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Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistribution within the tissue is impossible. In the study presented here we used a sample embedding protocol optimized for mass spectrometry imaging (MSI) to prepare up to 15 rat intestine specimens at once. Desorption electrospray ionization (DESI) and matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) were employed to determine the distributions and relative abundances of four benchmarking compounds in the intestinal segments. High resolution MALDI-MSI experiments performed at 10 µm spatial resolution allowed to determine the drug distribution in the different intestinal histological compartments to determine the absorbed and tissue bound fractions of the drugs. The low tissue bound drug fractions, which were determined to account for 56-66% of the total drug, highlight the importance to understand the spatial distribution of drugs within the histological compartments of a given tissue to rationalize concentration differences found in PK studies. The mean drug abundances of four benchmark compounds determined by MSI were correlated with the absolute drug concentrations. Linear regression resulted in coefficients of determination (R2) ranging from 0.532 to 0.926 for MALDI-MSI and R2 values ranging from 0.585 to 0.945 for DESI-MSI, validating a quantitative relation of the imaging data. The good correlation of the absolute tissue concentrations of the benchmark compounds and the MSI data provides a bases for relative quantification of compounds within and between tissues, without normalization to an isotopically labelled standard, provided that the compared tissues have inherently similar ion suppression effects.
Virology, as a branch of the life sciences, discovered mass spectrometry (MS) to be the pivotal tool around two decades ago. The technique unveiled the complex network of interactions between the living world of pro- and eukaryotes and viruses, which delivered "a piece of bad news wrapped in protein" as defined by Peter Medawar, Nobel Prize Laureate, in 1960. However, MS is constantly evolving, and novel approaches allow for a better understanding of interactions in this micro- and nanoworld. Currently, we can investigate the interplay between the virus and the cell by analyzing proteomes, interactomes, virus-cell interactions, and search for the compounds that build viral structures. In addition, by using MS, it is possible to look at the cell from the broader perspective and determine the role of viral infection on the scale of the organism, for example, monitoring the crosstalk between infected tissues and the immune system. In such a way, MS became one of the major tools for the modern virology, allowing us to see the infection in the context of the whole cell or the organism. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.
The hydrophobic nature of most membrane proteins severely complicates their extraction, proteolysis and identification. Although detergents can be used to enhance the solubility of the membrane proteins, it is often difficult for a detergent not only to have a strong ability to extract membrane proteins, but also to be compatible with the subsequent proteolysis and mass spectrometric analysis. In this study, we made evaluation on a novel application of sodium laurate (SL) to the shotgun analysis of membrane proteomes. SL was found not only to lyse the membranes and solubilize membrane proteins as efficiently as SDS, but also to be well compatible with trypsin and chymotrypsin. Furthermore, SL could be efficiently removed by phase transfer method from samples after acidification, thus ensuring not to interfere with the subsequent CapLC-MS/MS analysis of the proteolytic peptides of proteins. When SL was applied to assist the digestion and identification of a standard protein mixture containing bacteriorhodoposin and the proteins in rat liver plasma membrane-enriched fractions, it was found that, compared with other two representative enzyme- and MS-compatible detergents RapiGest SF (RGS) and sodium deoxycholate (SDC), SL exhibited obvious superiority in the identification of membrane proteins particularly those with high hydrophobicity and/or multiple transmembrane domains.
Matrix-enhanced secondary ion mass spectrometry (ME-SIMS) has overcome one of the biggest disadvantages of SIMS analysis by providing the ability to detect intact biomolecules at high spatial resolution. By increasing ionization efficiency and minimizing primary ion beam-induced fragmentation of analytes, ME-SIMS has proven useful for detection of numerous biorelevant species, now including peptides. We report here the first demonstration of tandem ME-SIMS for de novo sequencing of endogenous neuropeptides from tissue in situ (i.e., rat pituitary gland). The peptide ions were isolated for tandem MS analysis using a 1 Da mass isolation window, followed by collision-induced dissociation (CID) at 1.5 keV in a collision cell filled with argon gas, for confident identification of the detected peptide. Using this method, neuropeptides up to m/z 2000 were detected and sequenced from the posterior lobe of the rat pituitary gland. These results demonstrate the potential for ME-SIMS tandem MS development in bottom-up proteomics imaging at high-spatial resolution.
Canine oral tumors are relatively common neoplasms in dogs. For disease monitoring and early diagnosis, salivary biomarkers are appropriate because saliva collection is non-invasive and requires no professional skills. In the era of omics, matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry (MALDI-TOF MS) coupled with liquid chromatography-tandem MS (LC-MS/MS) are suitable to identify potential disease-associated peptides and proteins. The present study aimed to use MALDI-TOF MS and LC-MS/MS to search for particular peptide mass fingerprints (PMFs) and conceivable biomarkers in saliva of dogs with early- and late-stage oral melanoma (EOM and LOM, respectively), oral squamous cell carcinoma (OSCC), benign oral tumors (BN), and periodontitis and healthy controls (CP). Pooled saliva samples in each group were used to be representative of population change. Unique PMFs were obtained and specific peptide fragments were sequenced by LC-MS/MS and BLAST-searched with mammalian protein databases. Seven peptide fragments appeared in the tumor groups (EOM, LOM, OSCC and BN) at 1096, 1208, 1322, 1794, 1864, 2354 and 2483 Da, two peptide fragments appeared in the LOM and OSCC groups at 2450 and 3492 Da, and in the CP controls at 2544 and 3026 Da. Also, protein-chemotherapy drug interaction networks were exhibited. Using western blot analysis, the expression of sentrin-specific protease 7 (SENP7), a peptide fragment at 1096 Da, in OSCC was significantly increased, as was the expression of TLR4, a peptide fragment at 3492 Da, in LOM and OSCC, compared with the CP group. The expression of nuclear factor kappa B (NF-κB), a TLR4 partner, was notably increased in OSCC compared with CP, BN and EOM. The expression was also enhanced in LOM compared with EOM. Expressed protein sequences from western blots were verified by LC-MS/MS. Western blots were then performed with individual samples in each group. The results showed the elevated expression of TLR4 in LOM and OSCC, compared with that in CP and BN, the increased expression of NF-κB in LOM and OSCC, compared with CP and in LOM compared with BN, and the enhanced expression of SENP7 in LOM and OSCC, compared with that in CP and BN. In conclusion, discrete clusters of EOM, LOM, OSCC, BN and CP groups and potential protein candidates associated with the diseases were demonstrated by salivary proteomics. Western blot analysis verified SENP7, TLR4 and NF-κB as potential salivary biomarkers of canine oral tumors.
More than 560 genes are annotated as proteases in the human genome. About half of the genes are not or are only marginally characterized. Over the past decade, mass spectrometry has become the basis for proteomics, especially for protein identification, performed in a high-throughput manner. This development was also very fruitful for exploring the complex systems associated with protease functions, as briefly reviewed here. Mass spectrometry is an ideal tool for monitoring protease reactions, as will be highlighted in this review.
Ensuring data quality and proper instrument functionality is a prerequisite for scientific investigation. Manual quality assurance is time-consuming and subjective. Metrics for describing liquid chromatography mass spectrometry (LC-MS) data have been developed; however, the wide variety of LC-MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether a data set is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the trade-off between false positive and false negative errors. The classifier models detected bad data sets with high sensitivity while maintaining high specificity. Moreover, the composite classifier was dramatically more specific than single metrics. Finally, we evaluated the performance of the classifier on a separate validation set where it performed comparably to the results for the testing/training data sets. By presenting the methods and software used to create the classifier, other groups can create a classifier for their specific QC regimen, which is highly variable lab-to-lab. In total, this manuscript presents 3400 LC-MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320-PXD000324.
Structural Mass Spectrometry (SMS) provides a comprehensive toolbox for the analysis of protein structure and function. It offers multiple sources of structural information that are increasingly useful for integrative structural modeling of complex protein systems. As MS-based structural workflows scale to larger systems, consistent and coherent data interpretation resources are needed to better support modeling. Unlike the proteomics community, practitioners of SMS lack adequate computational tools. Here, we review new developments in the Mass Spec Studio: an expandable ecosystem of workflows for the analysis of complementary SMS techniques with linkages to modeling. Current functionality in the Studio (version 2) supports three major SMS workflows (crosslinking, hydrogen/deuterium exchange and covalent labelling) and two pipelines for structural modeling, with a special focus on data integration. The Mass Spec Studio is an architecture focused on rapid and robust extension of functionality by a community of developers. SIGNIFICANCE: This review surveys the new data analysis capabilities within the Mass Spec Studio, a rich framework for rapid software development specifically targeting the community of structural proteomics and structural mass spectrometry. Updates to crosslinking, hydrogen/deuterium-exchange and covalent labeling apps are provided as well as a utility for translating such analyses into restraints that support integrative structural modeling. These new capabilities, together with the underlying design tools and content, provide the community with a wealth of resources to tackle complex structural problem and design new approaches to data analysis.
High-throughput Proteomics has been accelerated by (tandem) mass spectrometry. However, the slow speed of mass spectra analysis prevents the analysis results from being up-to-date. Tandem mass spectrometry database search requires O(|S||D|) time where S is the set of spectra and D is the set of peptides in a database. With usual values of |S| and |D|, database search is quite time consuming. Meanwhile, the database for search is usually updated every month, with 0.5-2% changes. Although the change in the database is usually very small, it may cause extensive changes in the overall analysis results because individual PSM scores such as deltaCn and E-value depend on the entire search results. Therefore, to keep the search results up-to-date, one needs to perform database search from scratch every time the database is updated, which is very inefficient.
In this review, we provide a comprehensive bibliographic overview of the role of mass spectrometry and the recent technical developments in the detection of post-translational modifications (PTMs). We briefly describe the principles of mass spectrometry for detecting PTMs and the protein and peptide enrichment strategies for PTM analysis, including phosphorylation, acetylation and oxidation. This review presents a bibliographic overview of the scientific achievements and the recent technical development in the detection of PTMs is provided. In order to ascertain the state of the art in mass spectrometry and proteomics methodologies for the study of PTMs, we analyzed all the PTM data introduced in the Universal Protein Resource (UniProt) and the literature published in the last three years. The evolution of curated data in UniProt for proteins annotated as being post-translationally modified is also analyzed. Additionally, we have undertaken a careful analysis of the research articles published in the years 2010 to 2012 reporting the detection of PTMs in biological samples by mass spectrometry.
2DE and 2D-DIGE based proteomics analysis of serum from women with endometriosis revealed several proteins to be dysregulated. A complete list of these proteins along with their mass spectrometry data and subsequent bioinformatics analysis are presented here. The data is related to "Investigation of serum proteome alterations in human endometriosis" by Dutta et al. [1].
Chromatin accessibility is a major regulator of gene expression. Histone writers/erasers have a critical role in chromatin compaction, as they "flag" chromatin regions by catalyzing/removing covalent post-translational modifications on histone proteins. Anomalous chromatin decondensation is a common phenomenon in cells experiencing aging and viral infection. Moreover, about 50% of cancers have mutations in enzymes regulating chromatin state. Numerous genomics methods have evolved to characterize chromatin state, but the analysis of (in)accessible chromatin from the protein perspective is not yet in the spotlight. We present an overview of the most used approaches to generate data on chromatin accessibility and then focus on emerging methods that utilize mass spectrometry to quantify the accessibility of histones and the rest of the chromatin bound proteome. Mass spectrometry is currently the method of choice to quantify entire proteomes in an unbiased large-scale manner; accessibility on chromatin of proteins and protein modifications adds an extra quantitative layer to proteomics dataset that assist more informed data-driven hypotheses in chromatin biology. We speculate that this emerging new set of methods will enhance predictive strength on which proteins and histone modifications are critical in gene regulation, and which proteins occupy different chromatin states in health and disease.
Hepatocellular carcinoma (HCC) is a highly malignant disease for which the development of prospective or prognostic biomarkers is urgently required. Although metabolomics is widely used for biomarker discovery, there are some bottlenecks regarding the comprehensiveness of detected features, reproducibility of methods, and identification of metabolites. In addition, information on localization of metabolites in tumor tissue is needed for functional analysis. Here, we developed a wide-polarity global metabolomics (G-Met) method, identified HCC biomarkers in human liver samples by high-definition mass spectrometry (HDMS), and demonstrated localization in cryosections using desorption electrospray ionization MS imaging (DESI-MSI) analysis.
Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample's molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.
Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.
Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new "parsimonious" charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies.
Mammalian zinc metallothionein-3 (Zn7MT3) plays an important role in protecting against copper toxicity by scavenging free Cu(II) ions or removing Cu(II) bound to β-amyloid and α-synuclein. While previous studies reported that Zn7MT3 reacts with Cu(II) ions to form Cu(I)4Zn(II)4MT3ox containing two disulfides (ox), the precise localization of the metal ions and disulfides remained unclear. Here, we undertook comprehensive structural characterization of the metal-protein complexes formed by the reaction between Zn7MT3 and Cu(II) ions using native ion mobility mass spectrometry (IM-MS). The complex formation mechanism was found to involve the disassembly of Zn3S9 and Zn4S11 clusters from Zn7MT3 and reassembly into Cu(I)xZn(II)yMT3ox complexes rather than simply Zn(II)-to-Cu(I) exchange. At neutral pH, the β-domain was shown to be capable of binding up to six Cu(I) ions to form Cu(I)6Zn(II)4MT3ox, although the most predominant species was the Cu(I)4Zn(II)4MT3ox complex. Under acidic conditions, four Zn(II) ions dissociate, but the Cu(I)4-thiolate cluster remains stable, highlighting the MT3 role as a Cu(II) scavenger even at lower than the cytosolic pH. IM-derived collision cross sections (CCS) reveal that Cu(I)-to-Zn(II) swap in Zn7MT3 with concomitant disulfide formation induces structural compaction and a decrease in conformational heterogeneity. Collision-induced unfolding (CIU) experiments estimated that the native-like folded Cu(I)4Zn(II)4MT3ox conformation is more stable than Zn7MT3. Native top-down MS demonstrated that the Cu(I) ions are exclusively bound to the β-domain in the Cu(I)4Zn(II)4MT3ox complex as well as the two disulfides, serving as a steric constraint for the Cu(I)4-thiolate cluster. In conclusion, this study enhances our comprehension of the structure, stability, and dynamics of Cu(I)xZn(II)yMT3ox complexes.
Protein import into organelles is essential for all eukaryotes and facilitated by multi-protein translocation machineries. Analysing whether a protein is transported into an organelle is largely restricted to single constituents. This renders knowledge about imported proteins incomplete, limiting our understanding of organellar biogenesis and function. Here we introduce a method that enables charting an organelle's importome. The approach relies on inducible RNAi-mediated knockdown of an essential subunit of a translocase to impair import and quantitative mass spectrometry. To highlight its potential, we established the mitochondrial importome of Trypanosoma brucei, comprising 1,120 proteins including 331 new candidates. Furthermore, the method allows for the identification of proteins with dual or multiple locations and the substrates of distinct protein import pathways. We demonstrate the specificity and versatility of this ImportOmics method by targeting import factors in mitochondria and glycosomes, which demonstrates its potential for globally studying protein import and inventories of organelles.
Two-dimensional mass spectrometry (2D MS) is a data-independent tandem mass spectrometry technique in which precursor and fragment ion species can be correlated without the need for prior ion isolation. The behavior of phase in 2D Fourier transform mass spectrometry is investigated with respect to the calculation of phase-corrected absorption-mode 2D mass spectra. 2D MS datasets have a phase that is defined differently in each dimension. In both dimensions, the phase behavior of precursor and fragment ions is found to be different. The dependence of the phase for both precursor and fragment ion signals on various parameters (e.g., modulation frequency, shape of the fragmentation zone) is discussed. Experimental data confirms the theoretical calculations of the phase in each dimension. Understanding the phase relationships in a 2D mass spectrum is beneficial to the development of possible algorithms for phase correction, which may improve both the signal-to-noise ratio and the resolving power of peaks in 2D mass spectra.
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