Mass Spectrometry Datasets Research Articles

Likelihood-based bacterial identification approach for bimicrobial mass spectrometry data

Mass spectrometry is a potential diagnostic tool for rapid bacterial detection. However, in order to use this technology in clinical settings, it is important to develop sound statistical algorithms that can accurately analyze polymicrobial mass spectrometry data. Here, we propose a likelihood-based bacterial identification algorithm for bimicrobial mass spectrometry data. Specifically, we introduce a two-component mixture model with partially known labels. This method can model peaks with unknown origins. It also considers errors in mass-to-charge ratios and intensities of peaks between observed and reference mass spectra. Coupled with a decoy strategy, the likelihood is used to identify bacterial species and to measure uncertainty of such identifications. Using two real mass spectrometry datasets, we demonstrate the superior performance of our approach in accurate bacterial identifications, compared to model-free approaches. Example datasets and R codes for the proposed method are freely available under MIT license at https://github.com/soyoungryu/BacID.

Bayesian model calibration for vacuum-ultraviolet photoionisation mass spectrometry

We investigate the calibration of an instrument model for a high-pressure chemical reactor and vacuum ultraviolet photoionisation mass spectrometry apparatus, using a collection of ‘static’ mass spectrometry data (i.e. from unreactive samples). A Bayesian calibration method is applied to characterise the uncertainty of the model parameters, the associated model error, and the predicted mass spectrum. A partitioning algorithm is introduced to decompose the mass spectrum into several independent calibration problems, making the analysis of complex mass spectrometry data sets tractable. The a priori mass spectrometer model poorly predicts some portions of the ion time-of-flight spectrum due to both, parametric (i.e. uncertain parameter values) and mechanistic (i.e. missing physics) inadequacies; however, by incorporating an additive model error the prediction model is vastly improved and closely replicates the measured spectrum. We found that the model calibration and additive model error reveals a set of experimental problems, including chemical contamination of the sample, the presence of high harmonics of the ionising radiation, and a systematic fluctuation in ion flight times due to instability of the ion optics power supplies. This approach results in a calibrated instrument model with well-defined parameter uncertainties, which is critical to the analysis of ‘dynamic’ data (i.e. from reacting gas samples).

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.

BackgroundThe use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied.ObjectiveThe aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets.MethodsWe applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5.ResultsThe experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency.ConclusionsThe XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

Compound Dereplication and De Novo Characterization of Small Molecules by Mass Spectrometry

We describe the molecular dereplication principles and de novo characterization of small molecules obtained from liquid-chromatography mass spectrometry and imaging mass spectrometry data sets. Our methodology aims at supporting chemists and computer programmers to understand the hidden computing algorithms used for metabolomics mass spectrometry data processing. The approaches have been made available in the open-source tool CycloBranch. The presented tutorial extends the interpretation of mass spectra portfolia described in a series of papers published in Chemicke Listy, issues 2/2020 and 3/2020.

DbPepVar: A Novel Cancer Proteogenomics Database

Cancers arise from the acquisition of DNA mutations, such as substitutions, deletions, amplifications, and rearrangements. Understanding the distribution and correlation of such mutations in cancer may aid the characterization of the disease and subsequent identification of biomarkers for diagnosis and treatment. The proteogenomics database (dbPepVar) created here combines genetic variation information from dbSNP with protein sequences from NCBI’s RefSeq. Public mass spectrometry datasets (Ovarian, Colorectal, Breast, and Prostate) were used to perform a pan-cancer analysis, allowing the identification of unique genetic variations. As a result, 3,726 variant peptides were identified in samples from patients with ovarian cancer, 2,543 in prostate, 2,661 in breast and 2,411 in colon-rectal cancer patients. Data resulting from the proteogenomics approach employed and connected to other biological databases is now available in an intuitive and dynamic web portal where novice users can explore general aspects of the dataset in graph or table format, or dive in to filter the data with click and select options or using more advanced queries with regex. All data can be downloaded in csv or pdf format. In perspective, the web portal developed may direct studies to identify new therapeutic targets for different cancers, and one can also use our database for characterization of variants in samples of unknown genetic background, such as archived samples.

A Peptide Encoded by a Long Non-Coding RNA DLX6-AS1 Facilitates Cell Proliferation, Migration, and Invasion by Activating the wnt/β-Catenin Signaling Pathway in Non-Small-Cell Lung Cancer Cell.

Recently, accumulating study shows that some long non-coding RNAs (lncRNAs) have potential protein/peptide-coding capacities. In this study, the coding potential of lncRNA distal-less homeobox 6 antisense 1 (DLX6-AS1) was examined and the roles and downstream pathways of a DLX6-AS1-encoded peptide in non-small-cell lung cancer (NSCLC) cell development were investigated. The peptide-coding potential of lncRNA DLX6-AS1 was extrapolated based on prior ribosome footprint and ribosome sequencing data, IPX0002962000 mass spectrometry dataset, and Getorf bioinformatics analysis. The peptide-coding abilities of several DLX6-AS1 open reading frame (ORF) fragments, as well as protein levels were detected by Western blot assay. Cell proliferative, migratory, and invasive abilities were tested by CCK-8 or Transwell assays, respectively. Potential key biological processes and pathways related to DLX6-AS1 expression were identified by single-gene gene set enrichment analysis (GSEA) based on RNA-seq data of 510 lung adenocarcinoma samples in the TCGA GDC database. The results showed that an ORF of lncRNA DLX6-AS1 could encode a short peptide. The exogenous overexpression of this ORF-encoded peptide promoted NSCLC cell proliferation, migration, and invasion. GSEA analysis suggested that DLX6-AS1 might play crucial roles in cancer progression and wnt signaling pathway. Further analysis revealed that the exogenous overexpression of a DLX6-AS1-encoded peptide could exert its functions by activating the wnt/β-catenin pathway in NSCLC cells. In conclusion, the exogenous overexpression of a DLX6-AS1-encoded peptide could facilitate NSCLC cell growth by activating wnt/β-catenin pathway.

Mass Deconvolution of Top-Down Mass Spectrometry Datasets by FLASHDeconv.

Mass deconvolution, the determination of proteoform precursor and fragment masses, is crucial for top-down proteomics data analysis. Here we describe the detailed procedure to run FLASHDeconv, an ultrafast, high-quality mass deconvolution tool. Both spectrum- and feature-level deconvolution results are obtainable in various output formats by FLASHDeconv. FLASHDeconv is runnable in different environments such as the command line and OpenMS workflows.

INCA 2.0: A tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.

Selection of Formal Baseline Correction Methods in Thermal Analysis

AbstractBaseline correction is a key step in processing of thermal analysis data. Whilst this is a common step, techniques range from linear baselines to use of high‐order polynomials. When considering a formal baseline correction (those without physical or experimental justification), only linear correction methods should be used: linear with time, linear with temperature, and linear with extent of reaction. The absence of baseline correction should also be considered. An in silico study shows that the wrong baseline correction can significantly impact the parameters obtained from kinetic modeling. The four baseline correction methods are demonstrated with a mass spectrometry dataset. It is recommended that the selection of correction method should be based on comparison of Akaike weights.

RTP: One Effective Platform to Probe Reactive Compound Transformation Products and Its Applications for a Reactive Plasticizer BADGE.

Reactive compounds, such as covalent toxicants/drugs, have their ubiquitous occurrences and are known to react with protein or DNA in human beings, but their reactions with endogenous metabolites are rarely understood. Currently, a viable platform is demanded for discovering their reaction products since their efficacy/toxicity may be altered after the reaction. We aim to develop a platform for identifying unknown abiotic or biotransformation products for these reactive compounds. Based on stable isotope-labeling (SIL) metabolomics, we have developed a novel and robust analytical platform, reactive compound transformation profiler (RTP), which can automatically analyze preannotated high-resolution mass spectrometry (LC-HRMS) data sets and uncover probable transformation products. Generally, RTP consists of four complementary steps: (1) selecting peak pairs of light and heavy-labeled products, (2) defining the "core structure mass" for possible reaction search, (3) constructing an endogenous metabolite reaction database, and (4) developing algorithms to propose the potential transformation products by searching against the database with a single-/multiple-site reaction. Its performance was validated using the reactive plasticizer bisphenol A diglycidyl ether (BADGE) in several sample matrices. This platform enabled the identification of novel transformation products while also demonstrating its capacity to filter out the false-positive signals and provide product annotation. The RTP is freely accessible at https://github.com/FangLabNTU/Reactive-Compound-Transformation-Profiler-RTP-.

Probing the Metabolic Landscape of Plant Vascular Bundles by Infrared Fingerprint Analysis, Imaging and Mass Spectrometry.

Fingerprint analysis is a common technique in forensic and criminal investigations. Similar techniques exist in the field of infrared spectroscopy to identify biomolecules according to their characteristic spectral fingerprint features. These unique markers are located in a wavenumber range from 1800 to 600 cm−1 in the mid infrared region. Here, a novel bioanalytical concept of correlating these spectral features with corresponding mass spectrometry datasets to unravel metabolic clusters within complex plant tissues was applied. As proof of concept, vascular bundles of oilseed rape (Brassica napus) were investigated, one of the most important and widely cultivated temperate zone oilseed crops. The link between mass spectrometry data and spectral data identified features that co-aligned within both datasets. Regions of origin were then detected by searching for these features in hyperspectral images of plant tissues. This approach, based on co-alignment and co-localization, finally enabled the detection of eight distinct metabolic clusters, reflecting functional and structural arrangements within the vascular bundle. The proposed analytical concept may assist future synergistic research approaches and may lead to biotechnological innovations with regard to crop yield and sustainability.

Proteomic analysis of differential expression of lung proteins in response to highly pathogenic avian influenza virus infection in chickens

Elucidation of the molecular pathogenesis underlying virus-host interactions is important for the development of new diagnostic and therapeutic strategies against highly pathogenic avian influenza (HPAI) virus infection in chickens. However, the pathogenesis of HPAI virus in chickens is not completely understood. To identify the intracellular signaling pathways and critical host proteins associated with influenza pathogenesis, we analyzed the lung proteome of a chicken infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala). Mass spectrometry data sets were searched against the chicken UniProt reference database. At the local false discovery rate level of 5%, a total of 3313 proteins with the presence of at least one unique peptide were identified in the chicken lung proteome datasets. Differential expression analysis of these proteins showed that 247 and 1754 proteins were downregulated at 12 h and 48 h postinfection, respectively. We observed expression of proteins of the predominant signaling pathways, including Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and JAK-STAT signaling. Activation of these pathways is associated with the cytokine storm effect and thus may be the cause of the severity of HPAI H5N1 infection in chickens. We also observed the expression of myeloid differentiation primary response protein (MyD88), inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB), interleukin 1 receptor associated kinase 4 (IRAK4), RELA proto-oncogene NF-κB subunit (RELA), and mitochondrial antiviral signaling protein (MAVS), which are involved in critical signaling pathways, as well as other, less-commonly identified proteins such as hepatocyte nuclear factor 4 alpha (HNF4A), ELAV-like RNA binding protein 1 (ELAVL1), fibronectin 1 (FN1), COP9 signalosome subunit 5 (COPS5), cullin 1 (CUL1), breast cancer type 1 susceptibility protein (BRCA1), and the FYN proto-oncogene Src family tyrosine kinase (FYN) as main hub proteins that might play important roles in influenza pathogenesis in chickens. In summary, we identified the signaling pathways and the proteomic determinants associated with disease pathogenesis in chickens infected with HPAI H5N1 virus.

Abstract 14286: Crosstalk Between Methylglyoxal and Acetylation Modifications on Actin, Myosin, and Myosin Light Chain Proteins Affects Sarcomere Functionality

Diabetes affects ten percent of Americans. Risk of developing heart failure in these patients is doubled, independent of coronary artery disease and hypertension. However, the mechanistic connection between these two diseases is poorly understood. We have shown one connection is likely due to methylglyoxal (MGO), a highly reactive glycolysis product that can irreversibly modify lysine and arginine residues, a process called glycation. Moreover, we found MGO glycation of the myofilament decreased both calcium sensitivity and maximum calcium activated force (Fmax). Here, we hypothesized that MGO may also negatively impact function by competing for lysine residues that would otherwise be targets for acetylation, which has been shown to be beneficial to cardiac function. Using non-failing human left ventricular (LV) myocardial tissue that had been exposed to acute treatment of MGO (500 μM) or vehicle and subsequently treated with a high dose of acetic anhydride in acetonitrile (300μM) overnight, we found that compared to the vehicle treated samples, MGO inhibited overall acetylation, suggesting that these two PTMs do compete for a subset of lysine residues in the heart. Next, we compared mass spectrometry data sets from diabetic humans, MGO treated mouse myocytes, and acetylated myocytes to identify the specific overlapping residues that acetylation and glycation compete for. This analysis identified multiple sites of crosstalk on actin, myosin, and myosin light chains - proteins essential for normal contractile function. Our preliminary data support a contractile consequence to this crosstalk. Incubation of mouse LV isolated skinned myocytes with acetic anhydride in acetonitrile (300μM, overnight) increased both calcium sensitivity and Fmax. However, exposure to MGO first (500μM) blocked these beneficial functions acetylation (300μM, overnight) in mouse myocytes. Together, we believe these data demonstrate that glycation and acetylation do compete for residues in the myofilament, and that glycation is capable of blocking the beneficial functional effects of protein acetylation. If these results are confirmed and extended in vivo, they could have important consequences in diabetes and in the utility of using HDAC inhibitors clinically.

A Bi-directional Hierarchical Clustering (BHC) for Peak Matching of Large Mass Spectrometry Data Sets

A Bi-directional Hierarchical Clustering (BHC) for Peak Matching of Large Mass Spectrometry Data Sets

65 Identification of frequently presented non-mutated tumor-specific immunogens for the development of both off-the-shelf and personalized vaccines without need for tumor biopsy

BackgroundVaccines have little chance of destroying heterogeneous tumor cells since they rarely induce polyclonal T-cell responses against the tumor. The main challenge is the accurate identification of tumor targets recognizable by T cells. Presently, 6–8% of neoepitopes selected based on the patients‘ tumor biopsies are confirmed as real T cell targets.1 2. To overcome this limitation, we developed a computational platform called Personal Antigen Selection Calculator (PASCal) that identifies frequently presented immunogenic peptide sequences built on HLA-genetics and tumor profile of thousands of real individuals.3 Here we show the performance of PASCal for the identification of both shared and personalized tumor targets in metastatic colorectal cancer (mCRC) and breast cancer subjects.MethodsExpression frequency of the tumor-specific antigens (TSAs) ranked in PASCal’s database (based on 7,548 CRC specimen) was compared to the RNA-sequencing data of CRC tumors obtained from TCGA. Using PASCal, 12 shared PEPIs (epitopes restricted to at least 3 HLA class I alleles of a subject from an in silico cohort) derived from 7 TSAs were selected as frequent targets (calculated probability: average 2.5 [95%CI 2.4–2.8] TSAs/patient). Spontaneous immune responses against each of the twelve 9mer peptides were determined by ELISpot using PBMCs of 10 mCRC subjects who participated in the OBERTO-101 study.4 PEPIs selected for a breast cancer subject based on her HLA genotype were also tested.ResultsEach of the 106 tumors analyzed expressed at least 13, average 15 of the 20 top-ranked TSAs in PASCal’s database confirming their prevalence in CRC. 7/10 subjects had spontaneous CD8+ T-cell responses against at least one peptide selected with PASCal. Each peptide (12/12) was recognized by at least one patient. Patients‘ T-cells reacted with average 3.6/12 (30%) peptides confirming the expression of average 2.8 [95%CI 1.0–4.6] TSAs (n=10). After HLA-matching, among the subjects for whom we could select at least 4 PEPIs (average 5) from the list of 12 peptides (n=6), average 2.5 (50%) peptides were positive. Of the 12 PEPIs selected with PASCal for a breast cancer subject, we detected spontaneous T-cell responses against 9 PEPIs, indicating that at least 75% of the selected peptides were present in the subject’s tumor and were recognized by T-cells.ConclusionsPASCal platform accommodates both tumor- and patient heterogeneity and identifies non-mutated tumor targets that may trigger polyclonal cytotoxic T-cell responses. It is a rapid tool for the design of both off-the-shelf and personalized cancer vaccines negating the need for tumor biopsy.ReferencesWells DK, van Buuren MM, Dang KK, et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 2020:183(3):818–34.e13.Bulik-Sullivan B, Busby J, Palmer CD, et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat Biotech 2018:37:55–63.Somogyi E, Csiszovszki Z, Lorincz O, et al. 1181PDPersonal antigen selection calculator (PASCal) for the design of personal cancer vaccines. Annal Oncol 2019:30(Supplement_5):v480-v81.Hubbard J, Cremolini C, Graham R, et al. P329 PolyPEPI1018 off-the shelf vaccine as add-on to maintenance therapy achieved durable treatment responses in patients with microsatellite-stable metastatic colorectal cancer patients (MSS mCRC). J ImmunoTher Cancer 2019:7(1):282.

Revealing Contamination and Sequence of Overlapping Fingerprints by Unsupervised Treatment of a Hyperspectral Secondary Ion Mass Spectrometry Dataset.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been successfully applied for chemical imaging of overlapping fingermarks. The resulting big dataset has been treated by means of an unsupervised machine learning approach based on uniform manifold approximation and projection. The hyperspectral matrix was composed of 49 million pixels associated with 518 peaks. However, the single-pixel spectrum results in a very poor signal intensity, mostly like a barcode. Contrary to what has been reported in the literature recently, we have not applied a crude approach based on binning but a sophisticated machine learning method capable of separating the chemical signals of the two fingerprints from each other and from the substrate in which they were impressed. Moreover, using ToF-SIMS, an extremely surface-sensitive technique, the sequence of deposition of the fingerprints has been determined.

Peptide Location Fingerprinting Reveals Tissue Region-Specific Differences in Protein Structures in an Ageing Human Organ.

In ageing tissues, long-lived extracellular matrix (ECM) proteins are susceptible to the accumulation of structural damage due to diverse mechanisms including glycation, oxidation and protease cleavage. Peptide location fingerprinting (PLF) is a new mass spectrometry (MS) analysis technique capable of identifying proteins exhibiting structural differences in complex proteomes. PLF applied to published young and aged intervertebral disc (IVD) MS datasets (posterior, lateral and anterior regions of the annulus fibrosus) identified 268 proteins with age-associated structural differences. For several ECM assemblies (collagens I, II and V and aggrecan), these differences were markedly conserved between degeneration-prone (posterior and lateral) and -resistant (anterior) regions. Significant differences in peptide yields, observed within collagen I α2, collagen II α1 and collagen V α1, were located within their triple-helical regions and/or cleaved C-terminal propeptides, indicating potential accumulation of damage and impaired maintenance. Several proteins (collagen V α1, collagen II α1 and aggrecan) also exhibited tissue region (lateral)-specific differences in structure between aged and young samples, suggesting that some ageing mechanisms may act locally within tissues. This study not only reveals possible age-associated differences in ECM protein structures which are tissue-region specific, but also highlights the ability of PLF as a proteomic tool to aid in biomarker discovery.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure.

Advance access and reuse mechanisms for large-scale Mass Spectrometry (MS) data are essential for democratizing data for the omics research community and making it adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although a number of centralized data repositories have been established, they have been limited to search mechanisms that depend on the meta-data associated with these MS datasets. Furthermore, they require constant influx of resources for maintenance. In this paper, we proposed an alternative novel distributed infrastructure for direct MS/MS spectral search. We designed and developed a simulation testbed using concepts from computer networks, queuing theory, and stochastic simulation methods. Results show that a distributed MS search based on raw MS/MS spectra can scale gracefully for up-to 2000 participating nodes, while simultaneously processing queries using the proposed networked infrastructure on the order of milliseconds to a few seconds for up-to a total of fifty billion MS/MS spectra.

Proteomic Analysis of the Meniscus Cartilage in Osteoarthritis

The distribution of differential extracellular matrix (ECM) in the lateral and medial menisci can contribute to knee instability, and changes in the meniscus tissue can lead to joint disease. Thus, deep proteomic identification of the lateral and medial meniscus cartilage is expected to provide important information for treatment and diagnosis of various knee joint diseases. We investigated the proteomic profiles of 12 lateral/medial meniscus pairs obtained from excess tissue of osteoarthritis patients who underwent knee arthroscopy surgery using mass spectrometry-based techniques and measured 75 ECM protein levels in the lesions using a multiple reaction monitoring (MRM) assay we developed. A total of 906 meniscus proteins with a 1% false discovery rate (FDR) was identified through a tandem mass tag (TMT) analysis showing that the lateral and medial menisci had similar protein expression profiles. A total of 131 ECM-related proteins was included in meniscus tissues such as collagen, fibronectin, and laminin. Our data showed that 14 ECM protein levels were differentially expressed in lateral and medial lesions (p < 0.05). We present the proteomic characterization of meniscal tissue with mass spectrometry-based comparative proteomic analysis and developed an MRM-based assay of ECM proteins correlated with tissue regeneration. The mass spectrometry dataset has been deposited to the MassIVE repository with the dataset identifier MSV000087753.