High-Throughput Compound Quality Assessment with High-Mass-Resolution Acoustic Ejection Mass Spectrometry: An Automatic Data Processing Toolkit.

Pharmacological screening heavily relies on the reliability of compound libraries. To ensure the accuracy of screening results, fast and reliable quality control (QC) of these libraries is essential. While liquid chromatography (LC) with ultraviolet (UV) or mass spectrometry (MS) detection has been employed for molecule QC on small sample sets, the analytical throughput becomes a bottleneck when dealing with large libraries. Acoustic ejection mass spectrometry (AEMS) is a high-throughput analytical platform that covers a broad range of chemical structural space. In this study, we present the utilization of an AEMS system equipped with a high-resolution MS analyzer for high-throughput compound QC. To facilitate efficient data processing, which is a key challenge for such a high-throughput application, we introduce an automatic data processing toolkit that allows for the high-throughput assessment of the sample standards' quantitative and qualitative characteristics, including purity calculation with the background processing option. Moreover, the toolkit includes a module for quantitatively comparing spectral similarity with the reference library. Integrating the described high-resolution AEMS system with the data processing toolkit effectively eliminates the analytical bottleneck, enabling a rapid and reliable compound quality assessment of large-scale compound libraries.

Ultra-fast proteomics with Scanning SWATH.

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Prioritizing potential endocrine active high resolution mass spectrometry (HRMS) features in Minnesota lakewater.

Liquid chromatography high-resolution mass spectrometry (LC-HRMS) shows great potential for expanding our understanding of relevant unknown chemical components present within complex environmental mixtures. This study identified potentially endocrine active components within Minnesota lakewater by prioritizing LC-HRMS features uniquely present at sunfish spawning habitats where male fish showed signs of estrogen agonism. Porewater samples from four locations within the same lake were analyzed using liquid chromatography quadrupole time of flight mass spectrometry (LC-QToF/MS) with positive (ESI+) and negative (ESI-) electrospray ionization. Plasma vitellogenin concentrations of captured male sunfish was used to designate sites as either endocrine active (ACT; 2 sites) or reference (REF; 2 sites). Assuming unique chemical presence at active sites contributed to endocrine activity, features at significantly higher intensities (p-value < 0.05, t-value > t-critical, log-fold change > 0.1; equal variance t-test of log2 transformed data) in ACT sites were then compiled into a suspect search list for feature identification. Adducts and isotopes of prioritized features were deprioritized using pattern recognizing algorithms using mass, retention time, and intensity. Feature identities were reported according to established confidence metrics using spectral libraries and elemental composition algorithms. This LC-HRMS approach identified a number of features omitted by targeted analysis with higher relative abundances in ACT sites, including plant essential oils, fatty acids, and mycotoxins. Multivariate analysis determined whether features were either present at both sites (AB) or unique to individual ACT sites (A or B). Detection frequency across datasets indicated bias in feature prioritization influenced by the chosen sampling method and sample acquisition mode. The majority of features prioritized by this workflow remain tentatively identified or unidentified masses of interest, reflective of current limitations in shared spectral libraries for soft ionization analyses. Strategies similar to this workflow have the potential to reduce bias in database-driven toxicological prioritization frameworks.

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.

A high dynamic range pulse counting detection system for mass spectrometry.

RATIONALE: A high dynamic range pulse counting system has been developed that demonstrates an ability to operate at up to 2e8 counts per second (cps) on a triple quadrupole mass spectrometer. Previous pulse counting detection systems have typically been limited to about 1e7 cps at the upper end of the systems dynamic range. Modifications to the detection electronics and dead time correction algorithm are described in this paper. METHODS: A high gain transimpedance amplifier is employed that allows a multi-channel electron multiplier to be operated at a significantly lower bias potential than in previous pulse counting systems. The system utilises a high-energy conversion dynode, a multi-channel electron multiplier, a high gain transimpedance amplifier, non-paralysing detection electronics and a modified dead time correction algorithm. Modification of the dead time correction algorithm is necessary due to a characteristic of the pulse counting electronics. RESULTS: A pulse counting detection system with the capability to count at ion arrival rates of up to 2e8 cps is described. This is shown to provide a linear dynamic range of nearly five orders of magnitude for a sample of aprazolam with concentrations ranging from 0.0006970 ng/mL to 3333 ng/mL while monitoring the m/z 309.1 → m/z 205.2 transition. This represents an upward extension of the detector's linear dynamic range of about two orders of magnitude. CONCLUSIONS: A new high dynamic range pulse counting system has been developed demonstrating the ability to operate at up to 2e8 cps on a triple quadrupole mass spectrometer. This provides an upward extension of the detector's linear dynamic range by about two orders of magnitude over previous pulse counting systems.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor.

Signaling pathways are commonly organized through inducible protein-protein interactions, mediated by adaptor proteins that link activated receptors to cytoplasmic effectors. However, we have little quantitative data regarding the kinetics with which such networks assemble and dissolve to generate specific cellular responses. To address this deficiency, we designed a mass spectrometry method, affinity purification-selected reaction monitoring (AP-SRM), which we used to comprehensively and quantitatively investigate changes in protein interactions with GRB2, an adaptor protein that participates in a remarkably diverse set of protein complexes involved in multiple aspects of cellular function. Our data reliably define context-specific and time-dependent networks that form around GRB2 after stimulation, and reveal core and growth factor-selective complexes comprising 90 proteins identified as interacting with GRB2 in HEK293T cells. Capturing a key hub protein and dissecting its interactions by SRM should be equally applicable to quantifying signaling dynamics for a range of hubs in protein interaction networks.

Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools.

A comprehensive analytical LC-MS(/MS) platform for low weight biomarkers molecule in biological fluids is described. Two complementary retention mechanisms were used in HPLC by optimizing the chromatographic conditions for a reversed-phase column and a hydrophilic interaction chromatography column. LC separation was coupled to mass spectrometry by using an electrospray ionization operating in positive polarity mode. This strategy enables us to correctly retain and separate hydrophobic as well as polar analytes. For that purpose artificial model study samples were generated with a mixture of 38 well characterized compounds likely to be present in biofluids. The set of compounds was used as a standard aqueous mixture or was spiked into urine at different concentration levels to investigate the capability of the LC-MS(/MS) platform to detect variations across biological samples. Unsupervised data analysis by principal component analysis was performed and followed by principal component variable grouping to find correlated variables. This tool allows us to distinguish three main groups whose variables belong to (a) background ions (found in all type of samples), (b) ions distinguishing urine samples from aqueous standard and blank samples, (c) ions related to the spiked compounds. Interpretation of these groups allows us to identify and eliminate isotopes, adducts, fragments, etc. and to generate a reduced list of m/z candidates. This list is then submitted to the prototype MZSearcher software tool which simultaneously searches several lists of potential metabolites extracted from metabolomics databases (e.g., KEGG, HMDB, etc) to propose biomarker candidates. Structural confirmation of these candidates was done off-line by fraction collection followed by nanoelectrospray infusion to provide high quality MS/MS data for spectral database queries.

Dimensionality reduction and visualization in principal component analysis.

Many modern applications of analytical chemistry involve the collection of large megavariate data sets and subsequent processing with multivariate analysis techniques (MVA), two of the more common goals being data analysis (also known as data mining and exploratory data analysis) and classification. Classification attempts to determine variables that can distinguish known classes allowing unknown samples to be correctly assigned, whereas data analysis seeks to uncover and understand or confirm relationships between the samples and the variables. An important part of analysis is visualization which allows analysts to apply their expertise and knowledge and is often easier for the samples than the variables since there are frequently far more of the latter. Here we describe principal component variable grouping (PCVG), an unsupervised, intuitive method that assigns a large number of variables to a smaller number of groups that can be more readily visualized and understood. Knowledge of the source or nature of the variables in a group allows them all to be appropriately treated, for example, removed if they result from uninteresting effects or replaced by a single representative for further processing.

Instrumental and experimental effects in LC-MS-based metabolomics.

The experimental complexity of a metabolomics study can cause uncontrolled variance that is not related to the biological effect being studied and may distort or obscure the data analysis. While some sources can be controlled with good experimental techniques and careful sample handling, others are inherent in the analytical technique used and cannot easily be avoided. We discuss the sources and appearance of some of these artifacts and show ways in which they can be detected using visualization and statistical tools, allowing appropriate treatment prior to multivariate analysis (MVA).