Meta-analysis for protein identification: a case study on yeast data

Thorough Performance Evaluation of 213 nm Ultraviolet Photodissociation for Top-down Proteomics

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

Multidimensional Top-Down Proteomics of Brain-Region-Specific Mouse Brain Proteoforms Responsive to Cocaine and Estradiol

Cocaine addiction afflicts nearly 1 million adults in the United States, and to date, there are no known treatments approved for this psychiatric condition. Women are particularly vulnerable to developing a cocaine use disorder and suffer from more serious cardiac consequences than men when using cocaine. Estrogen is one biological factor contributing to the increased risk for females to develop problematic cocaine use. Animal studies have demonstrated that estrogen (17β-estradiol or E2) enhances the rewarding properties of cocaine. Although E2 affects the dopamine system, the molecular and cellular mechanisms of E2-enhanced cocaine reward have not been characterized. In this study, quantitative top-down proteomics was used to measure intact proteins in specific regions of the female mouse brain after mice were trained for cocaine-conditioned place preference, a behavioral test of cocaine reward. Several proteoform changes occurred in the ventral tegmental area after combined cocaine and E2 treatments, with the most numerous proteoform alterations on myelin basic protein, indicating possible changes in white matter structure. There were also changes in histone H4, protein phosphatase inhibitors, cholecystokinin, and calmodulin proteoforms. These observations provide insight into estrogen signaling in the brain and may guide new approaches to treating women with cocaine use disorder.

A comprehensive pipeline for translational top-down proteomics from a single blood draw

Top-down proteomics (TDP) by mass spectrometry (MS) is a technique by which intact proteins are analyzed. It has become increasingly popDesalting and concentrating GELFrEEular in translational research because of the value of characterizing distinct proteoforms of intact proteins. Compared to bottom-up proteomics (BUP) strategies, which measure digested peptide mixtures, TDP provides highly specific molecular information that avoids the bioinformatic challenge of protein inference. However, the technique has been difficult to implement widely because of inherent limitations of existing sample preparation methods and instrumentation. Recent improvements in proteoform pre-fractionation and the availability of high-resolution benchtop mass spectrometers have made it possible to use high-throughput TDP for the analysis of complex clinical samples. Here, we provide a comprehensive protocol for analysis of a common sample type in translational research: human peripheral blood mononuclear cells (PBMCs). The pipeline comprises multiple workflows that can be treated as modular by the reader and used for various applications. First, sample collection and cell preservation are described for two clinical biorepository storage schemes. Cell lysis and proteoform pre-fractionation by gel-eluted liquid fractionation entrapment electrophoresis are then described. Importantly, instrument setup and liquid chromatography-tandem MS are described for TDP analyses, which rely on high-resolution Fourier-transform MS. Finally, data processing and analysis are described using two different, application-dependent software tools: ProSight Lite for targeted analyses of one or a few proteoforms and TDPortal for high-throughput TDP in discovery mode. For a single sample, the minimum completion time of the entire experiment is 72 h.

Proteoforms in Peripheral Blood Mononuclear Cells as Novel Rejection Biomarkers in Liver Transplant Recipients

Biomarker profiles of acute rejection in liver transplant recipients could enhance the diagnosis and management of recipients. Our aim was to identify diagnostic proteoform signatures of acute rejection in circulating immune cells, using an emergent "top-down" proteomics methodology. We prepared differentially processed and cryopreserved cell lysates from 26 nonviral liver transplant recipients by molecular weight-based fractionation and analyzed them by mass spectrometry of whole proteins in three steps: (i) Nanocapillary liquid chromatography coupled with high-resolution tandem mass spectrometry; (ii) database searching to identify and characterize intact proteoforms; (iii) data processing through a hierarchical linear model matching the study design to quantify proteoform fold changes in patients with rejection versus normal liver function versus acute dysfunction without rejection. Differentially expressed proteoforms were seen in patients with rejection versus normal and nonspecific controls, most evidently in the cell preparations stored in traditional serum-rich media. Mapping analysis of these proteins back to genes through gene ontology and pathway analysis tools revealed multiple signaling pathways, including inflammation mediated by cytokines and chemokines. Larger studies are needed to validate these novel rejection signatures and test their predictive value for use in clinical management.

Comparative top down proteomics of peripheral blood mononuclear cells from kidney transplant recipients with normal kidney biopsies or acute rejection

Recent studies utilizing transcriptomics, metabolomics, and bottom up proteomics have identified molecular signatures of kidney allograft pathology. Although these results make significant progress toward non-invasive differential diagnostics of dysfunction of a transplanted kidney, they provide little information on the intact, often modified, protein molecules present during progression of this pathology. Because intact proteins underpin diverse biological processes, measuring the relative abundance of their modified forms promises to advance mechanistic understanding, and might provide a new class of biomarker candidates. Here, we used top down proteomics to inventory the modified forms of whole proteins in peripheral blood mononuclear cells (PBMCs) taken at the time of kidney biopsy for 40 kidney allograft recipients either with healthy transplants or those suffering acute rejection. Supported by gas-phase fragmentation of whole protein ions during tandem mass spectrometry, we identified 344 proteins mapping to 2905 distinct molecular forms (proteoforms). Using an initial implementation of a label-free approach to quantitative top down proteomics, we obtained evidence suggesting relative abundance changes in 111 proteoforms between the two patient groups. Collectively, our work is the first to catalog intact protein molecules in PBMCs and suggests differentially abundant proteoforms for further analysis.

High-Throughput Analysis of Intact Human Proteins Using UVPD and HCD on an Orbitrap Mass Spectrometer

The analysis of intact proteins (top-down strategy) by mass spectrometry has great potential to elucidate proteoform variation, including patterns of post-translational modifications (PTMs), which may not be discernible by analysis of peptides alone (bottom-up approach). To maximize sequence coverage and localization of PTMs, various fragmentation modes have been developed to produce fragment ions from deep within intact proteins. Ultraviolet photodissociation (UVPD) has recently been shown to produce high sequence coverage and PTM retention on a variety of proteins, with increasing evidence of efficacy on a chromatographic time scale. However, utilization of UVPD for high-throughput top-down analysis to date has been limited by bioinformatics. Here we detected 153 proteins and 489 proteoforms using UVPD and 271 proteins and 982 proteoforms using higher energy collisional dissociation (HCD) in a comparative analysis of HeLa whole-cell lysate by qualitative top-down proteomics. Of the total detected proteoforms, 286 overlapped between the UVPD and HCD data sets, with 68% of proteoforms having C scores greater than 40 for UVPD and 63% for HCD. The average sequence coverage (28 ± 20% for UVPD versus 17 ± 8% for HCD, p < 0.0001) was found to be higher for UVPD than HCD and with a trend toward improvement in q value for the UVPD data set. This study demonstrates the complementarity of UVPD and HCD for more extensive protein profiling and proteoform characterization.

Identification and Characterization of Human Proteoforms by Top-Down LC-21 Tesla FT-ICR Mass Spectrometry

Successful high-throughput characterization of intact proteins from complex biological samples by mass spectrometry requires instrumentation capable of high mass resolving power, mass accuracy, sensitivity, and spectral acquisition rate. These limitations often necessitate the performance of hundreds of LC-MS/MS experiments to obtain reasonable coverage of the targeted proteome, which is still typically limited to molecular weights below 30 kDa. The National High Magnetic Field Laboratory (NHMFL) recently installed a 21 T FT-ICR mass spectrometer, which is part of the NHMFL FT-ICR User Facility and available to all qualified users. Here we demonstrate top-down LC-21 T FT-ICR MS/MS of intact proteins derived from human colorectal cancer cell lysate. We identified a combined total of 684 unique protein entries observed as 3238 unique proteoforms at a 1% false discovery rate, based on rapid, data-dependent acquisition of collision-induced and electron-transfer dissociation tandem mass spectra from just 40 LC-MS/MS experiments. Our identifications included 372 proteoforms with molecular weights over 30 kDa detected at isotopic resolution, which substantially extends the accessible mass range for high-throughput top-down LC-MS/MS.

Advancing Top-down Analysis of the Human Proteome Using a Benchtop Quadrupole-Orbitrap Mass Spectrometer

Over the past decade, developments in high resolution mass spectrometry have enabled the high throughput analysis of intact proteins from complex proteomes, leading to the identification of thousands of proteoforms. Several previous reports on top-down proteomics (TDP) relied on hybrid ion trap-Fourier transform mass spectrometers combined with data-dependent acquisition strategies. To further reduce TDP to practice, we use a quadrupole-Orbitrap instrument coupled with software for proteoform-dependent data acquisition to identify and characterize nearly 2000 proteoforms at a 1% false discovery rate from human fibroblasts. By combining a 3 m/z isolation window with short transients to improve specificity and signal-to-noise for proteoforms >30 kDa, we demonstrate improving proteome coverage by capturing 439 proteoforms in the 30-60 kDa range. Three different data acquisition strategies were compared and resulted in the identification of many proteoforms not observed in replicate data-dependent experiments. Notably, the data set is reported with updated metrics and tools including a new viewer and assignment of permanent proteoform record identifiers for inclusion of highly characterized proteoforms (i.e., those with C-scores >40) in a repository curated by the Consortium for Top-Down Proteomics.

Unraveling the Complexities of Life Sciences Data

The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.

MOPED: Model Organism Protein Expression Database

Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43,000 proteins with at least one spectral match and more than 11 million high certainty spectra.

IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.

SPIRE: Systematic protein investigative research environment

The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data.

metaXCMS: second-order analysis of untargeted metabolomics data

Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distinguishing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

The necessity of adjusting tests of protein category enrichment in discovery proteomics

MOTIVATION

Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test is commonly used. We specifically examined the associations produced by the Fisher test between protein identification by mass spectrometry discovery proteomics, and their Gene Ontology (GO) term assignments in a large yeast dataset. We found that direct application of the Fisher test is misleading in proteomics due to the bias in mass spectrometry to preferentially identify proteins based on their biochemical properties. False inference about associations can be made if this bias is not corrected. Our method adjusts Fisher tests for these biases and produces associations more directly attributable to protein expression rather than experimental bias.

RESULTS

Using logistic regression, we modeled the association between protein identification and GO term assignments while adjusting for identification bias in mass spectrometry. The model accounts for five biochemical properties of peptides: (i) hydrophobicity, (ii) molecular weight, (iii) transfer energy, (iv) beta turn frequency and (v) isoelectric point. The model was fit on 181 060 peptides from 2678 proteins identified in 24 yeast proteomics datasets with a 1% false discovery rate. In analyzing the association between protein identification and their GO term assignments, we found that 25% (134 out of 544) of Fisher tests that showed significant association (q-value ≤0.05) were non-significant after adjustment using our model. Simulations generating yeast protein sets enriched for identification propensity show that unadjusted enrichment tests were biased while our approach worked well.