Comparison of data acquisition strategies on quadrupole ion trap instrumentation for shotgun proteomics

Rapid assay development for low input targeted proteomics using a versatile linear ion trap

Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4+ and CD8+ T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.

A workflow for targeted proteomics assay development using a versatile linear ion trap

Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopeDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation

Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.

Comparative assessment and novel strategy on methods for imputing proteomics data

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy—convex analysis of mixtures—for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.

DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies

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

Proteomics of host-bacterial interactions: new insights from dual perspectives

Mass-spectrometry (MS)-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and MS-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and (or) release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and (or) network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of MS-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen), and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Peptide variability and signatures associated with disease progression in CSF collected longitudinally from ALS patients

We employ shotgun proteomics and data-independent acquisition (DIA) mass spectrometry to analyze cerebrospinal fluid longitudinally collected from 14 amyotrophic lateral sclerosis (ALS) patients (8 males and 6 females). We perform three main analyses of these data: (1) examine the intra- and inter-patient protein variability in CSF; (2) explore the association of inflammation with rate of disease progression; and (3) develop a mixed-effects model to best explain the decrease in ALS-Functional Rating Scale (ALS-FRS) score. Overall, the CSF protein abundances are tightly regulated with the intra-individual variability contributing just 4% to the overall variance. In four patients, a moderately significant correlation (p < 0.1) was observed between inflammation and rate of disease progression. Using a least absolute shrinkage and selection operator (LASSO) variable selection, we selected 55 viable peptides for mathematical modeling via a linear mixed-effects regression. We then employed forward selection to generate a final model by minimizing Akaike's information criterion (AIC). The final model utilized changes in abundance from 28 peptides as fixed effects to model progression of the disease in these patients. These peptides were from proteins involved in stress response and innate immunity. Graphical abstract.

Quantitative Proteomic Analysis of MARC-145 Cells Infected with a Mexican Porcine Reproductive and Respiratory Syndrome Virus Strain Using a Label-Free Based DIA approach

Porcine reproductive and respiratory syndrome (PRRS) is an infectious disease characterized by severe reproductive failure in sows, acute respiratory disorders in growing pigs, and high mortality in piglets. The causative agent of this syndrome is the PRRS virus (PRRSV), an RNA virus belonging to the Arteriviridae family. To date, several quantitative approaches of proteomics have been applied to analyze the gene expression profiles during PRRSV infection in PAMs and MARC-145 cells, and few proteins have been consistent among independent studies, probably due to the differences in the levels of virulence of different PRRSV strains used and/or due to analytical conditions. In this study, total proteins isolated from noninfected and infected MARC-145 cells with a Mexican PRRSV strain were relatively quantified using label-free based DIA approach in combination with ion-mobility separation. As a result, 1456 quantified proteins were found to be shared between the control and infected samples. Afterward, these proteins were filtered, and 699 of them were considered without change. Also, 17 proteins were up-regulated and 19 proteins were down-regulated during the PRSSV infection. Bioinformatic analysis revealed that many of the differentially expressed proteins are involved in processes like antigen processing, presentation of antigens, response to viruses, response to IFNs, and innate immune response, among others. The present work is the first one which provides a detailed proteomic analysis through label-free based DIA approach in MARC-145 cells during the infection with a Mexican PRRSV strain.

Comparison of data acquisition modes with Orbitrap high-resolution mass spectrometry for targeted and non-targeted residue screening in aquacultured eel

RATIONALE

A current trend in monitoring chemical contaminants in animal products is to use high-resolution mass spectrometry (HRMS). In this study, several HRMS data acquistion modes using Orbitrap MS for simultaneous full-scan MS in combination with MS2 analysis were evaulated for their effectiveness in detecting and identifying both targeted and non-targeted veterinary drug residues in aquacultured eel samples.

METHODS

Sample preparation consisted of an acidic acetonitrile extraction with solid-phase extraction cleanup for analysis using LC/HRMS. Different data acquisition methods, including full-scan MS with non-targeted all ion fragmentation (AIF), multiplexed or variable data-independent analysis (mDIA or vDIA), targeted data-dependent MS2 (DDMS2), and parallel reaction monitoring (PRM) acquisition, were explored. The methods were evaluated with fortified eel tissue and imported eel samples to determine how many analytes could be detected and identified.

RESULTS

For non-targeted data acquisition, the number of analytes detected using DIA methods matched the results obtained by AIF, but the resulting product ion scans were more diagnostic because characteristic ions were predominant in the DIA MS2 spectra. In targeted analysis for a limited list of 68 compounds, full-scan MS followed by PRM was advantageous compared with DDMS2 because high-quality MS2 spectra were generated for almost all the analytes at target testing levels.

CONCLUSIONS

For residue screening, AIF has fast MS1 scan speed with adequate detection of product ions but may lead to false positive findings. DIA methods are better suited to monitor for both targeted and non-targeted compounds because they generate more characteristic MS2 spectra for retrospective library searching. For follow-up targeted analysis, PRM is prefered over DDMS2 when searching for a limited set of compounds.

Site-specific N-glycosylation of HeLa cell glycoproteins

We have characterized site-specific N-glycosylation of the HeLa cell line glycoproteins, using a complex workflow based on high and low energy tandem mass spectrometry of glycopeptides. The objective was to obtain highly reliable data on common glycoforms, so rigorous data evaluation was performed. The analysis revealed the presence of a high amount of bovine serum contaminants originating from the cell culture media - nearly 50% of all glycans were of bovine origin. Unaccounted, the presence of bovine serum components causes major bias in the human cellular glycosylation pattern; as is shown when literature results using released glycan analysis are compared. We have reliably identified 43 (human) glycoproteins, 69 N-glycosylation sites, and 178 glycoforms. HeLa glycoproteins were found to be highly (68.7%) fucosylated. A medium degree of sialylation was observed, on average 46.8% of possible sialylation sites were occupied. High-mannose sugars were expressed in large amounts, as expected in the case of a cancer cell line. Glycosylation in HeLa cells is highly variable. It is markedly different not only on various proteins but also at the different glycosylation sites of the same protein. Our method enabled the detailed characterization of site-specific N-glycosylation of several glycoproteins expressed in HeLa cell line.

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows

A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.

Comparing SRM and SWATH Methods for Quantitation of Bovine Muscle Proteomes

Mass spectrometry (MS) has become essential for efficient and accurate quantification of proteins and proteomes and, thus, a key technology throughout all biosciences. However, validated MS methods are still scarce for meat quality research applications. The objective of this work was to develop and compare two targeted proteomic methods, namely, selected reaction monitoring (SRM) and sequential window acquisition of all theoretical spectra (SWATH), for the quantification of 11 bovine muscle proteins that may be indicators of meat color. Both methods require evaluation of spectra from proteotypic and quantotypic peptides, and we here report our evaluation of which peptides and MS parameters are best suited for robust quantification of these 11 proteins. We observed that the SRM approach provides better reproducibility, linearity, and sensitivity than SWATH and is therefore ideal for targeted quantification of low-abundance proteins, while the SWATH approach provides a more time-efficient method for targeted protein quantification of high-abundance proteins and, additionally, supports the search for novel biomarkers.

Rapid Food Product Analysis by Surface Acoustic Wave Nebulization Coupled Mass Spectrometry

Rapid food product analysis is of great interest for quality control and assurance during the production process. Conventional quality control protocols require time and labor intensive sample preparation for analysis by state-of-the-art analytical methods. To reduce overall cost and facilitate rapid qualitative assessments, food products need to be tested with minimal sample preparation. We present a novel and simple method for assessing food product compositions by mass spectrometry using a novel surface acoustic wave nebulization method. This method provides significant advantages over conventional methods requiring no pumps, capillaries, or additional chemicals to enhance ionization for mass spectrometric analysis. In addition, the surface acoustic wave nebulization - mass spectrometry method is ideal for rapid analysis and to investigate certain compounds by using the mass spectra as a type of species-specific fingerprint analysis. We present for the first time surface acoustic wave nebulization generated mass spectra of a variety of fermented food products from a small selection of vinegars, wines, and beers.

Maillard Proteomics: Opening New Pages

Protein glycation is a ubiquitous non-enzymatic post-translational modification, formed by reaction of protein amino and guanidino groups with carbonyl compounds, presumably reducing sugars and α-dicarbonyls. Resulting advanced glycation end products (AGEs) represent a highly heterogeneous group of compounds, deleterious in mammals due to their pro-inflammatory effect, and impact in pathogenesis of diabetes mellitus, Alzheimer's disease and ageing. The body of information on the mechanisms and pathways of AGE formation, acquired during the last decades, clearly indicates a certain site-specificity of glycation. It makes characterization of individual glycation sites a critical pre-requisite for understanding in vivo mechanisms of AGE formation and developing adequate nutritional and therapeutic approaches to reduce it in humans. In this context, proteomics is the methodology of choice to address site-specific molecular changes related to protein glycation. Therefore, here we summarize the methods of Maillard proteomics, specifically focusing on the techniques providing comprehensive structural and quantitative characterization of glycated proteome. Further, we address the novel break-through areas, recently established in the field of Maillard research, i.e., in vitro models based on synthetic peptides, site-based diagnostics of metabolism-related diseases (e.g., diabetes mellitus), proteomics of anti-glycative defense, and dynamics of plant glycated proteome during ageing and response to environmental stress.

Propafenone suppresses esophageal cancer proliferation through inducing mitochondrial dysfunction

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors with poor survival and limited therapeutic options. The aim of this study is to identify novel anticancer strategies from existing Food and Drug Administration (FDA)-approved drugs that have been used to clinically treat other diseases. Here, propafenone, an antiarrhythmic medication, was found to induce apoptosis and exert a significantly inhibitory effect on the proliferation and colony-forming ability of ESCC cells in a dose-dependent manner without observed cytotoxicity on normal esophageal epithelial cells. Furthermore, propafenone markedly suppressed growth of tumor xenografts in nude mice by reducing the Ki-67 proliferation index and angiogenesis but did not damage the vital organs of the animals. Mechanistically, our data from the proteomics, Western blot and flow cytometry analyses demonstrated that propafenone caused mitochondrial dysfunction as indicated by a decreased mitochondrial membrane potential and reduced expression of Bcl-xL and Bcl-2. In summary, this study provides the first evidence that propafenone, an FDA-approved drug to treat arrhythmias, could be a novel therapeutic strategy for treating ESCC without obvious side effects.

DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs

BACKGROUND

The classification of samples on a molecular level has manifold applications, from patient classification regarding cancer treatment to phylogenetics for identifying evolutionary relationships between species. Modern methods employ the alignment of DNA or amino acid sequences, mostly not genome-wide but only on selected parts of the genome. Recently proteomics-based approaches have become popular. An established method for the identification of peptides and proteins is liquid chromatography-tandem mass spectrometry (LC-MS/MS). First, protein sequences from MS/MS spectra are identified by means of database searches, given samples with known genome-wide sequence information, then sequence based methods are applied. Alternatively, de novo peptide sequencing algorithms annotate MS/MS spectra and deduce peptide/protein information without a database. A newer approach independent of additional information is to directly compare unidentified tandem mass spectra. The challenge then is to compute the distance between pairwise MS/MS runs consisting of thousands of spectra.

METHODS

We present DISMS2, a new algorithm to calculate proteome-wide distances directly from MS/MS data, extending the algorithm compareMS2, an approach that also uses a spectral comparison pipeline.

RESULTS

Our new more flexible algorithm, DISMS2, allows for the choice of the spectrum distance measure and includes different spectra preprocessing and filtering steps that can be tailored to specific situations by parameter optimization.

CONCLUSIONS

DISMS2 performs well for samples from species with and without database annotation and thus has clear advantages over methods that are purely based on database search.

Retrieving Quantitative Information of Histone PTMs by Mass Spectrometry

Posttranslational modifications (PTMs) of histones are one of the main research interests in the rapidly growing field of epigenetics. Accurate and precise quantification of these highly complex histone PTMs is critical for understanding the histone code and the biological significance behind it. It nonetheless remains a major analytical challenge. Mass spectrometry (MS) has been proven as a robust tool in retrieving quantitative information of histone PTMs, and a variety of MS-based quantitative strategies have been successfully developed and employed in basic research as well as clinical studies. In this chapter, we provide an overview for quantitative analysis of histone PTMs, often highly flexible and case dependent, as a primer for future experimental designs.

Surface acoustic wave nebulization device with dual interdigitated transducers improves SAWN-MS performance

We compared mass spectrometric (MS) performance of surface acoustic wave nebulization (SAWN) generated by a single interdigitated transducer (IDT) designed to produce a progressive wave (PW) to one with a dual IDT that can in theory generate standing waves (SW). Given that devices using dual IDTs had been shown to produce fewer large size droplets on average, we hypothesized they would improve MS performance by improving the efficiency of desolvation. Indeed, the SW-SAWN chip provided an improved limit of detection of 1 femtomole of peptide placed on chip making it 100× more sensitive than the PW design. However, as measured by high-speed image recording and phase Doppler particle analyzer measurements, there was only a 26% increase in the small diameter (1-10 µm) droplets produced from the new device, precluding a conclusion that the decrease in droplet size was solely responsible for the improvement in MS signal/noise. Given that the dual IDT design produced a more instantaneous plume than the PW design, the more likely contributor to improved MS signal/noise was concluded to be a higher ion flux entering the mass spectrometer for the dual IDT designs. Notably, the dual IDT device allowed production of much higher quality protein mass spectra up to about 20 kDa, compared with the single IDT device. Copyright © 2016 John Wiley & Sons, Ltd.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome.

AVAILABILITY

The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics.

In Vitro Transcription/Translation System: A Versatile Tool in the Search for Missing Proteins

Approximately 18% of all human genes purported to encode proteins have not been directly evidenced at the protein level, according to the validation criteria established by neXtProt, and are considered to be "missing" proteins. One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to identify as many of these missing proteins as possible in human samples using mass spectrometry-based methods. To further this goal, a consortium of C-HPP teams (chromosomes 5, 10, 16, and 19) has joined forces to devise new strategies to identify missing proteins by use of a cell-free in vitro transcription/translation system (IVTT). The proposed strategy employs LC-MS/MS data-dependent acquisition (DDA) and targeted selective reaction monitoring (SRM) methods to scrutinize low-complexity samples derived from IVTT. The optimized assays are then applied to identify missing proteins in human cells and tissues. We describe the approach and show proof-of-concept results for development of LC-SRM assays for identification of 18 missing proteins. We believe that the IVTT system, when coupled with downstream mass spectrometric identification, can be applied to identify proteins that have eluded more traditional methods of detection.

Multiplexed peptide analysis using data-independent acquisition and Skyline

Here we describe the use of data-independent acquisition (DIA) on a Q-Exactive mass spectrometer for the detection and quantification of peptides in complex mixtures using the Skyline Targeted Proteomics Environment (freely available online at http://skyline.maccosslab.org). The systematic acquisition of mass spectrometry (MS) or tandem MS (MS/MS) spectra by DIA is in contrast to DDA, in which the acquired MS/MS spectra are only suitable for the identification of a stochastically sampled set of peptides. Similarly to selected reaction monitoring (SRM), peptides can be quantified from DIA data using targeted chromatogram extraction. Unlike SRM, data acquisition is not constrained to a predetermined set of target peptides. In this protocol, a spectral library is generated using data-dependent acquisition (DDA), and chromatograms are extracted from the DIA data for all peptides in the library. As in SRM, quantification using DIA data is based on the area under the curve of extracted MS/MS chromatograms. In addition, a quality control (QC) method suitable for DIA based on targeted MS/MS acquisition is detailed. Not including time spent acquiring data, and time for database searching, the procedure takes ∼1-2 h to complete. Typically, data acquisition requires roughly 1-4 h per sample, and a database search will take 0.5-2 h to complete.