bims-mascan 2021-11-07 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2021‒11‒07
fifteen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

Analysis of Leukemia Cell Metabolism through Stable Isotope Tracing in Mice.
Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses.
Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow.
HERMES: a molecular-formula-oriented method to target the metabolome.
The Potential Role of Changes in the Glucose and Lipid Metabolic Pathways in Gastrointestinal Cancer Progression: Strategy in Cancer Therapy.
Fast Sampling of the Cellular Metabolome.
Stable isotope phosphate labelling of diverse metabolites is enabled by a family of 18O-phosphoramidites.
Structural library and visualization of endogenously oxidized phosphatidylcholines using mass spectrometry-based techniques.
Evaluation of Disposable Trap Column nanoLC-FAIMS-MS/MS for the Proteomic Analysis of FFPE Tissue.
Deep learning-derived evaluation metrics enable effective benchmarking of computational tools for phosphopeptide identification.
Comparison of two LC-MS/MS methods for the quantification of 24,25-dihydroxyvitamin D3 in patients and external quality assurance samples.
Development and Provisional Validation of a Multiplex LC-MRM-MS Test for Timely Kidney Injury Detection in Urine.
SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS.
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.
ClusterSheep: A Graphics Processing Unit-Accelerated Software Tool for Large-Scale Clustering of Tandem Mass Spectra from Shotgun Proteomics.

Bio Protoc. 2021 Oct 05. 11(19): e4171

Analysis of Leukemia Cell Metabolism through Stable Isotope Tracing in Mice.

Nick van Gastel, Jessica B Spinelli, Marcia C Haigis, David T Scadden.

  Once thought to be a mere consequence of the state of a cell, intermediary metabolism is now recognized as a key regulator of mammalian cell fate and function. In addition, cell metabolism is often disturbed in malignancies such as cancer, and targeting metabolic pathways can provide new therapeutic options. Cell metabolism is mostly studied in cell cultures in vitro, using techniques such as metabolomics, stable isotope tracing, and biochemical assays. Increasing evidence however shows that the metabolic profile of cells is highly dependent on the microenvironment, and metabolic vulnerabilities identified in vitro do not always translate to in vivo settings. Here, we provide a detailed protocol on how to perform in vivo stable isotope tracing in leukemia cells in mice, focusing on glutamine metabolism in acute myeloid leukemia (AML) cells. This method allows studying the metabolic profile of leukemia cells in their native bone marrow niche.

Keywords:  Cancer biology; Cell metabolism; Glutamine; Leukemia; Metabolic tracing; Mouse models

DOI:  https://doi.org/10.21769/BioProtoc.4171
Med Res Rev. 2021 Oct 31.

Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses.

Prema K Agarwala, Ritu Aneja, Shobhna Kapoor.

  Cancer cells display altered cellular lipid metabolism, including disruption in endogenous lipid synthesis, storage, and exogenous uptake for membrane biogenesis and functions. Altered lipid metabolism and, consequently, lipid composition impacts cellular function by affecting membrane structure and properties, such as fluidity, rigidity, membrane dynamics, and lateral organization. Herein, we provide an overview of lipid membranes and how their properties affect cellular functions. We also detail how the rewiring of lipid metabolism impacts the lipidomic landscape of cancer cell membranes and influences the characteristics of cancer cells. Furthermore, we discuss how the altered cancer lipidome provides cues for developing lipid-inspired innovative therapeutic and diagnostic strategies while improving our limited understanding of the role of lipids in cancer initiation and progression. We also present the arcade of membrane characterization techniques to cement their relevance in cancer diagnosis and monitoring of treatment response.

Keywords:  cancer; fluidity; lipid biomarkers; lipid membranes; lipid therapy; lipidomics

DOI:  https://doi.org/10.1002/med.21868
Anal Chem. 2021 Nov 02.

Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow.

Andrea Cerrato, Sara Elsa Aita, Anna Laura Capriotti, Chiara Cavaliere, Carmela Maria Montone, Susy Piovesana, Aldo Laganà.

High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CH3COO]-, [M + CD3COO]-, and [M - CH3]- coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.

DOI: https://doi.org/10.1021/acs.analchem.1c02944
Nat Methods. 2021 Nov;18(11): 1370-1376

HERMES: a molecular-formula-oriented method to target the metabolome.

Roger Giné, Jordi Capellades, Josep M Badia, Dennis Vughs, Michaela Schwaiger-Haber, Theodore Alexandrov, Maria Vinaixa, Andrea M Brunner, Gary J Patti, Oscar Yanes.

Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.

DOI: https://doi.org/10.1038/s41592-021-01307-z
Gastrointest Tumors. 2021 Oct;8(4): 169-176

The Potential Role of Changes in the Glucose and Lipid Metabolic Pathways in Gastrointestinal Cancer Progression: Strategy in Cancer Therapy.

Gordon A Ferns, Milad Shahini Shams Abadi, Ahmad Raeisi, Mohammad-Hassan Arjmand.

  Background: Changes in cell metabolism are a well-known feature of some cancers, and this may be involved in the etiology of tumor formation and progression, as well as tumor heterogeneity. These changes may affect fatty acid metabolism and glycolysis and are required to provide the increase in energy necessary for the high rate of proliferation of cancer cells. Gastrointestinal cancers remain a difficult-to-treat cancer, particularly as they are usually diagnosed at a late stage of disease and are associated with poor outcomes.Summary: Recently, the changes in the metabolic pathways, including the expression of the rate-limiting enzymes involved, have been considered to be a potential target for therapy for gastrointestinal tumors.
Key Message: A combination of routine chemotherapy drugs with metabolic inhibitors may improve the effectiveness of treatment.

Keywords:  Cancer metabolism; Gastrointestinal tumors; Glucose metabolism; Lipid metabolism

DOI:  https://doi.org/10.1159/000517771
Methods Mol Biol. 2022 ;2349 11-39

Fast Sampling of the Cellular Metabolome.

Walter M van Gulik, Andre B Canelas, Hilal Taymaz-Nikerel, Rutger D Douma, Lodewijk P de Jonge, Joseph J Heijnen.

  Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally, a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells and may interfere with the quantification of the endo metabolome. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, this release can be minimized by adaptation of the quenching method. For prokaryotic cells, this has not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction, for measurement of metabolites in total broth samples, washed cell samples, and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

Keywords:  Endometabolome; Exometabolome; Fast sampling; Isotope dilution mass spectrometry; Microbial metabolomics; Quenching

DOI:  https://doi.org/10.1007/978-1-0716-1585-0_2
Angew Chem Int Ed Engl. 2021 Nov 03.

Stable isotope phosphate labelling of diverse metabolites is enabled by a family of 18O-phosphoramidites.

Thomas M Haas, Stephan Mundinger, Danye Qiu, Nikolaus Jork, Kevin Ritter, Tobias Dürr-Mayer, Alexander Ripp, Adolfo Saiardi, Gabriel Schaaf, Henning Jacob Jessen.

  Stable isotope labelling is state-of-the-art in quantitative mass spectrometry, yet often accessing the required standards is cumbersome and very expensive. Here, a unifying synthetic concept for 18O-labelled phosphates is presented, based on a family of modified 18O2-phosphoramidite reagents. This toolbox offers access to major classes of biologically highly relevant phosphorylated metabolites as their isotopologues including nucleotides, inositol phosphates, -pyrophosphates, and inorganic polyphosphates. 18O-enrichment ratios >95% and good yields are obtained consistently in gram-scale reactions, while enabling late-stage labelling. We demonstrate the utility of the 18O labelled inositol phosphates and pyrophosphates by assignment of these metabolites from different biological matrices. We demonstrate that phosphate neutral loss is negligible in an analytical setup employing capillary electrophoresis electrospray ionization triple quadrupole mass spectrometry.

Keywords:  Capillary Electrophoresis; Mass spectrometry; Phosphorylation; Stable Isotope Labelling; nucleotides

DOI:  https://doi.org/10.1002/anie.202112457
Nat Commun. 2021 Nov 03. 12(1): 6339

Structural library and visualization of endogenously oxidized phosphatidylcholines using mass spectrometry-based techniques.

Yuta Matsuoka, Masatomo Takahashi, Yuki Sugiura, Yoshihiro Izumi, Kazuhiro Nishiyama, Motohiro Nishida, Makoto Suematsu, Takeshi Bamba, Ken-Ichi Yamada.

Although oxidized phosphatidylcholines (oxPCs) play critical roles in numerous pathological events, the type and production sites of endogenous oxPCs remain unknown because of the lack of structural information and dedicated analytical methods. Herein, a library of 465 oxPCs is constructed using high-resolution mass spectrometry-based non-targeted analytical methods and employed to detect 70 oxPCs in mice with acetaminophen-induced acute liver failure. We show that doubly oxygenated polyunsaturated fatty acid (PUFA)-PCs (PC PUFA;O2), containing epoxy and hydroxide groups, are generated in the early phase of liver injury. Hybridization with in-vivo 18O labeling and matrix-assisted laser desorption/ionization-tandem MS imaging reveals that PC PUFA;O2 are accumulated in cytochrome P450 2E1-expressing and glutathione-depleted hepatocytes, which are the major sites of liver injury. The developed library and visualization methodology should facilitate the characterization of specific lipid peroxidation events and enhance our understanding of their physiological and pathological significance in lipid peroxidation-related diseases.

DOI: https://doi.org/10.1038/s41467-021-26633-w
J Proteome Res. 2021 Nov 04.

Evaluation of Disposable Trap Column nanoLC-FAIMS-MS/MS for the Proteomic Analysis of FFPE Tissue.

Stephan Eckert, Yun-Chien Chang, Florian P Bayer, Matthew The, Peer-Hendrik Kuhn, Wilko Weichert, Bernhard Kuster.

  Proteomic biomarker discovery using formalin-fixed paraffin-embedded (FFPE) tissue requires robust workflows to support the analysis of large cohorts of patient samples. It also requires finding a reasonable balance between achieving a high proteomic depth and limiting the overall analysis time. To this end, we evaluated the merits of online coupling of single-use disposable trap column nanoflow liquid chromatography, high-field asymmetric-waveform ion-mobility spectrometry (FAIMS), and tandem mass spectrometry (nLC-FAIMS-MS/MS). The data show that ≤600 ng of peptide digest should be loaded onto the chromatographic part of the system. Careful characterization of the FAIMS settings enabled the choice of optimal combinations of compensation voltages (CVs) as a function of the employed LC gradient time. We found nLC-FAIMS-MS/MS to be on par with StageTip-based off-line basic pH reversed-phase fractionation in terms of proteomic depth and reproducibility of protein quantification (coefficient of variation ≤15% for 90% of all proteins) but requiring 50% less sample and substantially reducing sample handling. Using FFPE materials from the lymph node, lung, and prostate tissue as examples, we show that nLC-FAIMS-MS/MS can identify 5000-6000 proteins from the respective tissue within a total of 3 h of analysis time.

Keywords:  FFPE analysis; LC−FAIMS−MS/MS; clinical proteomics; high-field asymmetric waveform ion mobility spectrometry (FAIMS); quantitative proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.1c00695
Mol Cell Proteomics. 2021 Nov 01. pii: S1535-9476(21)00143-2. [Epub ahead of print] 100171

Deep learning-derived evaluation metrics enable effective benchmarking of computational tools for phosphopeptide identification.

Wen Jiang, Bo Wen, Kai Li, Wen-Feng Zeng, Felipe da Veiga Leprevost, Jamie Moon, Vladislav A Petyuk, Nathan J Edwards, Tao Liu, Alexey I Nesvizhskii, Bing Zhang.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson's correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

DOI: https://doi.org/10.1016/j.mcpro.2021.100171
Clin Chem Lab Med. 2021 Nov 02.

Comparison of two LC-MS/MS methods for the quantification of 24,25-dihydroxyvitamin D3 in patients and external quality assurance samples.

Sieglinde Zelzer, Caroline Le Goff, Stéphanie Peeters, Chiara Calaprice, Andreas Meinitzer, Dietmar Enko, Walter Goessler, Markus Herrmann, Etienne Cavalier.

  OBJECTIVES: In-house developed liquid-chromatography mass spectrometry (LC-MS/MS) methods are used more and more frequently for the simultaneous quantification of vitamin D metabolites. Among these, 24,25-dihydroxyvitamin D3 (24,25(OH)2D3) is of clinical interest. This study assessed the agreement of this metabolite in two validated in-house LC-MS/MS methods.METHODS: 24,25(OH)2D3 was measured in 20 samples from the vitamin D external quality assurance (DEQAS) program and in a mixed cohort of hospital patients samples (n=195) with the LC-MS/MS method at the Medical University of Graz (LC-MS/MS 1) and at the University of Liège (LC-MS/MS 2).
RESULTS: In DEQAS samples, 24,25(OH)2D3 results with LC-MS/MS 1 had a proportional bias of 1.0% and a negative systemic difference of -0.05%. LC-MS/MS 2 also showed a proportional bias of 1.0% and the negative systemic bias was -0.22%. Comparing the EQA samples with both methods, no systemic bias was found (0.0%) and the slope was 1%. The mean difference of 195 serum sample measurements between the two LC-MS/MS methods was minimal (-0.2%). Both LC-MS/MS methods showed a constant bias of 0.31 nmol/L and a positive proportional bias of 0.90%, respectively.
CONCLUSIONS: This study is the first to assess the comparability of 24,25(OH)2D3 concentrations in a mixed cohort of hospitalized patients with two fully validated in-house LC-MS/MS methods. Despite different sample preparation, chromatographic separation and ionization, both methods showed high precision measurements of 24,25(OH)2D3. Furthermore, we demonstrate the improvement of accuracy and precision measurements of 24,25(OH)2D3 in serum samples and in the DEQAS program.

Keywords:  24,25-dihydroxyvitamin D (24,25(OH)2D); liquid-chromatography mass spectrometry (LC-MS/MS); method comparison; patient samples; vitamin D external quality assurance scheme (DEQAS)

DOI:  https://doi.org/10.1515/cclm-2021-0792
J Proteome Res. 2021 Nov 04.

Development and Provisional Validation of a Multiplex LC-MRM-MS Test for Timely Kidney Injury Detection in Urine.

Tirsa T van Duijl, L Renee Ruhaak, Nico P M Smit, Mervin M Pieterse, Fred P H T M Romijn, Natasja Dolezal, Jan Wouter Drijfhout, Johan W de Fijter, Christa M Cobbaert.

  Kidney injury is a complication frequently encountered in hospitalized patients. Early detection of kidney injury prior to loss of renal function is an unmet clinical need that should be targeted by a protein-based biomarker panel. In this study, we aim to quantitate urinary kidney injury biomarkers at the picomolar to nanomolar level by liquid chromatography coupled to tandem mass spectrometry in multiple reaction monitoring mode (LC-MRM-MS). Proteins were immunocaptured from urinary samples, denatured, reduced, alkylated, and digested into peptides before LC-MRM-MS analysis. Stable-isotope-labeled peptides functioned as internal standards, and biomarker concentrations were attained by an external calibration strategy. The method was evaluated for selectivity, carryover, matrix effects, linearity, and imprecision. The LC-MRM-MS method enabled the quantitation of KIM-1, NGAL, TIMP2, IGFBP7, CXCL9, nephrin, and SLC22A2 and the detection of TGF-β1, cubilin, and uromodulin. Two to three peptides were included per protein, and three transitions were monitored per peptide for analytical selectivity. The analytical carryover was <1%, and minimal urine matrix effects were observed by combining immunocapture and targeted LC-MRM-MS analysis. The average total CV of all quantifier peptides was 26%. The linear measurement range was determined per measurand and found to be 0.05-30 nmol/L. The targeted MS-based method enables the multiplex quantitation of low-abundance urinary kidney injury biomarkers for future clinical evaluation.

Keywords:  LC-MRM-MS; kidney injury; low-abundance biomarkers; protein markers; quantitative bottom-up proteomics; urine

DOI:  https://doi.org/10.1021/acs.jproteome.1c00532
Anal Chem. 2021 Nov 04.

SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS.

Alexis Delabriere, Philipp Warmer, Vincenth Brennsteiner, Nicola Zamboni.

Metabolomics has been shown to be promising for diverse applications in basic, applied, and clinical research. These applications often require large-scale data, and while the technology to perform such experiments exists, downstream analysis remains challenging. Different tools exist in a variety of ecosystems, but they often do not scale to large data and are not integrated into a single coherent workflow. Moreover, the outcome of processing is very sensitive to a multitude of algorithmic parameters. Hence, parameter optimization is not only critical but also challenging. We present SLAW, a scalable and yet easy-to-use workflow for processing untargeted LC-MS data in metabolomics and lipidomics. The capabilities of SLAW include (1) state-of-the-art peak-picking algorithms, (2) a new automated parameter optimization routine, (3) an efficient sample alignment procedure, (4) gap filling by data recursion, and (5) the extraction of consolidated MS2 and an isotopic pattern across all samples. Importantly, both the workflow and the parameter optimization were designed for robust analysis of untargeted studies with thousands of individual LC-MSn runs. We compared SLAW to two state-of-the-art workflows based on openMS and XCMS. SLAW was able to detect and align more reproducible features in all data sets considered. SLAW scaled well, and its analysis of a data set with 2500 LC-MS files consumed 40% less memory and was 6 times faster than that using the XCMS-based workflow. SLAW also extracted 2-fold more isotopic patterns and MS2 spectra, which in 60% of the cases led to positive matches against a spectral library.

DOI: https://doi.org/10.1021/acs.analchem.1c02687
Nucleic Acids Res. 2021 Nov 01. pii: gkab1038. [Epub ahead of print]

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.

Yasset Perez-Riverol, Jingwen Bai, Chakradhar Bandla, David García-Seisdedos, Suresh Hewapathirana, Selvakumar Kamatchinathan, Deepti J Kundu, Ananth Prakash, Anika Frericks-Zipper, Martin Eisenacher, Mathias Walzer, Shengbo Wang, Alvis Brazma, Juan Antonio Vizcaíno.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

DOI: https://doi.org/10.1093/nar/gkab1038
J Proteome Res. 2021 Nov 04.

ClusterSheep: A Graphics Processing Unit-Accelerated Software Tool for Large-Scale Clustering of Tandem Mass Spectra from Shotgun Proteomics.

Paul Ka Po To, Long Wu, Chak Ming Chan, Ayman Hoque, Henry Lam.

  Modern shotgun proteomics experiments generate gigabytes of spectra every hour, only a fraction of which were utilized to form biological conclusions. Instead of being stored as flat files in public data repositories, this large amount of data can be better organized to facilitate data reuse. Clustering these spectra by similarity can be helpful in building high-quality spectral libraries, correcting identification errors, and highlighting frequently observed but unidentified spectra. However, large-scale clustering is time-consuming. Here, we present ClusterSheep, a method utilizing Graphics Processing Units (GPUs) to accelerate the process. Unlike previously proposed algorithms for this purpose, our method performs true pairwise comparison of all spectra within a precursor mass-to-charge ratio tolerance, thereby preserving the full cluster structures. ClusterSheep was benchmarked against previously reported clustering tools, MS-Cluster, MaRaCluster, and msCRUSH. The software tool also functions as an interactive visualization tool with a persistent state, enabling the user to explore the resulting clusters visually and retrieve the clustering results as desired.

Keywords:  GPU; computational proteomics; spectral archives; spectral libraries; spectrum clustering

DOI:  https://doi.org/10.1021/acs.jproteome.1c00485