Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry

Deep topographic proteomics of a human brain tumour

The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.

Routine Workflow of Spatial Proteomics on Micro-formalin-Fixed Paraffin-Embedded Tissues

In the era of single-cell biology, spatial proteomics has emerged as an important frontier. However, it still faces several challenges in technology. Formalin-fixed paraffin-embedded (FFPE) tissues are an important material in spatial proteomics, in which fixed tissues are excised using laser capture microdissection (LCM), followed by protein identification with mass spectrometry. For a satisfied spatial proteomics upon FFPE tissues, the excision area is expected to be as small as possible, and the identified proteins are countered upon as much as possible. For a general laboratory for spatial proteomics, a routine workflow is required, not relying on any special device, and is easily operating. In view of these challenges in technology, we initiated a technology evaluation throughout the entire procedure of proteomic analysis with micro-FFPE tissues. In contrast to the protocols reported previously, several innovations in technology were proposed and conducted, such as removal of destaining, decross-linking with "hang-down", solution simplification for peptide generation and balancing to excision area, and capture rate of micro-FFPE tissues. After optimization of all the necessary steps, a routine workflow was established, in which the minimized area for protein identification was 0.002 mm2, while the excision area for a consistent proteomic analysis was 0.05 mm2. Using the developed workflow and collecting the micro-FFPE tissues continuously, for the first time, a spatial proteomic atlas of mouse brain was preliminarily constructed, which exhibited the typical characteristics of spatial-dependent protein abundance and functional enrichment.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP)

Accurate, in-depth mapping of proteins on whole-tissue levels provides comprehensive insights into the spatially-organized regulatory processes/networks in tissues, but is challenging. Here we describe a micro-scaffold assisted spatial proteomics (MASP) strategy, based on spatially-resolved micro-compartmentalization of tissue using a 3D-printed micro-scaffold, capable of mapping thousands of proteins across a whole-tissue slice with excellent quantitative accuracy/precision. The pipeline includes robust tissue micro-compartmentalization with precisely-preserved spatial information, reproducible procurement and preparation of the micro-specimens, followed by sensitive LC-MS analysis and map generation by a MAsP app. The mapping accuracy was validated by comparing the MASP-generated maps of spiked-in peptides and brain-region-specific markers with known patterns, and by correlating the maps of the two protein components of the same heterodimer. The MASP was applied in mapping >5000 cerebral proteins in the mouse brain, encompassing numerous important brain markers, regulators, and transporters, where many of these proteins had not previously been mapped on the whole-tissue level.

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI-IMS. This is achieved by combining information from IMS and shotgun proteomics (LC-MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC-MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.

Infrared Laser Ablation Microsampling for Small Volume Proteomics

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm², respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm² regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

Diversity of synaptic protein complexes as a function of the abundance of their constituent proteins: A modeling approach

The postsynaptic density (PSD) is a dense protein network playing a key role in information processing during learning and memory, and is also indicated in a number of neurological disorders. Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins (NMDAR, AMPAR, PSD-95, SynGAP, GKAP, Shank3, Homer1) based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients (altogether 524 different simulations). Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse.

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.

Mapping Molecular Datasets Back to the Brain Regions They are Extracted from: Remembering the Native Countries of Hypothalamic Expatriates and Refugees

This article focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets-laser-capture microdissection-is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.

Mass Spectrometry Imaging and GC-MS Profiling of the Mammalian Peripheral Sensory-Motor Circuit

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has evolved to become an effective discovery tool in science and clinical diagnostics. Here, chemical imaging approaches are applied to well-defined regions of the mammalian peripheral sensory-motor system, including the dorsal root ganglia (DRG) and adjacent nerves. By combining several MSI approaches, analyte coverage is increased and 195 distinct molecular features are observed. Principal component analysis suggests three chemically different regions within the sensory-motor system, with the DRG and adjacent nerve regions being the most distinct. Investigation of these regions using gas chromatography-mass spectrometry corroborate these findings and reveal important metabolic markers related to the observed differences. The heterogeneity of the structurally, physiologically, and functionally connected regions demonstrates the intricate chemical and spatial regulation of their chemical composition.

Automatic registration of mass spectrometry imaging data sets to the Allen brain atlas

Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal's tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions.

Quantitative proteomic profiling reveals novel region-specific markers in the adult mouse brain

Despite major advances in neuroscience, a comprehensive understanding of the structural and functional components of the adult brain compartments remains to be fully elucidated at a quantitative molecular level. Indeed, over half of the soluble- and membrane-annotated proteins are currently unmapped within online digital brain atlases. In this study, two complementary approaches were used to assess the unique repertoire of proteins enriched within select regions of the adult mouse CNS, including the brain stem, cerebellum, and remaining brain hemispheres. Of the 1200 proteins visualized by 2D-DIGE, approximately 150 (including cytosolic and membrane proteins) were found to exhibit statistically significant changes in relative abundance thus representing putative region-specific brain markers. In addition to using a high-precision (18) O-labeling strategy for the quantitative LC-MS/MS mapping of membrane proteins isolated from myelin-enriched fractions, we have identified over 1000 proteins that have yet to be described in any other mammalian myelin proteome. A comparison of our myelin proteome was made to an existing transcriptome database containing mRNA abundance profiles during oligodendrocyte differentiation and has confirmed statistically significant abundance changes for ∼500 of these newly mapped proteins, thus revealing new roles in oligodendrocyte and myelin biology. These data offer a resource for the neuroscience community studying the molecular basis for specialized neuronal activities in the CNS and myelin-related disorders. The MS proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000327 (http://proteomecentral.proteomexchange.org/dataset/PXD000327).

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Exploring the proteomic landscape of a gastric cancer biopsy with the shotgun imaging analyzer

Accessing localized proteomic profiles has emerged as a fundamental strategy to understand the biology of diseases, as recently demonstrated, for example, in the context of determining cancer resection margins with improved precision. Here, we analyze a gastric cancer biopsy sectioned into 10 parts, each one subjected to MudPIT analysis. We introduce a software tool, named Shotgun Imaging Analyzer and inspired in MALDI imaging, to enable the overlaying of a protein's expression heat map on a tissue picture. The software is tightly integrated with the NeXtProt database, so it enables the browsing of identified proteins according to chromosomes, quickly listing human proteins never identified by mass spectrometry (i.e., the so-called missing proteins), and the automatic search for proteins that are more expressed over a specific region of interest on the biopsy, all of which constitute goals that are clearly well-aligned with those of the C-HPP. Our software has been able to highlight an intense expression of proteins previously known to be correlated with cancers (e.g., glutathione S-transferase Mu 3), and in particular, we draw attention to Gastrokine-2, a "missing protein" identified in this work of which we were able to clearly delineate the tumoral region from the "healthy" with our approach. Data are available via ProteomeXchange with identifier PXD000584.

Reduced insulin/insulin-like growth factor-1 signaling and dietary restriction inhibit translation but preserve muscle mass in Caenorhabditis elegans

Reduced signaling through the C. elegans insulin/insulin-like growth factor-1-like tyrosine kinase receptor daf-2 and dietary restriction via bacterial dilution are two well-characterized lifespan-extending interventions that operate in parallel or through (partially) independent mechanisms. Using accurate mass and time tag LC-MS/MS quantitative proteomics, we detected that the abundance of a large number of ribosomal subunits is decreased in response to dietary restriction, as well as in the daf-2(e1370) insulin/insulin-like growth factor-1-receptor mutant. In addition, general protein synthesis levels in these long-lived worms are repressed. Surprisingly, ribosomal transcript levels were not correlated to actual protein abundance, suggesting that post-transcriptional regulation determines ribosome content. Proteomics also revealed the increased presence of many structural muscle cell components in long-lived worms, which appeared to result from the prioritized preservation of muscle cell volume in nutrient-poor conditions or low insulin-like signaling. Activation of DAF-16, but not diet restriction, stimulates mRNA expression of muscle-related genes to prevent muscle atrophy. Important daf-2-specific proteome changes include overexpression of aerobic metabolism enzymes and general activation of stress-responsive and immune defense systems, whereas the increased abundance of many protein subunits of the proteasome core complex is a dietary-restriction-specific characteristic.

Applications of Mass Spectrometry in Proteomics

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

Label-free quantitative LC-MS proteomics of Alzheimer's disease and normally aged human brains

Quantitative proteomics analysis of cortical samples of 10 Alzheimer's disease (AD) brains versus 10 normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values <0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty-seven of these proteins were reported as differentially abundant or modified in AD in previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

Isotopic labelling of peptides in tissues enhances mass spectrometric profiling

RATIONALE

There is a need in imaging mass spectrometry to use the acquired isotope distribution to unequivocally determine the identity of a peptide ion. A way of achieving unambiguous differentiation of ions from protonated peptides from other [M + H](+) ions in a tissue would be via the direct on-tissue incorporation of (18)O into peptides.

METHODS

Tissues were first digested with trypsin for 3  h at 37 °C in a humidified chamber. For the (18)O-labelling of digested peptides 1  μL of H(2)(18)O/50  mM ammonium acetate (at pH 6.75) was added to the array of tryptic spots and incubated at room temperature for 20  min. α-Cyano-4-hydroxycinnamic acid was used as a matrix modifier. The mass spectral analysis of tissue sections was carried out using a matrix-assisted laser desorption/ionisation tandem time-of-flight (MALDI-TOF-TOF) instrument.

RESULTS

On-tissue incorporation of (18)O into peptides cannot be carried out during the digestion step inside a humidified chamber. After tissue digestion for 3  h at 37 °C in an humidified chamber, (18)O labelling was carried out for 20  min at room temperature (no humidified chamber). No trypsin was needed to enhance the labelling.

CONCLUSIONS

For first time the feasibility of (18)O-labelling of peptides in situ for tissues has been demonstrated. The method decouples protein digestion from peptide labelling and is performed in sequential steps. Furthermore, we observed that (18)O incorporation produces characteristic isotopic peptide distributions, thus making facile distinguishing peptides from other tissue molecular components that ionise in the MALDI ion source.

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.

18O-labeled proteome reference as global internal standards for targeted quantification by selected reaction monitoring-mass spectrometry

Selected reaction monitoring (SRM)-MS is an emerging technology for high throughput targeted protein quantification and verification in biomarker discovery studies; however, the cost associated with the application of stable isotope-labeled synthetic peptides as internal standards can be prohibitive for screening a large number of candidate proteins as often required in the preverification phase of discovery studies. Herein we present a proof of concept study using an (18)O-labeled proteome reference as global internal standards (GIS) for SRM-based relative quantification. The (18)O-labeled proteome reference (or GIS) can be readily prepared and contains a heavy isotope ((18)O)-labeled internal standard for every possible tryptic peptide. Our results showed that the percentage of heavy isotope ((18)O) incorporation applying an improved protocol was >99.5% for most peptides investigated. The accuracy, reproducibility, and linear dynamic range of quantification were further assessed based on known ratios of standard proteins spiked into the labeled mouse plasma reference. Reliable quantification was observed with high reproducibility (i.e. coefficient of variance <10%) for analyte concentrations that were set at 100-fold higher or lower than those of the GIS based on the light ((16)O)/heavy ((18)O) peak area ratios. The utility of (18)O-labeled GIS was further illustrated by accurate relative quantification of 45 major human plasma proteins. Moreover, quantification of the concentrations of C-reactive protein and prostate-specific antigen was illustrated by coupling the GIS with standard additions of purified protein standards. Collectively, our results demonstrated that the use of (18)O-labeled proteome reference as GIS provides a convenient, low cost, and effective strategy for relative quantification of a large number of candidate proteins in biological or clinical samples using SRM.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Liquid chromatography-mass spectrometry-based quantitative proteomics

LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications due to growing capabilities for broad proteome coverage and good accuracy and precision in quantification. Herein, we review the current LC-MS-based quantification methods with respect to their advantages and limitations and highlight their potential applications.

Distinct cerebrospinal fluid proteomes differentiate post-treatment lyme disease from chronic fatigue syndrome

Background

Neurologic Post Treatment Lyme disease (nPTLS) and Chronic Fatigue (CFS) are syndromes of unknown etiology. They share features of fatigue and cognitive dysfunction, making it difficult to differentiate them. Unresolved is whether nPTLS is a subset of CFS.

Methods and Principal Findings

Pooled cerebrospinal fluid (CSF) samples from nPTLS patients, CFS patients, and healthy volunteers were comprehensively analyzed using high-resolution mass spectrometry (MS), coupled with immunoaffinity depletion methods to reduce protein-masking by abundant proteins. Individual patient and healthy control CSF samples were analyzed directly employing a MS-based label-free quantitative proteomics approach. We found that both groups, and individuals within the groups, could be distinguished from each other and normals based on their specific CSF proteins (p<0.01). CFS (n = 43) had 2,783 non-redundant proteins, nPTLS (n = 25) contained 2,768 proteins, and healthy normals had 2,630 proteins. Preliminary pathway analysis demonstrated that the data could be useful for hypothesis generation on the pathogenetic mechanisms underlying these two related syndromes.

Conclusions

nPTLS and CFS have distinguishing CSF protein complements. Each condition has a number of CSF proteins that can be useful in providing candidates for future validation studies and insights on the respective mechanisms of pathogenesis. Distinguishing nPTLS and CFS permits more focused study of each condition, and can lead to novel diagnostics and therapeutic interventions.

Plasma proteome response to severe burn injury revealed by 18O-labeled "universal" reference-based quantitative proteomics

A burn injury represents one of the most severe forms of human trauma and is responsible for significant mortality worldwide. Here, we present the first quantitative proteomics investigation of the blood plasma proteome response to severe burn injury by comparing the plasma protein concentrations of 10 healthy control subjects with those of 15 severe burn patients at two time-points following the injury. The overall analytical strategy for this work integrated immunoaffinity depletion of the 12 most abundant plasma proteins with cysteinyl-peptide enrichment-based fractionation prior to LC-MS analyses of individual patient samples. Incorporation of an 18O-labeled "universal" reference among the sample sets enabled precise relative quantification across samples. In total, 313 plasma proteins confidently identified with two or more unique peptides were quantified. Following statistical analysis, 110 proteins exhibited significant abundance changes in response to the burn injury. The observed changes in protein concentrations suggest significant inflammatory and hypermetabolic response to the injury, which is supported by the fact that many of the identified proteins are associated with acute phase response signaling, the complement system, and coagulation system pathways. The regulation of approximately 35 proteins observed in this study is in agreement with previous results reported for inflammatory or burn response, but approximately 50 potentially novel proteins previously not known to be associated with burn response or inflammation are also found. Elucidating proteins involved in the response to severe burn injury may reveal novel targets for therapeutic interventions as well as potential predictive biomarkers for patient outcomes such as multiple organ failure.

Rapid and efficient protein digestion using trypsin-coated magnetic nanoparticles under pressure cycles

Trypsin-coated magnetic nanoparticles (EC-TR/NPs), prepared via a simple multilayer random crosslinking of the trypsin molecules onto magnetic nanoparticles, were highly stable and could be easily captured using a magnet after the digestion was complete. EC-TR/NPs showed a negligible loss of trypsin activity after multiple uses and continuous shaking, whereas the conventional immobilization of covalently attached trypsin on NPs resulted in a rapid inactivation under the same conditions due to the denaturation and autolysis of trypsin. A single model protein, a five-protein mixture, and a whole mouse brain proteome were digested at atmospheric pressure and 37°C for 12 h or in combination with pressure cycling technology at room temperature for 1 min. In all cases, EC-TR/NPs performed equally to or better than free trypsin in terms of both the identified peptide/protein number and the digestion reproducibility. In addition, the concomitant use of EC-TR/NPs and pressure cycling technology resulted in very rapid (∼1 min) and efficient digestions with more reproducible digestion results.

Improved strategies for rapid identification of chemically cross-linked peptides using protein interaction reporter technology

Protein interaction reporter (PIR) technology can enable identification of in vivo protein interactions with the use of specialized chemical cross-linkers, liquid chromatography, and high-resolution mass spectrometry. PIR-cross-linkers contain labile bonds that are specifically fragmented under low energy collision or photodissociation conditions in the mass spectrometer source, thus releasing cross-linked peptides. Successful analysis of PIR-cross-linked proteins requires the use of expected mathematical relationships between cross-linked complexes and released peptides after fragmentation of the labile PIR bonds. Presented here is a next-generation software tool, BLinks, for use in the analysis and identification of PIR-cross-linked proteins. BLinks is an advancement beyond our previous efforts by incorporation of chromatographic profiles that must match between cross-linked complexes and released peptides to enable estimation of p-values to help filter true relationships from complex data sets. Additionally, BLinks was used to incorporate Mascot database searching results from subsequent MS/MS analysis of the released peptides to facilitate identification of cross-linked proteins. BLinks was used in the analysis of human serum albumin, and 46 interpeptide relationships were found spanning 30 proximal residues with a 2.2% false discovery rate. BLinks was also used to track peptides involved in multiple, coeluting relationships that make accurate identification of protein interactions difficult. An additional 10 interpeptide relationships were identified despite poor correlation using the profiling tools provided with BLinks. Additionally, BLinks can be used to globally map all interpeptide relationships from the data analysis and customize subsequent analysis to target specific peptides of interest, thus making it a useful tool for both discovery of protein interactions and mapping protein topology.

Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples

Nine replicate samples of peptides from soybean leaves, each spiked with a different concentration of bovine apotransferrin peptides, were analyzed on a mass spectrometer using multidimensional protein identification technology (MudPIT). Proteins were detected from the peptide tandem mass spectra, and the numbers of spectra were statistically evaluated for variation between samples. The results corroborate prior knowledge that combining spectra from replicate samples increases the number of identifiable proteins and that a summed spectral count for a protein increases linearly with increasing molar amounts of protein. Furthermore, statistical analysis of spectral counts for proteins in two- and three-way comparisons between replicates and combined replicates revealed little significant variation arising from run-to-run differences or data-dependent instrument ion sampling that might falsely suggest differential protein accumulation. In these experiments, spectral counting was enabled by PANORAMICS, probability-based software that predicts proteins detected by sets of observed peptides. Three alternative approaches to counting spectra were also evaluated by comparison. As the counting thresholds were changed from weaker to more stringent, the accuracy of ratio determination also changed. These results suggest that thresholds for counting can be empirically set to improve relative quantitation. All together, the data confirm the accuracy and reliability of label-free spectral counting in the relative, quantitative analysis of proteins between samples.

Clinical microfluidics for neutrophil genomics and proteomics

Neutrophils play critical roles in modulating the immune response. We present a robust methodology for rapidly isolating neutrophils directly from whole blood and develop ‘on-chip’ processing for mRNA and protein isolation for genomics and proteomics. We validate this device with an ex vivo stimulation experiment and by comparison with standard bulk isolation methodologies. Lastly, we implement this tool as part of a near patient blood processing system within a multi-center clinical study of the immune response to severe trauma and burn injury. The preliminary results from a small cohort of patients in our study and healthy controls show a unique time-dependent gene expression pattern clearly demonstrating the ability of this tool to discriminate temporal transcriptional events of neutrophils within a clinical setting.

Quantitative analysis of cell surface membrane proteins using membrane-impermeable chemical probe coupled with 18O labeling

We report a mass spectrometry-based strategy for quantitative analysis of cell surface membrane proteome changes. The strategy includes enrichment of surface membrane proteins using a membrane-impermeable chemical probe followed by stable isotope (18)O labeling and LC-MS analysis. We applied this strategy for enriching membrane proteins expressed by Shewanella oneidensis MR-1, a Gram-negative bacterium with known metal-reduction capability via extracellular electron transfer between outer membrane proteins and extracellular electron receptors. LC/MS/MS analysis resulted in the identification of about 400 proteins with 79% of them being predicted to be membrane localized. Quantitative aspects of the membrane enrichment were shown by peptide level (16)O and (18)O labeling of proteins from wild-type and mutant cells (generated from deletion of a type II secretion protein, GspD) prior to LC-MS analysis. Using a chemical probe labeled pure protein as an internal standard for normalization, the quantitative data revealed reduced abundances in Delta gspD mutant cells of many outer membrane proteins including the outer membrane c-type cytochromes OmcA and MtrC, in agreement with a previous report that these proteins are substrates of the type II secretion system.

Establishing the proteome of normal human cerebrospinal fluid

Background

Knowledge of the entire protein content, the proteome, of normal human cerebrospinal fluid (CSF) would enable insights into neurologic and psychiatric disorders. Until now technologic hurdles and access to true normal samples hindered attaining this goal.

Methods and Principal Findings

We applied immunoaffinity separation and high sensitivity and resolution liquid chromatography-mass spectrometry to examine CSF from healthy normal individuals. 2630 proteins in CSF from normal subjects were identified, of which 56% were CSF-specific, not found in the much larger set of 3654 proteins we have identified in plasma. We also examined CSF from groups of subjects previously examined by others as surrogates for normals where neurologic symptoms warranted a lumbar puncture but where clinical laboratory were reported as normal. We found statistically significant differences between their CSF proteins and our non-neurological normals. We also examined CSF from 10 volunteer subjects who had lumbar punctures at least 4 weeks apart and found that there was little variability in CSF proteins in an individual as compared to subject to subject.

Conclusions

Our results represent the most comprehensive characterization of true normal CSF to date. This normal CSF proteome establishes a comparative standard and basis for investigations into a variety of diseases with neurological and psychiatric features.

A concise review of mass spectrometry imaging

Mass spectrometric imaging allows the investigation of the spatial distribution of molecules at complex surfaces. The combination of molecular speciation with local analysis renders a chemical microscope that can be used for the direct biomolecular characterization of histological tissue surfaces. MS based imaging advantageously allows label-free detection and mapping of a wide-range of biological compounds whose presence or absence can be the direct result of disease pathology. Successful detection of the analytes of interest at the desired spatial resolution requires careful attention to several steps in the mass spectrometry imaging protocol. This review will describe and discuss a selected number of crucial developments in ionization, instrumentation, and application of this innovative technology. The focus of this review is on the latest developments in imaging MS. Selected biological applications are employed to illustrate some of the novel features discussed. Two commonly used MS imaging techniques, secondary ion mass spectrometric (SIMS) imaging and matrix-assisted laser desorption ionization (MALDI) mass spectrometric imaging, center this review. New instrumental developments are discussed that extend spatial resolution, mass resolving power, mass accuracy, tandem-MS capabilities, and offer new gas-phase separation capabilities for both imaging techniques. It will be shown how the success of MS imaging is crucially dependent on sample preparation protocols as they dictate the nature and mass range of detected biomolecules that can be imaged. Finally, developments in data analysis strategies for large imaging datasets will be briefly discussed.

Dual spatial maps of transcript and protein abundance in the mouse brain

Integrating quantitative proteomic and transcriptomic datasets promises valuable insights in unraveling the molecular mechanisms of the brain. We concentrate on recent studies using mass spectrometry and microarray data to investigate transcript and protein abundance in normal and diseased neural tissues. Highlighted are dual spatial maps of these molecules obtained using voxelation of the mouse brain. We demonstrate that the relationship between transcript and protein levels displays a specific anatomical distribution, with greatest fidelity in midline structures and the hypothalamus. Genes are also identified that have strong correlations between mRNA and protein abundance. In addition, transcriptomic and proteomic analysis of mouse models of Parkinson's disease are discussed.

Shotgun proteomics in neuroscience

Mass spectrometry-based proteomics is increasingly used to address basic and clinical questions in biomedical research through studies of differential protein expression, protein-protein interactions, and posttranslational modifications. The complex structural and functional organization of the human brain warrants the application of high-throughput, systematic approaches to understand the functional alterations under normal physiological conditions and the perturbations of neurological diseases. This primer focuses on shotgun-proteomics-based tandem mass spectrometry for the identification of proteins in a complex mixture. It describes the basic concepts of protein differential expression analysis and posttranslational modification analysis and discusses several strategies to improve the coverage of the proteome.

Mapping protein abundance patterns in the brain using voxelation combined with liquid chromatography and mass spectrometry

Voxelation creates expression atlases by high-throughput analysis of spatially registered cubes or voxels harvested from the brain. The modality independence of voxelation allows a variety of bioanalytical techniques to be used to map abundance. Protein expression patterns in the brain can be obtained using liquid chromatography (LC) combined with mass spectrometry (MS). Here we describe the methodology of voxelation as it pertains particularly to LC-MS proteomic analysis: sample preparation, instrumental set up and analysis, peptide identification and protein relative abundance quantitation. We also briefly describe some of the advantages, limitations and insights into the brain that can be obtained using combined proteomic and transcriptomic maps.

Direct quantitation of peptide mixtures without standards using clusters formed by electrospray ionization mass spectrometry

In electrospray ionization mass spectrometry, ion abundances depend on a number of different factors, including analyte surface activity, competition between analytes for charge, analyte concentration, as well as instrumental factors, including mass-dependent ion transmission and detection. Here, a novel method for obtaining quantitative information about solution-phase concentrations of peptide mixtures is described and demonstrated for five different peptide mixtures with relative concentrations ranging from 0.05% to 50%. In this method, the abundances of large clusters containing anywhere from 0 to 13 impurity molecules are measured and directly related to the relative solution-phase concentration of the peptides. For clusters containing approximately 15 or more peptides, the composition of the clusters approaches the statistical value indicating that these clusters are formed nonspecifically and that any differences in ion detection or ionization efficiency are negligible at these large cluster sizes. This method is accurate to within approximately 20% or better, even when the relative ion intensities of the protonated monomers can differ by over an order of magnitude compared to their solution-phase concentrations. Although less accurate than other quantitation methods that employ internal standards, this method does have the key advantages of speed, simplicity, and the ability to quantitate components in solution even when the identities of the components are unknown.

A simple procedure for effective quenching of trypsin activity and prevention of 18O-labeling back-exchange

Trypsin-catalyzed stable isotope 16O/18O-labeling of the C-terminal carboxyl groups of peptides is increasingly used in shotgun proteomics for relative peptide/protein quantitation. However, precise quantitative measurements are often complicated by residual trypsin that can catalyze the back-exchange of 18O with 16O after labeling. Here, we demonstrate through a detailed evaluation that boiling the peptide sample for 10 min provides a simple means for completely quenching residual trypsin activity and preventing oxygen back-exchange in 18O-labeled samples. We also observed that the presence of organic solvents such as acetonitrile made boiling less efficient for inactivating trypsin. Finally, current 18O-labeling methods that typically employ immobilized trypsin result in significant sample losses due to nonspecific binding of peptides to the resin, making their application toward smaller biological samples increasingly impractical. We present here an improved 18O-labeling protocol that is more applicable to microscale biological samples by using solution-phase trypsin instead of immobilized trypsin. The ability to generate stably 18O-labeled samples without back-exchange should enable more effective applications of 18O-labeling toward large-scale biomarker discovery and validations where an 18O-labeled sample can be used as a common reference for quantitation.

Robust MS quantification method for phospho-peptides using 18O/16O labeling

Background

Quantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using ¹⁸O/¹⁶O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-β treatment of neurons used as a cellular model system with relevance to Alzheimer's disease, but is generally applicable.

Results

The presented method assesses, in complete cell lysates, the degree of phosphorylation of specific peptide residues from MS spectra using ¹⁸O/¹⁶O labeling. The abundance of each observed phospho-peptide from two cell states was estimated from three overlapping isotope contours. The influence of peptide-specific labeling efficiency was removed by performing a label swapped experiment and assuming that the labeling efficiency was unchanged upon label swapping. Different degrees of phosphorylation were reported using the fold change measure which was extended with a confidence interval found to reflect the quality of the underlying spectra. Furthermore a new way of method assessment using simulated data is presented. Using simulated data generated in a manner mimicking real data it was possible to show the method's robustness both with increasing noise levels and with decreasing labeling efficiency.

Conclusion

The fold change error assessable on simulated data was on average 0.16 (median 0.10) with an error-to-signal ratio and labeling efficiency distributions similar to the ones found in the experimentally observed spectra. Applied to experimentally observed spectra a very good match was found to the model (<10% error for 85% of spectra) with a high degree of robustness, as assessed by data removal. This new method can thus be used for quantitative signal cascade analysis of total cell extracts in a high throughput mode.

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments

We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass spectrometry instrumentation. The motivations for our extensions include the need to exploit high-resolution data to support highly complex proteomics experiments, especially those involving extensive off-line fractionation, to which recent label-free workflows might not easily generalize.

Specification of spatial relationships in directed graphs of cell signaling networks

Graph theory provides a useful and powerful tool for the analysis of cellular signaling networks. Intracellular components such as cytoplasmic signaling proteins, transcription factors, and genes are connected by links, representing various types of chemical interactions that result in functional consequences. However, these graphs lack important information regarding the spatial distribution of cellular components. The ability of two cellular components to interact depends not only on their mutual chemical affinity but also on colocalization to the same subcellular region. Localization of components is often used as a regulatory mechanism to achieve specific effects in response to different receptor signals. Here we describe an approach for incorporating spatial distribution into graphs and for the development of mixed graphs where links are specified by mutual chemical affinity as well as colocalization. We suggest that such mixed graphs will provide more accurate descriptions of functional cellular networks and their regulatory capabilities and aid in the development of large-scale predictive models of cellular behavior.

Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope (18)O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the (18)O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on (16)O/(18)O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free (16)O-MS intensity data extracted from the same data sets. The results showed that the (18)O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the (18)O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

Dynamically multiplexed ion mobility time-of-flight mass spectrometry

Ion mobility spectrometry-time-of-flight mass spectrometry (IMS-TOFMS) has been increasingly used in analysis of complex biological samples. A major challenge is to transform IMS-TOFMS to a high-sensitivity, high-throughput platform, for example, for proteomics applications. In this work, we have developed and integrated three advanced technologies, including efficient ion accumulation in an ion funnel trap prior to IMS separation, multiplexing (MP) of ion packet introduction into the IMS drift tube, and signal detection with an analog-to-digital converter, into the IMS-TOFMS system for the high-throughput analysis of highly complex proteolytic digests of, for example, blood plasma. To better address variable sample complexity, we have developed and rigorously evaluated a novel dynamic MP approach that ensures correlation of the analyzer performance with an ion source function and provides the improved dynamic range and sensitivity throughout the experiment. The MP IMS-TOFMS instrument has been shown to reliably detect peptides at a concentration of 1 nM in the presence of a highly complex matrix, as well as to provide a 3 orders of magnitude dynamic range and a mass measurement accuracy of better than 5 ppm. When matched against human blood plasma database, the detected IMS-TOF features were found to yield approximately 700 unique peptide identifications at a false discovery rate (FDR) of approximately 7.5%. Accounting for IMS information gave rise to a projected FDR of approximately 4%. Signal reproducibility was found to be greater than 80%, while the variations in the number of unique peptide identifications were <15%. A single sample analysis was completed in 15 min that constitutes almost 1 order of magnitude improvement compared to a more conventional LC-MS approach.

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.