Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells.

To identify proteins by the bottom-up mass spectrometry workflow, enzymatic digestion is essential to break down proteins into smaller peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity and generation of peptides with desirable positively charged N- and C-terminal amino acid residues that are amenable to reverse phase HPLC separation and MS/MS analyses. However, trypsin can yield variable digestion profiles and its protein cleavage activity is interdependent on trypsin source and quality, digestion time and temperature, pH, denaturant, trypsin and substrate concentrations, composition/complexity of the sample matrix, and other factors. There is therefore a need for a more standardized, general-purpose trypsin digestion protocol. Based on a review of the literature we delineate optimal conditions for carrying out trypsin digestions of complex proteomes from bulk samples to limiting amounts of protein extracts. Furthermore, we highlight recent developments and technological advances used in digestion protocols to quantify complex proteomes from single cells. SIGNIFICANCE: Currently, bottom-up MS-based proteomics is the method of choice for global proteome analysis. Since trypsin is the most utilized protease in bottom-up MS proteomics, delineating optimal conditions for carrying out trypsin digestions of complex proteomes in samples ranging from tissues to single cells should positively impact a broad range of biomedical research.

Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome.

Substance use disorders are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based quantitative proteomics, along with a data-independent acquisition analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveal cocaine and morphine differentially alter diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between the phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome. The proteomics data in this study are available via ProteomeXchange with identifier PXD042043.

Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome.

Substance use disorders (SUDs) are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based (LC-MS/MS) quantitative proteomics, along with a data-independent acquisition (DIA) analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveals cocaine and morphine differentially alters diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome.

Deciphering Spatial Protein-Protein Interactions in Brain Using Proximity Labeling.

Cellular biomolecular complexes including protein-protein, protein-RNA, and protein-DNA interactions regulate and execute most biological functions. In particular in brain, protein-protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell-cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte-neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.

Exosomes as Emerging Biomarker Tools in Neurodegenerative and Neuropsychiatric Disorders-A Proteomics Perspective.

Exosomes are synthesized and secreted by different cell types and contain proteins, lipids, metabolites and RNA species that reflect the physiological status of the cell of origin. As such, exosomes are increasingly being used as a novel reservoir for disease biomarker discovery. However, isolation of exosomes can be challenging due to their nonuniformity of shape and variable tissue of origin. Moreover, various analytical techniques used for protein detection and quantitation remain insensitive to the low amounts of protein isolated from exosomes. Despite these challenges, techniques to improve proteomic yield and increase protein dynamic range continue to improve at a rapid rate. In this review, we highlight the importance of exosome proteomics in neurodegenerative and neuropsychiatric disorders and the associated technical difficulties. Furthermore, current progress and technological advancements in exosome proteomics research are discussed with an emphasis on disease-associated protein biomarkers.

Cerebrospinal fluid proteome evaluation in major depressive disorder by mass spectrometry.

BACKGROUND: Depression affects approximately 7.1% of the United States population every year and has an annual economic burden of over $210 billion dollars. Several recent studies have sought to investigate the pathophysiology of depression utilizing focused cerebrospinal fluid (CSF) and serum analysis. Inflammation and metabolic dysfunction have emerged as potential etiological factors from these studies. A dysregulation in the levels of inflammatory proteins such as IL-12, TNF, IL-6 and IFN-γ have been found to be significantly correlated with depression. METHODS: CSF samples were obtained from 15 patients, seven with major depressive disorder and eight age- and gender-matched non-psychiatric controls. CSF protein profiles were obtained using quantitative mass spectrometry. The data were analyzed by Progenesis QI proteomics software to identify significantly dysregulated proteins. The results were subjected to bioinformatics analysis using the Ingenuity Pathway Analysis suite to obtain unbiased mechanistic insight into biologically relevant interactions and pathways. RESULTS: Several dysregulated proteins were identified. Bioinformatics analysis indicated that the potential disorder/disease pathways include inflammatory response, metabolic disease and organismal injury. Molecular and cellular functions that were affected include cellular compromise, cell-to-cell signaling & interaction, cellular movement, protein synthesis, and cellular development. The major canonical pathway that was upregulated was acute phase response signaling. Endogenous upstream regulators that may influence dysregulation of proinflammatory molecules associated with depression are interleukin-6 (IL-6), signal transducer and activator of transcription 3 (STAT3), oncostatin M, PR domain zinc finger protein 1 (PRDM1), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A). CONCLUSIONS: The proteome profiling data in this report identifies several potential biological functions that may be involved in the pathophysiology of major depressive disorder. Future research into how the differential expression of these proteins is involved in the etiology and severity of depression will be important.

Differential Protein Expression in Striatal D1- and D2-Dopamine Receptor-Expressing Medium Spiny Neurons.

Many neurological disorders and diseases including drug addiction are associated with specific neuronal cell types in the brain. The striatum, a region that plays a critically important role in the development of addictive drug-related behavior, provides a good example of the cellular heterogeneity challenges associated with analyses of specific neuronal cell types. Such studies are needed to identify the adaptive changes in neuroproteomic signaling that occur in response to diseases such as addiction. The striatum contains two major cell types, D1 and D2 type dopaminoceptive medium spiny neurons (MSNs), whose cell bodies and processes are intermingled throughout this region. Since little is known about the proteomes of these two neuronal cell populations, we have begun to address this challenge by using fluorescence-activated nuclear sorting (FANS) to isolate nuclei-containing fractions from striatum from D1 and D2 "Translating Ribosome Affinity Purification" (TRAP) mice. This approach enabled us to devise and implement a robust and reproducible workflow for preparing samples from specific MSN cell types for mass spectrometry analyses. These analyses quantified at least 685 proteins in each of four biological replicates of 50 K sorted nuclei from two D1 mice/replicate and from each of four biological replicates of 50 K sorted nuclei from two D2 mice/replicate. Proteome analyses identified 87 proteins that were differentially expressed in D1 versus D2 MSN nuclei and principal component analysis (PCA) of these proteins separated the 8 biological replicates into specific cell types. Central network analysis of the 87 differentially expressed proteins identified Hnrnpd and Hnmpa2b1 in D1 and Cct2 and Cct7 in D2 as potential central interactors. This workflow can now be used to improve our understanding of many neurological diseases including characterizing the short and long-term impact of drugs of abuse on the proteomes of these two dopaminoceptive neuronal populations.

Editorial for Special Issue: Neuroproteomics.

Recent advances in mass spectrometry (MS) instrumentation [...].

Development of Targeted Mass Spectrometry-Based Approaches for Quantitation of Proteins Enriched in the Postsynaptic Density (PSD).

The postsynaptic density (PSD) is a structural, electron-dense region of excitatory glutamatergic synapses, which is involved in a variety of cellular and signaling processes in neurons. The PSD is comprised of a large network of proteins, many of which have been implicated in a wide variety of neuropsychiatric disorders. Biochemical fractionation combined with mass spectrometry analyses have enabled an in-depth understanding of the protein composition of the PSD. However, the PSD composition may change rapidly in response to stimuli, and robust and reproducible methods to thoroughly quantify changes in protein abundance are warranted. Here, we report on the development of two types of targeted mass spectrometry-based assays for quantitation of PSD-enriched proteins. In total, we quantified 50 PSD proteins in a targeted, parallel reaction monitoring (PRM) assay using heavy-labeled, synthetic internal peptide standards and identified and quantified over 2100 proteins through a pre-determined spectral library using a data-independent acquisition (DIA) approach in PSD fractions isolated from mouse cortical brain tissue.

Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq.

Cellular control of gene expression is a complex process that is subject to multiple levels of regulation, but ultimately it is the protein produced that determines the biosynthetic state of the cell. One way that a cell can regulate the protein output from each gene is by expressing alternate isoforms with distinct amino acid sequences. These isoforms may exhibit differences in localization and binding interactions that can have profound functional implications. High-throughput liquid chromatography tandem mass spectrometry proteomics (LC-MS/MS) relies on enzymatic digestion and has lower coverage and sensitivity than transcriptomic profiling methods such as RNA-seq. Digestion results in predictable fragmentation of a protein, which can limit the generation of peptides capable of distinguishing between isoforms. Here we exploit transcript-level expression from RNA-seq to set prior likelihoods and enable protein isoform abundances to be directly estimated from LC-MS/MS, an approach derived from the principle that most genes appear to be expressed as a single dominant isoform in a given cell type or tissue. Through this deep integration of RNA-seq and LC-MS/MS data from the same sample, we show that a principal isoform can be identified in >80% of gene products in homogeneous HEK293 cell culture and >70% of proteins detected in complex human brain tissue. We demonstrate that the incorporation of translatome data from ribosome profiling further refines this process. Defining isoforms in experiments with matched RNA-seq/translatome and proteomic data increases the functional relevance of such data sets and will further broaden our understanding of multilevel control of gene expression.

Data-Independent Acquisition and Parallel Reaction Monitoring Mass Spectrometry Identification of Serum Biomarkers for Ovarian Cancer.

A data-independent acquisition (DIA)/parallel reaction monitoring (PRM) workflow was implemented to identify improved ovarian cancer biomarkers. Data-independent acquisition on ovarian cancer versus control sera and literature searches identified 50 biomarkers and indicated that apolipoprotein A-IV (ApoA-IV) is the most significantly differentially regulated protein. Parallel reaction monitoring with Targeted Ovarian Cancer Proteome Assay validated differential ApoA-IV expression and quantified 9 other biomarkers. Random Forest (RF) analyses achieved 92.3% classification accuracy and confirmed ApoA-IV as the leading biomarker. Indeed, all samples were classified correctly with an [ApoA-IV] breakpoint. The next best biomarkers were C-reactive protein, transferrin, and transthyretin. The Targeted Ovarian Cancer Proteome Assay suggests that ApoA-IV is a more reliable biomarker than had been determined by immunological assays and it is a better biomarker than ApoA-I, which is in the OVA1 test for ovarian cancer. This research provides a PRM/RF approach together with 4 promising biomarkers to speed the development of a clinical assay for ovarian cancer.

Use of a Targeted Urine Proteome Assay (TUPA) to identify protein biomarkers of delayed recovery after kidney transplant.

PURPOSE: Development of delayed graft function (DGF) following kidney transplant is associated with poor outcomes. An ability to rapidly identify patients with DGF versus those with immediate graft function (IGF) may facilitate the treatment of DGF and the research needed to improve prognosis. The purpose of this study was to use a Targeted Urine Proteome Assay to identify protein biomarkers of delayed recovery from kidney transplant. EXPERIMENTAL DESIGN: Potential biomarkers were identified using the Targeted Urine Proteome (MRM) Assay to interrogate the relative DGF/IGF levels of expression of 167 proteins in urine taken 12-18 h after kidney implantation from 21 DGF, 15 SGF (slow graft function), and 16 IGF patients. An iterative Random Forest analysis approach evaluated the relative importance of each biomarker, which was then used to identify an optimum biomarker panel that provided the maximum sensitivity and specificity with the least number of biomarkers. CONCLUSIONS AND CLINICAL RELEVANCE: Four proteins were identified that together distinguished DGF with a sensitivity of 77.4%, specificity of 82.6%, and AUC of 0.891. This panel represents an important step toward identifying DGF at an early stage so that more effective treatments can be developed to improve long-term graft outcomes.

Development of a Targeted Urine Proteome Assay for kidney diseases.

PURPOSE: Since human urine is the most readily available biofluid whose proteome changes in response to disease, it is a logical sample for identifying protein biomarkers for kidney diseases. EXPERIMENTAL DESIGN: Potential biomarkers were identified by using a multiproteomics workflow to compare urine proteomes of kidney transplant patients with immediate and delayed graft function. Differentially expressed proteins were identified, and corresponding stable isotope labeled internal peptide standards were synthesized for scheduled MRM. RESULTS: The Targeted Urine Proteome Assay (TUPA) was then developed by identifying those peptides for which there were at least two transitions for which interference in a urine matrix across 156 MRM runs was <30%. This resulted in an assay that monitors 224 peptides from 167 quantifiable proteins. CONCLUSIONS AND CLINICAL RELEVANCE: TUPA opens the way for using a robust mass spectrometric technology, MRM, for quantifying and validating biomarkers from among 167 urinary proteins. This approach, while developed using differentially expressed urinary proteins from patients with delayed versus immediate graft function after kidney transplant, can be expanded to include differentially expressed urinary proteins in multiple kidney diseases. Thus, TUPA could provide a single assay to help diagnose, prognose, and manage many kidney diseases.

Antipurinergic therapy corrects the autism-like features in the Fragile X (Fmr1 knockout) mouse model.

BACKGROUND: This study was designed to test a new approach to drug treatment of autism spectrum disorders (ASDs) in the Fragile X (Fmr1) knockout mouse model. METHODS: We used behavioral analysis, mass spectrometry, metabolomics, electron microscopy, and western analysis to test the hypothesis that the disturbances in social behavior, novelty preference, metabolism, and synapse structure are treatable with antipurinergic therapy (APT). RESULTS: Weekly treatment with the purinergic antagonist suramin (20 mg/kg intraperitoneally), started at 9 weeks of age, restored normal social behavior, and improved metabolism, and brain synaptosomal structure. Abnormalities in synaptosomal glutamate, endocannabinoid, purinergic, and IP3 receptor expression, complement C1q, TDP43, and amyloid ß precursor protein (APP) were corrected. Comprehensive metabolomic analysis identified 20 biochemical pathways associated with symptom improvements. Seventeen pathways were shared with human ASD, and 11 were shared with the maternal immune activation (MIA) model of ASD. These metabolic pathways were previously identified as functionally related mediators of the evolutionarily conserved cell danger response (CDR). CONCLUSIONS: The data show that antipurinergic therapy improves the multisystem, ASD-like features of both the environmental MIA, and the genetic Fragile X models. These abnormalities appeared to be traceable to mitochondria and regulated by purinergic signaling.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research.

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins.

We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC-MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay.

YPED: a web-accessible database system for protein expression analysis.

We have developed an integrated web-accessible software system called the Yale Protein Expression Database (YPED) to address the need for storage, retrieval, and integrated analysis of large amounts of data from high throughput proteomic technologies. YPED is an open source system which integrates gel analysis results with protein identifications from DIGE experiments. The system associates the DIGE gel spots and image, analyzed with DeCyder, with mass spectrometric protein identifications from selected gel spots. Following in gel trypsin digestion, proteins in spots of interest are analyzed using MALDI-TOF/TOF on an AB 4700 or, more recently, on an AB 4800 with protein identifications performed by Mascot in conjunction with the AB GPS Explorer system. In addition to DIGE, YPED currently handles protein identifications from MudPIT, iTRAQ, and ICAT experiments. Sample descriptions are compatible with the evolving MIAPE standards. Tandem MS/MS results from MudPIT, and ICAT analyses are validated with the Trans-Proteomic Pipeline and then stored in the database for viewing and linking to the identified proteins. Researchers can view, subset, and download their data through a secure Web interface that includes a table containing proteins identified, a sample summary, the sample description, and a clickable gel image for DIGE samples. Tools are available to facilitate sample comparison and the viewing of phosphoproteins. A summary report with PANTHER Classification System annotations is also available to aid in biological interpretation of the results. The source code is open-source and is available from http://yped.med.yale.edu/yped_dist.

Identification of proteins based on MS/MS spectra and location of posttranslational modifications.

Comparative protein profiling is a key approach to understanding the human and other proteomes. Systems-level profiling technologies, such as differential fluorescence two-dimensional gel electrophoresis (DIGE), often require the identification of the proteins that are contained within 50 or more spots per gel. A major focus of this chapter therefore is devoted to a general approach for high throughput protein identification that is based on liquid chromatography (LC)/tandem mass spectrometry (MS/MS) analysis of tryptic digests of individual proteins or mixtures of only a few proteins (i.e., as are usually obtained from individual DIGE spots), and that is also applicable to the analysis of complex protein extracts. Additionally, multiple techniques will be described for identifying sites of protein posttranslational modification, with emphasis on phosphorylation and Arg methylation.

Multiple peak alignment in sequential data analysis: a scale-space-based approach.

In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).