Mapping microhabitats of lignocellulose decomposition by a microbial consortium.

The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data.

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format.

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows.

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

Online Hydrophilic Interaction Chromatography (HILIC) Enhanced Top-Down Mass Spectrometry Characterization of the SARS-CoV-2 Spike Receptor-Binding Domain.

SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein's structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.

AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry.

MOTIVATION: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. RESULTS: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.

Recommendations for reporting ion mobility Mass Spectrometry measurements.

Here we present a guide to ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties of mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0 ) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N; (ii) ion mobility does not measure molecular surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model; (iii) methods relying on calibration are empirical (and thus may provide method-dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort toward establishing primary standards and reference materials for ion mobility, and provides recommendations to do so. © 2019 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc.

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies.

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Processing strategies and software solutions for data-independent acquisition in mass spectrometry.

Data-independent acquisition (DIA) offers several advantages over data-dependent acquisition (DDA) schemes for characterizing complex protein digests analyzed by LC-MS/MS. In contrast to the sequential detection, selection, and analysis of individual ions during DDA, DIA systematically parallelizes the fragmentation of all detectable ions within a wide m/z range regardless of intensity, thereby providing broader dynamic range of detected signals, improved reproducibility for identification, better sensitivity, and accuracy for quantification, and, potentially, enhanced proteome coverage. To fully exploit these advantages, composite or multiplexed fragment ion spectra generated by DIA require more elaborate processing algorithms compared to DDA. This review examines different DIA schemes and, in particular, discusses the concepts applied to and related to data processing. Available software implementations for identification and quantification are presented as comprehensively as possible and examples of software usage are cited. Processing workflows, including complete proprietary frameworks or combinations of modules from different open source data processing packages are described and compared in terms of software availability and usability, programming language, operating system support, input/output data formats, as well as the main principles employed in the algorithms used for identification and quantification. This comparative study concludes with further discussion of current limitations and expectable improvements in the short- and midterm future.

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS.

Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative data set (i.e., the SWATH Gold Standard) indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to that with the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest data set, NOFI properly assigns low-priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80, against 0.76 for the Spectronaut interference detection algorithm.

The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition.

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of "scheduled SWATH" proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry.

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

LipidOz enables automated elucidation of lipid carbon-carbon double bond positions from ozone-induced dissociation mass spectrometry data.

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

Metabolomic, photoprotective, and photosynthetic acclimatory responses to post-flowering drought in sorghum.

Climate change is globally affecting rainfall patterns, necessitating the improvement of drought tolerance in crops. Sorghum bicolor is a relatively drought-tolerant cereal. Functional stay-green sorghum genotypes can maintain green leaf area and efficient grain filling during terminal post-flowering water deprivation, a period of ~10 weeks. To obtain molecular insights into these characteristics, two drought-tolerant genotypes, BTx642 and RTx430, were grown in replicated control and terminal post-flowering drought field plots in California's Central Valley. Photosynthetic, photoprotective, and water dynamics traits were quantified and correlated with metabolomic data collected from leaves, stems, and roots at multiple timepoints during control and drought conditions. Physiological and metabolomic data were then compared to longitudinal RNA sequencing data collected from these two genotypes. The unique metabolic and transcriptomic response to post-flowering drought in sorghum supports a role for the metabolite galactinol in controlling photosynthetic activity through regulating stomatal closure in post-flowering drought. Additionally, in the functional stay-green genotype BTx642, photoprotective responses were specifically induced in post-flowering drought, supporting a role for photoprotection in the molecular response associated with the functional stay-green trait. From these insights, new pathways are identified that can be targeted to maximize yields under growth conditions with limited water.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.