Elamipretide effects on the skeletal muscle phosphoproteome in aged female mice.

The age-related decline in skeletal muscle mass and function is known as sarcopenia. Sarcopenia progresses based on complex processes involving protein dynamics, cell signaling, oxidative stress, and repair. We have previously found that 8-week treatment with elamipretide improves skeletal muscle function, reverses redox stress, and restores protein S-glutathionylation changes in aged female mice. This study tested whether 8-week treatment with elamipretide also affects global phosphorylation in skeletal muscle consistent with functional improvements and S-glutathionylation. Using female 6-7-month-old mice and 28-29-month-old mice, we found that phosphorylation changes did not relate to S-glutathionylation modifications, but that treatment with elamipretide did partially reverse age-related changes in protein phosphorylation in mouse skeletal muscle.

Improving mitochondrial function with SS-31 reverses age-related redox stress and improves exercise tolerance in aged mice.

Sarcopenia and exercise intolerance are major contributors to reduced quality of life in the elderly for which there are few effective treatments. We tested whether enhancing mitochondrial function and reducing mitochondrial oxidant production with SS-31 (elamipretide) could restore redox balance and improve skeletal muscle function in aged mice. Young (5 mo) and aged (26 mo) female C57BL/6Nia mice were treated for 8-weeks with 3 mg/kg/day SS-31. Mitochondrial function was assessed in vivo using 31P and optical spectroscopy. SS-31 reversed age-related decline in maximum mitochondrial ATP production (ATPmax) and coupling of oxidative phosphorylation (P/O). Despite the increased in vivo mitochondrial capacity, mitochondrial protein expression was either unchanged or reduced in the treated aged mice and respiration in permeabilized gastrocnemius (GAS) fibers was not different between the aged and aged+SS-31 mice. Treatment with SS-31 also restored redox homeostasis in the aged skeletal muscle. The glutathione redox status was more reduced and thiol redox proteomics indicated a robust reversal of cysteine S-glutathionylation post-translational modifications across the skeletal muscle proteome. The gastrocnemius in the age+SS-31 mice was more fatigue resistant with significantly greater mass compared to aged controls. This contributed to a significant increase in treadmill endurance compared to both pretreatment and untreated control values. These results demonstrate that the shift of redox homeostasis due to mitochondrial oxidant production in aged muscle is a key factor in energetic defects and exercise intolerance. Treatment with SS-31 restores redox homeostasis, improves mitochondrial quality, and increases exercise tolerance without an increase in mitochondrial content. Since elamipretide is currently in clinical trials these results indicate it may have direct translational value for improving exercise tolerance and quality of life in the elderly.

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows.

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

Data-Independent Acquisition Mass Spectrometry To Quantify Protein Levels in FFPE Tumor Biopsies for Molecular Diagnostics.

Mass spectrometry-based protein quantitation is currently used to measure therapeutically relevant protein biomarkers in CAP/CLIA setting to predict likely responses of known therapies. Selected reaction monitoring (SRM) is the method of choice due to its outstanding analytical performance. However, data-independent acquisition (DIA) is now emerging as a proteome-scale clinical assay. We evaluated the ability of DIA to profile the patient-specific proteomes of sample-limited tumor biopsies and to quantify proteins of interest in a targeted fashion using formalin-fixed, paraffin-embedded (FFPE) tumor biopsies ( n = 12) selected from our clinical laboratory. DIA analysis on the tumor biopsies provided 3713 quantifiable proteins including actionable biomarkers currently in clinical use, successfully separated two gastric cancers from colorectal cancer specimen solely on the basis of global proteomic profiles, and identified subtype-specific proteins with prognostic or diagnostic value. We demonstrate the potential use of DIA-based quantitation to inform therapeutic decision-making using TUBB3, for which clinical cutoff expression levels have been established by SRM. Comparative analysis of DIA-based proteomic profiles and mRNA expression levels found positively and negatively correlated protein-gene pairs, a finding consistent with previously reported results from fresh-frozen tumor tissues.

Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry.

Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20-25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone.

Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions.

Recent advances in ion mobility spectrometry (IMS) have illustrated its power in determining the structural characteristics of a molecule, especially when coupled with other separations dimensions such as liquid chromatography (LC) and mass spectrometry (MS). However, these three separation techniques together greatly complicate data analyses, making better informatics tools essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-CID-MS data from numerous instrument vendor datasets and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the initial evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast protein digest at seven different concentrations, and Skyline was able to rapidly analyze the MS and CID-MS data for 38 of the BSA peptides. Calibration curves for the precursor and fragment ions were assessed with and without the IMS dimension. In all cases, addition of the IMS dimension removed noise from co-eluting peptides with close m/z values, resulting in calibration curves with greater linearity and lower detection limits. This study presents an important informatics development since to date LC-IMS-CID-MS data from the different instrument vendors is often assessed manually and cannot be analyzed quickly. Because these evaluations require days for the analysis of only a few target molecules in a limited number of samples, it is unfeasible to evaluate hundreds of targets in numerous samples. Thus, this study showcases Skyline's ability to work with the multidimensional LC-IMS-CID-MS data and provide biological and environmental insights rapidly. Graphical Abstract ᅟ.

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics.

Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays.

The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on nonlinear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations and using eleven experimental data sets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the nonlinear model changes the estimates of LOB/LOD upwards, in some data sets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with nonlinear statistical modeling were identical to those of weighted linear regression. We implemented the nonlinear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.

PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.

Development of Selected Reaction Monitoring Methods to Systematically Quantify Kinase Abundance and Phosphorylation Stoichiometry in Human Samples.

Protein phosphorylation, one of the most common types of post-translational modifications, is the central regulatory mechanism of cellular signaling networks. In human cells, thousands of proteins are continuously and dynamically phosphorylated and dephosphorylated at specific sites and times in response to external and internal stimuli. Reversible phosphorylation is facilitated by the action of two protein superfamilies: kinases and phosphatases. Kinases play an essential role in almost every relevant physiological process in human cells and their deregulation is linked to pathologies ranging from cancer to autoimmune diseases.Systematic identification of kinases expressed in a particular cell type, quantification of their abundance, and precise determination of their phosphorylation stoichiometry are essential to understand the cellular signaling networks and physiology of a sample. Our protocol outlines the steps to build and use a high-throughput, comprehensive, modular, and robust selected reaction monitoring (SRM) proteomics framework to facilitate quantification of the kinome state in research or clinical human samples.

Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.

In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data.

In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments.

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.

A deeper look into Comet--implementation and features.

The Comet database search software was initially released as an open source project in late 2012. Prior to that, Comet existed as the University of Washington's academic version of the SEQUEST database search tool. Despite its availability and widespread use over the years, some details about its implementation have not been previously disseminated or are not well understood. We address a few of these details in depth and highlight new features available in the latest release. Comet is freely available for download at http://comet-ms.sourceforge.net or it can be accessed as a component of a number of larger software projects into which it has been incorporated. Graphical Abstract ᅟ.

Multiplexed peptide analysis using data-independent acquisition and Skyline.

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

Multiplexed MS/MS for improved data-independent acquisition.

In mass spectrometry-based proteomics, data-independent acquisition (DIA) strategies can acquire a single data set useful for both identification and quantification of detectable peptides in a complex mixture. However, DIA data are noisy owing to a typical five- to tenfold reduction in precursor selectivity compared to data obtained with data-dependent acquisition or selected reaction monitoring. We demonstrate a multiplexing strategy, MSX, for DIA analysis that increases precursor selectivity fivefold.

Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders.

Exome sequencing studies of autism spectrum disorders (ASDs) have identified many de novo mutations but few recurrently disrupted genes. We therefore developed a modified molecular inversion probe method enabling ultra-low-cost candidate gene resequencing in very large cohorts. To demonstrate the power of this approach, we captured and sequenced 44 candidate genes in 2446 ASD probands. We discovered 27 de novo events in 16 genes, 59% of which are predicted to truncate proteins or disrupt splicing. We estimate that recurrent disruptive mutations in six genes-CHD8, DYRK1A, GRIN2B, TBR1, PTEN, and TBL1XR1-may contribute to 1% of sporadic ASDs. Our data support associations between specific genes and reciprocal subphenotypes (CHD8-macrocephaly and DYRK1A-microcephaly) and replicate the importance of a ß-catenin-chromatin-remodeling network to ASD etiology.

De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics.

We report an algorithm designed for the calibration of low resolution peptide mass spectra. Our algorithm is implemented in a program called FineTune, which corrects systematic mass measurement error in 1 min, with no input required besides the mass spectra themselves. The mass measurement accuracy for a set of spectra collected on an LTQ-Velos improved 20-fold from -0.1776 ± 0.0010 m/z to 0.0078 ± 0.0006 m/z after calibration (avg ± 95 % confidence interval). The precision in mass measurement was improved due to the correction of non-linear variation in mass measurement accuracy across the m/z range.