utilized as a multi-marker protein panel in a diagnostic platform for NSCLC.

Redefining the Breast Cancer Exosome Proteome by Tandem Mass Tag Quantitative Proteomics and Multivariate Cluster Analysis.

Exosomes are microvesicles of endocytic origin constitutively released by multiple cell types into the extracellular environment. With evidence that exosomes can be detected in the blood of patients with various malignancies, the development of a platform that uses exosomes as a diagnostic tool has been proposed. However, it has been difficult to truly define the exosome proteome due to the challenge of discerning contaminant proteins that may be identified via mass spectrometry using various exosome enrichment strategies. To better define the exosome proteome in breast cancer, we incorporated a combination of Tandem-Mass-Tag (TMT) quantitative proteomics approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived fractions corresponding to a 10 000g cellular debris pellet, a 100 000g crude exosome pellet, and an Optiprep enriched exosome pellet. The quantitative analysis identified 2 179 proteins in all three fractions, with known exosomal cargo proteins displaying at least a 2-fold enrichment in the exosome fraction based on the TMT protein ratios. Employing SVM cluster analysis allowed for the classification 251 proteins as "true" exosomal cargo proteins. This study provides a robust and vigorous framework for the future development of using exosomes as a potential multiprotein marker phenotyping tool that could be useful in breast cancer diagnosis and monitoring disease progression.