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The ocular lens proteome undergoes post-translational and progressive degradation as fiber cells age. The oldest fiber cells and the proteins therein are present at birth and are retained through death. Transparency of the lens is maintained in part by the high abundance Crystallin family proteins (up to 300 mg/mL), which establishes a high dynamic range of protein abundance. As a result, previous data-dependent analysis (DDA) measurements of the lens proteome are less equipped to identify the lowest abundance proteins. To probe more deeply into the lens proteome, we measured the insoluble lens proteome of an 18-year-old human with DDA and data-independent analysis (DIA) methods. By applying more recent library-free DIA search methods, 5,161 protein groups, 50,386 peptides, and 4,960 deamidation sites were detected: significantly outperforming the quantity of identifications in using DDA and pan-human DIA library searches. Finally, by segmenting the lens into multiple fiber cell-age-related regions, we uncovered cell-age-related changes in proteome composition and putative function.

Novel psychoactive substances (NPS) are constantly emerging in the drug market, and synthetic cannabinoids (SCs) are included in this NPS family. Forensic laboratories often struggle with these continually emerging SCs, forcing them to develop an untargeted workflow to incorporate these psychoactive drugs in their procedures. Usually, forensic laboratories select analytical methods based on targeted mass spectrometry (MS) technologies for strictly tracking already known NPS. The appropriate way to tackle unknown substances is to develop pipelines for untargeted analysis that include LC-HRMS analytical methods and data analysis. Once established, this strategy would allow drug testing laboratories to be always one step ahead of the new trends concerning the "designer drugs" market. To address this challenge an untargeted workflow based on mass spectrometry data acquisition and data analysis was developed to detect SCs in oral fluid (OF) samples at a low concentration range. The samples were extracted by mixed-mode solid-phase extraction and analyzed by Liquid Chromatography - High-Resolution Mass Spectrometry (LC-HRMS). Tandem mass spectra (MS2) were recorded performing a variable isolation width across a mass range of all theoretical precursor ions (vDIA) after the chromatographic separation. After raw data processing with the MSDial software, the deconvoluted features were sent to GNPS for Feature-Based Molecular Networking (FBMN) construction for nontargeted data mining. The FBMN analysis created a unique integrated network for most of the SCs assessed in the OF at a low level (20 ng/mL). These results demonstrate the potential of an untargeted approach to detect different derivatives of SCs at trace levels for forensic applications.

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not. To assess coelution, we performed a parallel LC-MS/MS analysis in data-independent acquisition (DIA) with all-ion-fragmentation (AIF) mode. Using the DIA (AIF) data, peak-peak correlation (PPC) score is calculated between the extracted ion chromatogram (EIC) of the fragment ion using the MS/MS scans and the EIC of the precursor ion using the MS1 scans. A high PPC score is an indication of TFIs, and a low PPC score is an indication of CFIs. Tested using metabolomics data generated by high resolution QTOF and Orbitrap MS from various vendors in different LC-MS configurations, we found that more than 70% of the fragment ions have PPC scores < 0.8 and identified three common sources of CFIs, including (1) solvent contamination, (2) adjacent chemical contamination, and (3) undetermined signals from artifacts and noise. Combining PPC scores with other precursor and fragment ion information, we further developed a machine learning model that can robustly and conservatively predict CFIs. Incorporating the machine learning model, we created an R program, MS2Purifier, to automatically recognize CFIs and clean MS/MS spectra of metabolic features in LC-MS/MS data with high sensitivity and specificity.

A novel approach for phenotype prediction is developed for data-independent acquisition (DIA) mass spectrometric (MS) data without the need for peptide precursor identification using existing DIA software tools. The first step converts the DIA-MS data file into a new file format called DIA tensor (DIAT), which can be used for the convenient visualization of all the ions from peptide precursors and fragments. DIAT files can be fed directly into a deep neural network to predict phenotypes such as appearances of cats, dogs, and microscopic images. As a proof of principle, we applied this approach to 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in distinguishing malignant from benign samples. We further applied a refined model to classify thyroid nodules. Deep learning based on 492 training samples achieved an accuracy of 91.7% in an independent cohort of 216 test samples. This approach surpassed the deep-learning model based on peptide and protein matrices generated by OpenSWATH. In summary, we present a new strategy for DIA data analysis based on a novel data format called DIAT, which enables facile two-dimensional visualization of DIA proteomics data. DIAT files can be directly used for deep learning for biological and clinical phenotype classification. Future research will interpret the deep-learning models emerged from DIAT analysis.

Due to the technical advances of mass spectrometers, particularly increased scanning speed and higher MS/MS resolution, the use of data-independent acquisition mass spectrometry (DIA-MS) became more popular, which enables high reproducibility in both proteomic identification and quantification. The current DIA-MS methods normally cover a wide mass range, with the aim to target and identify as many peptides and proteins as possible and therefore frequently generate MS/MS spectra of high complexity. In this report, we assessed the performance and benefits of using small windows with, e.g., 5-m/z width across the peptide elution time. We further devised a new DIA method named RTwinDIA that schedules the small isolation windows in different retention time blocks, taking advantage of the fact that larger peptides are normally eluting later in reversed phase chromatography. We assessed the direct proteomic identification by using shotgun database searching tools such as MaxQuant and pFind, and also Spectronaut with an external comprehensive spectral library of human proteins. We conclude that algorithms like pFind have potential in directly analyzing DIA data acquired with small windows, and that the instrumental time and DIA cycle time, if prioritized to be spent on small windows rather than on covering a broad mass range by large windows, will improve the direct proteome coverage for new biological samples and increase the quantitative precision. These results further provide perspectives for the future convergence between DDA and DIA on faster MS analyzers.

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 Electronic supplementary material The online version of this article (10.1007/s13361-018-2122-8) contains supplementary material, which is available to authorized users.

The crustacean cardiac neuromuscular system is a useful model for studying how neural circuits generate behavior, as it is comprised of a simple ganglion containing nine neurons, yet acts as a robust central pattern generator. The crustacean heart is neurogenic, receiving input from neuropeptides. However, the specific effects of neuropeptides on cardiac output is not fully understood, and the large degree of comodulation between multiple neuropeptides makes studying these effects more challenging. To address this challenge, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) imaging was used to localize neuropeptides within the cardiac ganglion (CG), providing information about the identity and localization of neuropeptides being present. CG extracts were also profiled using liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) with a data independent acquisition (DIA) method, resulting in the confirmation of 316 neuropeptides. Two MS imaging (MSI) platforms were compared in order to provide comprehensive results, including a MALDI-Orbitrap instrument for high mass spectral resolution for accurate identifications and a MALDI-TOF/TOF for improved spatial resolution and sensitivity, providing more descriptive MS images. MS images for 235 putative neuropeptides were obtained, with the identification of 145 of these being confirmed by either complementary MS/MS data or accurate mass matching. The MSI studies demonstrate the sensitivity and power of MALDI-based in situ analytical strategy for unraveling the chemical complexity present in a small 9-cell neuronal system. The results of this study will enable more informative assays of the functions of neuropeptides within this important neural circuit.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is an important tool for measuring and monitoring protein structure. A bottom-up approach to HDX-MS provides peptide level deuterium uptake values and a more refined localization of deuterium incorporation compared with global HDX-MS measurements. The degree of localization provided by HDX-MS is proportional to the number of peptides that can be identified and monitored across an exchange experiment. Ion mobility spectrometry (IMS) has been shown to improve MS-based peptide analysis of biological samples through increased separation capacity. The integration of IMS within HDX-MS workflows has been commercialized but presently its adoption has not been widespread. The potential benefits of IMS, therefore, have not yet been fully explored. We herein describe a comprehensive evaluation of traveling wave ion mobility integrated within an online-HDX-MS system and present the first reported example of UDMSE acquisition for HDX analysis. Instrument settings required for optimal peptide identifications are described and the effects of detector saturation due to peak compression are discussed. A model system is utilized to confirm the comparability of HDX-IM-MS and HDX-MS uptake values prior to an evaluation of the benefits of IMS at increasing sample complexity. Interestingly, MS and IM-MS acquisitions were found to identify distinct populations of peptides that were unique to the respective methods, a property that can be utilized to increase the spatial resolution of HDX-MS experiments by >60%. Graphical Abstractᅟ Electronic supplementary material The online version of this article (doi:10.1007/s13361-017-1633-z) contains supplementary material, which is available to authorized users.

The goal in proteomics to identify all peptides in a complex mixture has been largely addressed using various LC MS/MS approaches, such as data dependent acquisition, SRM/MRM, and data independent acquisition instrumentation. Despite these developments, many peptides remain unsequenced, often due to low abundance, poor fragmentation patterns, or data analysis difficulties. Many of the unidentified peptides exhibit strong evidence in high resolution MS(1) data and are frequently post-translationally modified, playing a significant role in biological processes. Proteomics Workbench (PWB) software was developed to automate the detection and visualization of all possible peptides in MS(1) data, reveal candidate peptides not initially identified, and build inclusion lists for subsequent MS(2) analysis to uncover new identifications. We used this software on existing data on the autophagy regulating kinase Ulk1 as a proof of concept for this method, as we had already manually identified a number of phosphorylation sites Dorsey, F. C. et al (J. Proteome. Res. 8(11), 5253-5263 (2009)). PWB found all previously identified sites of phosphorylation. The software has been made freely available at http://www.proteomicsworkbench.com . Graphical Abstract ᅟ.

A common strategy in mass spectrometry analyses of complex protein mixtures is to digest the proteins to peptides, separate the peptides by microcapillary liquid chromatography and collect tandem mass spectra (MS/MS) on the eluting, complex peptide mixtures, a process commonly termed “shotgun proteomics”. For years, the most common way of data collection was via data-dependent acquisition (DDA), a process driven by an automated instrument control routine that directs MS/MS acquisition from the highest abundant signals to the lowest, a process often leaving lower abundant signals unanalyzed and therefore unidentified in the experiment. Advances in both instrumentation duty cycle and sensitivity allow DDA to probe to lower peptide abundance and therefore enable mapping proteomes to a more significant depth. An alternative to acquiring data by DDA is by data-independent acquisition (DIA), in which a specified range in m/z is fragmented without regard to prioritization of a precursor ion or its relative abundance in the mass spectrum. As a consequence, DIA acquisition potentially offers more comprehensive analysis of peptides than DDA and in principle can yield tandem mass spectra of all ionized molecules following their conversion to the gas-phase. In this work, we evaluate both DDA and DIA on three different linear ion trap instruments: an LTQ, an LTQ modified in-house with an electrodynamic ion funnel, and an LTQ-Velos. These instruments were chosen as they are representative of both older (LTQ) and newer (LTQ-Velos) ion trap designs i.e., linear ion trap and dual ion traps, respectively, and allow direct comparison of peptide identification using both DDA and DIA analysis. Further, as the LTQ-Velos has an improved “S-lens” ion guide in the high-pressure region to improve ion flux, we found it logical to determine if the former LTQ model could be leveraged by improving sensitivity by modifying with an electrodynamic ion guide of significantly different design to the S-lens. We find that the ion funnel enabled LTQ identifies more proteins in the insoluble fraction of a yeast lysate than the other two instruments in DIA mode, while the faster scanning LTQ-Velos performs better in DDA mode. We explore reasons for these results, including differences in scan speed, source ion optics, and linear ion trap design.

Post-translational modification of lysine residues by NƐ-acylation is an important regulator of protein function. Many large-scale protein acylation studies have assessed relative changes of lysine acylation sites after antibody enrichment using mass spectrometry-based proteomics. Although relative acylation fold-changes are important, this does not reveal site occupancy, or stoichiometry, of individual modification sites, which is critical to understand functional consequences. Recently, methods for determining lysine acetylation stoichiometry have been proposed based on ratiometric analysis of endogenous levels to those introduced after quantitative per-acetylation of proteins using stable isotope-labeled acetic anhydride. However, in our hands, we find that these methods can overestimate acetylation stoichiometries because of signal interferences when endogenous levels of acylation are very low, which is especially problematic when using MS1 scans for quantification. In this study, we sought to improve the accuracy of determining acylation stoichiometry using data-independent acquisition (DIA). Specifically, we use SWATH acquisition to comprehensively collect both precursor and fragment ion intensity data. The use of fragment ions for stoichiometry quantification not only reduces interferences but also allows for determination of site-level stoichiometry from peptides with multiple lysine residues. We also demonstrate the novel extension of this method to measurements of succinylation stoichiometry using deuterium-labeled succinic anhydride. Proof of principle SWATH acquisition studies were first performed using bovine serum albumin for both acetylation and succinylation occupancy measurements, followed by the analysis of more complex samples of E. coli cell lysates. Although overall site occupancy was low (