High-quality Mass Spectrometry Research Articles

DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.

The Current State-of-the-Art Identification of Unknown Proteins Using Mass Spectrometry Exemplified on De Novo Sequencing of a Venom Protease from Bothrops moojeni.

(1) Background: The amino acid sequence elucidation of peptides from the gas phase fragmentation mass spectra, de novo sequencing, is a valuable method for the identification of unknown proteins complementary to Edman sequencing. It is increasingly used in shot-gun mass spectrometry (MS)-based proteomics experiments. We review the current state-of-the-art and use the identification of an unknown snake venom protein targeting the human tissue factor (TF) as an example to describe the analysis process based on manual spectrum interrogation. (2) Methods: The immobilized TF was incubated with a crude B. moojeni venom solution. The potential binding partners were eluted and further purified by gel electrophoresis. Edman degradation was performed to elucidate the N-terminus of the 31 kDa protein of interest. High-resolution MS with collision-induced dissociation was employed to generate peptide fragmentation spectra. Sequence tags were deduced and used for searches in the NCBI and Uniprot databases. Protein matches from the snake species were further validated by target MS/MS. (3) Results: Sequence tag D [K/Q] D [I/L] VDD [K/Q] led to a snake venom serine protease (SVSP) from lancehead B. jararaca (P81824). With target MS/MS, 24% of the SVSP sequence were confirmed; an additional 41% were tentatively assigned by data-independent MS. Edman sequencing provided information for 10 N-terminal amino acid residues, also confirming the match to SVSP. (4) Conclusions: The identification of unknown proteins continues to be a challenge despite major advances in MS instrumentation and bioinformatic tools. The main requirement is the generation of meaningful, high-quality MS peptide fragmentation spectra. These are used to elucidate sufficiently long sequence tags, which can subsequently be submitted to searches in protein databases. This basic method does not require extensive bioinformatics because peptide MS/MS spectra, especially of doubly-charged ions, can be analysed manually. We demonstrated the procedure with the elucidation of SVSP. While de novo sequencing quickly indicates the correct protein group, the validation of the entire protein sequence of amino acid-by-amino acid will take time. Reasons are the need to properly assign isobaric amino acid residues and modifications. With the ongoing efforts in genomics and transcriptomics and the availability of ever more data in public databases, the need for de novo MS sequencing will decrease. Still, not every animal and plant species will be sequenced, so the combination of MS and Edman sequencing will continue to be of importance for the identification of unknown proteins.

Recent advances in glycopeptide enrichment and mass spectrometry data interpretation approaches for glycoproteomics analyses

蛋白质糖基化是生物体内最重要的翻译后修饰之一,在蛋白质稳定性、细胞内和细胞间信号转导、激素活化或失活和免疫调节等生理过程和病理进程中发挥重要作用。而异常的蛋白质糖基化往往和多种疾病的发生发展密切相关,目前应用于临床检测的多种肿瘤生物标志物大多属于糖蛋白或者糖抗原。因此在组学层次系统分析蛋白质糖基化的变化对阐明生物体内糖基化修饰的调控机理和发现新型疾病标志物都非常重要。基于质谱的蛋白质组学技术为全面分析蛋白质及其修饰提供了有效的分析手段。在自下而上的蛋白质组学研究中,由于完整糖基化肽段同时存在性质各异的肽段骨架和糖链结构、糖肽的相对丰度和离子化效率较低以及糖基化修饰有高度异质性等特点,完整糖肽的分析比其他翻译后修饰更加困难。近年来,为了更全面、系统地分析蛋白质糖基化,研究人员发展了一些新技术,包括完整糖肽的富集技术、质谱的碎裂模式和数据采集模式、质谱数据的解析方法和定量策略等等,大力推进了该领域的研究水平,也为研究蛋白质糖基化相关的生物标志物提供了技术支持。该篇综述主要关注近年来基于质谱的糖蛋白质组学研究中的新进展,重点介绍针对完整N-和O-糖基化肽段的富集新技术和谱图解析新方法,并讨论其在肿瘤早期诊断方面的应用潜力。

Abstract 3383: EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles

Abstract Background: Accurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS)profiling of eluted HLA ligands, which provides unbiased, high-throughput measurements of HLA associated peptides resulting from in vivo cellular processing, can be a highly valuable training set to build predictive models of HLA binding. In addition, gene expression profiles measured by RNA-seq data in a specific cell type could significantly improve the positive predictive value (PPV) of epitope presentation prediction. Although large amount of high-quality mass spectrometry data of HLA-bound peptides is being generated in the last few years, few of them provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. Here, we aim to develop a publicly available prediction tool incorporating both sources of information, and demonstrate its superior performance over existing methods. Methods: We obtained public HLA peptidome datasets with matching RNA-seq data of twelve cell lines derived from multiple tissues. We used these MS HLA ligand data to build Position Score Specific Matrixes (PSSMs) for five HLA-I alleles across these cell lines. We then used logistic regression to model the relationship among PSSM score, gene expression, peptide length distribution and whether the peptide could be presented in each of the twelve cell lines, and compared the feature weights among them. Results: We found that the feature weights across different HLA-I alleles and cell lines were close to each other, suggesting that there is a universal relationship between PSSM score and gene expression across different cell lines that could be applied to epitope presentation prediction for multiple alleles in diverse tissues. When we replaced the cell-line-specific weights with universal weights summarized from all the cell lines, the logistic regression model’s predicted power for each cell line only dropped slightly and still substantially outperformed predictions based on PSSM scores alone. Based on such a finding, we applied the universal feature weights to more than 180,000 unique HLA ligands collected from public HLA peptidomics datasets, and presented an Epitope Presentation Integrated prediCtion (EPIC) model for 66 HLA alleles. EPIC was substantially better than other popular methods, including MixMHCpred, NetMHCpan (v4.0), and MHCflurry, when evaluated on independent HLA eluted ligand datasets, with an average 0.1%PPV of 53.58%, compared to 40.50%, 40.20%, 29.81%, and 25.57% achieved by MixMHCpred, NetMHCpan (EL), NetMHCpan (BA), and MHCflurry, respectively. Conclusion: By integrating MS and expression data, EPIC is superior to currently available methods in predicting epitope presentation for the 66 common HLA alleles that our models were built on. Citation Format: Weipeng Hu, Si Qiu, Youping Li, Geng Liu, Xiuqing Zhang, Leo J Lee. EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3383.

Computational Approaches for Understanding High Quality Mass Spectrometry Proteomic Data

Computational approach of proteomic data science is used to identification and quantification of protein and provides the high-throughput data, concentration changes, interactions, posttranslational modifications and cellular localizations. The high-quality mass spectrometry recall to understanding the different sources of unsigned high-quality spectra features. The iterative computational method is interrogating the high efficiency of mass spectrometry protein data. The approach contains several databases searching with different search parameters, spectral library searching, modified peptides using blind search and genomic database searching. The mass spectrometry computational method is analysis the proteomics data focusing the key concepts with explanations, mass spectral feature detection, identifying the peptides, protein inference and control the false discovery rate. Then the method discusses the quantification of peptides and proteins, the downstream data analysis on machine learning, network analysis and multiomics integration of protein data and finally discuss the future of computational proteomics data.

Detection of Unknown Chemical Adduct Modifications on Proteins: From Wet to Dry Laboratory.

The detection and characterization of chemical adducts on proteins is of increasing interest. Here, we described a step-by-step procedure to identify unknown chemical adduct modifications on proteins resulting from the interaction with a given reactive compound. The protocol can be divided into two equally important parts: (1) the wet laboratory work, to produce high quality mass spectrometry (MS) data of in vitro modified proteins and (2) the dry laboratory work, to analyze the generated MS data and provide highly confident qualitative and quantitative results on the chemical composition and amino acid localization of adducts. This protocol is applicable to the study of any pharmaceutical or chemical compound forming covalent protein adducts, detectable in LC-MS/MS experiments.

Sample preparation for mass spectrometry imaging of leaf tissues: a case study on analyte delocalization.

Appropriate sample preparation is pivotally important to obtain high-quality mass spectrometry imaging (MSI) data. Unlike mammalian tissues, preparation of cryosections from plant tissues for MSI measurement is quite challenging due to its intrinsic complex texture and cellular structure. This is especially true for leaf samples which are generally thin, water-rich, and fragile. In this work, a systematic study was performed, aiming to evaluate three embedding materials and five mounting approaches for matrix-assisted laser desorption ionization (MALDI) MSI of secondary metabolites in cross sections of the ginkgo leaf. Delocalization of endogenous metabolites was chosen as a major indicator for evaluation of three embedding materials including ice, carboxymethyl cellulose (CMC), and gelatin and different mounting approaches. Image distortion and analyte delocalization were observed when ice was used as an embedding medium. CMC embedding provided better results compared to the ice by using modified mounting approach. Among three embedding materials, no delocalization was observed in specimens embedded with gelatin, and gelatin embedding is the least affected by different mounting approaches. An alternative approach to mitigate analyte delocalization is the removal of embedding media embraced the tissue sections before mounting, which is particularly suitable for ice-embedded samples. Additionally, the extent of analyte delocalization was closely related to their lipophilicity/hydrophilicity properties, and less analyte diffusion was observed for hydrophobic analytes than for the water-soluble compounds.

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

Strategy for Determination of β 1-Adrenergic Receptor Phosphorylation State In Vivo

β1-adrenergic receptor (Adrb1), a member of the G-protein coupled receptor (GPCR) superfamily, is a critical regulator of heart function. All GPCRs are phosphorylated at multiple sites and the specific pattern of phosphorylation acts as a “barcode” to regulate receptor function and downstream physiological processes in a tissue-specific manner. However, little is known about the location and function of Adrb1 phosphorylation sites in vivo due to the lack of specific antibodies. As a first step to identify the phosphorylation states of Adrb1 and associated functions in the in vivo mouse heart, we developed the following experimental strategy: 1) identification of agonist-dependent Adrb1 phosphorylation sites in isolated perfused mouse heart using advanced phosphoproteomics techniques; 2) definitive assignment of these phosphorylation sites by high-quality mass spectrometry (MS) data obtained from Adrb1- overexpressing HEK 293T cells; 3) generation of knock-in (KI) Mice expressing Adrb1 fused with FLAG-tag at the N-terminus for immunoaffinity purification to reveal phosphorylation status within the living organism; 4) elucidation of phosphorylation levels at specific sites of Adrb1 in KI mouse heart by MS measures of phosphorylated peptide to corresponding unphosphorylated peptide ion intensity ratios. Using this strategy, we identified Ser462 at the C-terminus of Adrb1 as an agonist-dependent phosphorylation site in the perfused mouse heart. We also revealed the basal phosphorylation ratios at Ser274 (0.25), Ser417 (0.55) and Ser462 (0.0023) in KI Mice. These findings provide novel insights into the regulatory mechanisms of Adrb1 function mediated by site-specific phosphorylation.

Application of direct analysis in real time-orbitrap mass spectrometry combined with multivariate data analysis for rapid quality assessment of Yuanhu Zhitong Tablet

Direct analysis in real time (DART)-orbitrap mass spectrometry (MS) combined with multivariate data analysis was applied to perform quality control for Yuanhu Zhitong Tablet (YZT). The ionization gas temperature of DART and extraction solvent were optimized to obtain high-quality MS spectra. Based on DART-MS data, the quantification of tetrahydropalmatine was conducted. Moreover, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to investigate the differences of samples from five manufactories. The samples were classified into three groups, although the content of tetrahydropalmatine as a commonly used analytical marker showed no significant differences among the samples. Eleven ions were selected as analytical markers via multivariate and univariate data analysis, and the intensities of these ions in different samples were investigated. The results demonstrated the complementary function of these ions to conventional quality control component tetrahydropalmatine. This study exhibited the promising potential of DART-MS in quality control of YHZ and could provide us a new option in the rapid quality assessment of traditional Chinese herbal medicine (TCHM) and traditional Chinese medicine preparation (TCMP).

The PeptideAtlas of the Domestic Laying Hen.

Proteomics-based biological research is greatly expanded by high-quality mass spectrometry studies, which are themselves enabled by access to quality mass spectrometry resources, such as high-quality curated proteome data repositories. We present a PeptideAtlas for the domestic chicken, containing an extensive and robust collection of chicken tissue and plasma samples with substantial value for the chicken proteomics community for protein validation and design of downstream targeted proteome quantitation. The chicken PeptideAtlas contains 6646 canonical proteins at a protein FDR of 1.3%, derived from ∼100 000 peptides at a peptide level FDR of 0.1%. The rich collection of readily accessible data is easily mined for the purposes of data validation and experimental planning, particularly in the realm of developing proteome quantitation workflows. Herein we demonstrate the use of the atlas to mine information on common chicken acute-phase proteins and biomarkers for cancer detection research, as well as their localization and polymorphisms. This wealth of information will support future proteome-based research using this highly important agricultural organism in pursuit of both chicken and human health outcomes.

A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes.

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

Multiple heart-cutting two dimensional liquid chromatography mass spectrometry: Towards real time determination of related impurities of bio-pharmaceuticals in salt based separation methods

Many of the chromatographic methods used in industry to determine related impurities in bio pharmaceuticals employ salt containing mobile phases. “Salty” mobile phases often provide superior chromatographic performance but are not compatible with mass spectrometry (MS) detection. Peak tracking necessary for method development is therefore often based on peak areas and the chemist’s experience/intuition. In addition, MS characterization of impurities usually is done by offline fraction collection, which apart from being time consuming often suffers from poor recovery or the degradation of impurities collected. The recent development of multiple heart-cutting (MHC) two-dimensional liquid chromatography (2D-LC) provides a way to address these problems. This study shows how MHC 2D-LC–MS can be used to obtain almost real time MS data for bovine insulin related impurities present at low level (<<0.03%). High quality MS spectra were obtained even for a first dimension using a mobile phase containing high concentrations of sodium, sulphate and phosphate. Thereby MHC 2D-LC–MS offers a possibility to eliminate the guesswork currently associated with peak tracking during method development. Furthermore, in contrast to current characterization methods involving fraction collection, solvent reduction/exchange etc., MS determination is done directly, which markedly shortens the workflow (from days to hours) and reduces the risk for poor recovery and degradation.

Improvement of Mitochondria Extract from Saccharomyces cerevisiae Characterization in Shotgun Proteomics Using Sheathless Capillary Electrophoresis Coupled to Tandem Mass Spectrometry.

In this work, we describe the characterization of a quantity-limited sample (100 ng) of yeast mitochondria by shotgun bottom-up proteomics. Sample characterization was carried out by sheathless capillary electrophoresis, equipped with a high sensitivity porous tip and coupled to tandem mass spectrometry (CESI-MS-MS) and concomitantly with a state-of-art nano flow liquid chromatography coupled to a similar mass spectrometry (MS) system (nanoLC-MS-MS). With single injections, both nanoLC-MS-MS and CESI-MS-MS 60 min-long separation experiments allowed us to identify 271 proteins (976 unique peptides) and 300 proteins (1,765 unique peptides) respectively, demonstrating a significant specificity and complementarity in identification depending on the physicochemical separation employed. Such complementary, maximizing the number of analytes detected, presents a powerful tool to deepen a biological sample's proteomic characterization. A comprehensive study of the specificity provided by each separating technique was also performed using the different properties of the identified peptides: molecular weight, mass-to-charge ratio (m/z), isoelectric point (pI), sequence coverage or MS-MS spectral quality enabled to determine the contribution of each separation. For example, CESI-MS-MS enables to identify larger peptides and eases the detection of those having extreme pI without impairing spectral quality. The addition of peptides, and therefore proteins identified by both techniques allowed us to increase significantly the sequence coverages and then the confidence of characterization. In this study, we also demonstrated that the two yeast enolase isoenzymes were both characterized in the CESI-MS-MS data set. The observation of discriminant proteotypic peptides is facilitated when a high number of precursors with high-quality MS-MS spectra are generated.

Minimizing technical variation during sample preparation prior to label-free quantitative mass spectrometry

Sample preparation is the crucial starting point to obtain high-quality mass spectrometry data and can be divided into two main steps in a bottom-up proteomics approach: cell/tissue lysis with or without detergents and a(n) (in-solution) digest comprising denaturation, reduction, alkylation, and digesting of the proteins. Here, some important considerations, among others, are that the reagents used for sample preparation can inhibit the digestion enzyme (e.g., 0.1% sodium dodecyl sulfate [SDS] and 0.5 M guanidine HCl), give rise to ion suppression (e.g., polyethylene glycol [PEG]), be incompatible with liquid chromatography–tandem mass spectrometry (LC–MS/MS) (e.g., SDS), and can induce additional modifications (e.g., urea). Taken together, all of these irreproducible effects are gradually becoming a problem when label-free quantitation of the samples is envisioned such as during the increasingly popular high-definition mass spectrometry (HDMSE) and sequential window acquisition of all theoretical fragment ion spectra (SWATH) data-independent acquisition strategies. Here, we describe the detailed validation of a reproducible method with sufficient protein yield for sample preparation without any known LC–MS/MS interfering substances by using 1% sodium deoxycholate (SDC) during both cell lysis and in-solution digest.

Keeping pace with NPS releases: fast GC‐MS screening of legal high products

The continuous appearance of novel psychoactive substances (NPS) in legal high products presents a challenge for the routine analytical laboratory. A rapid screening method for NPS analysis using fast gas chromatography mass spectrometry (fast GC-MS) is presented. Twenty-three analytes, including 5-iodo-2-aminoindane (5-IAI), 1-(thiophen-2-yl)-2-methylaminopropane (MPA), 1-benzylpiperazine (BZP), 4-methylmethcathinone (mephedrone), 5,6-methylenedioxy-2-aminoindane (MDAI) and methoxetamine (MXE) were separated within 4 min. The method was used to analyze 35 Internet and head shop purchased 'legal high' products with the successful identification of their active ingredients. As previously observed, legal high products do not always contain their stated ingredients. Of the group of products purchased as 5-IAI not one contained 5-IAI with several containing mixtures of substances either already controlled in the UK or under consideration by the Advisory Council on Misuse of Drugs (ACMD). The low bleed and high inertness of the chromatography column used ensured clean high quality mass spectrometry data which when combined with the short run time resulted in an efficient tool for NPS screening, even when standards were unavailable. Electron impact and chemical ionization mass spectra used in combination for the identification of unknown NPS are presented.

Self-sustained carbon monoxide oxidation oscillations on size-selected platinum nanoparticles at atmospheric pressure

High-quality mass spectrometry data of the oscillatory behavior of CO oxidation on SiO(2) supported Pt-nanoparticles at atmospheric pressure have been acquired as a function of pressure, coverage, gas composition and nanoparticle size. The oscillations are self-sustained for several days at constant temperature, pressure and CO/O(2) ratio. The frequency of the oscillations is very well defined and increases over time. The oscillation frequency is furthermore strongly temperature dependent with increasing temperature resulting in increasing frequency. A plausible mechanism for the oscillations is proposed based on an oxidation-reduction cycle of the nanoparticles which change the rate of CO oxidation on the particles.

Efficient Subfractionation of Gram-Negative Bacteria for Proteomics Studies

Proteomics studies of pathogenic bacteria are an important basis for biomarker discovery and for the development of antimicrobial drugs and vaccines. Especially where vaccines are concerned, it is of great interest to explore which bacterial factors are exposed on the bacterial cell surface and thus can be directly accessed by the immune system. One crucial step in proteomics studies of bacteria is an efficient subfractionation of their cellular compartments. We set out to compare and improve different protocols for the fractionation of proteins from Gram-negative bacteria into outer membrane, cytoplasmic membrane, periplasmic, and cytosolic fractions, with a focus on the outer membrane. Overall, five methods were compared, three methods for the fast isolation of outer membrane proteins and two methods for the fractionation of each cellular compartment, using Escherichia coli BL21 as a model organism. Proteins from the different fractions were prepared for further mass spectrometric analysis by SDS gel electrophoresis and consecutive in-gel tryptic digestion. Most published subfractionation protocols were not explicitly developed for proteomics applications. Thus, we evaluated not only the separation quality of the five methods but also the suitability of the samples for mass spectrometric analysis. We could obtain high quality mass spectrometry data from one-dimensional SDS-PAGE, which greatly reduces experimental time and sample amount compared to two-dimensional electrophoresis methods. We then applied the most specific fractionation technique to different Gram-negative pathogens, showing that it is efficient in separating the subcellular proteomes independent of the species and that it is capable of producing high-quality proteomics data in electrospray ionization mass spectrometry.

Evaluation for computational platforms of LC-MS based label-free quantitative proteomics: A global view

Label-free shotgun proteomics is a promising semi-quantitative protein profi ling method with capability of comparing a large number of samples in a single experiment. One of the key challenges in this proteomics approach is the high requirement of computational capability for tasks such as feature detection and LC-MS alignment due to the complexity of proteomics systems. Many software tools have been developed in recent years to aid these processes, yet it is often not clear to users whether these tools extract information from raw data correctly and comprehensively. In this paper, we described a comprehensive procedure to provide a fast and global view for performances of LC-MS label-free computational software. Two high quality mass spectrometry datasets with carefully controlled QC samples and spikedin proteins were also provided as benchmark datasets for such evaluations.