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

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

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.

CCSfind: A tool for chemically informed LC-IM-MS database building

In addition to providing critical knowledge of the accurate mass of ions, ion mobility-mass spectrometry (IM-MS) delivers complementary data relating to the conformation and size of ions in the form of an ion mobility spectrum and derived parameters, namely, the ion's mobility (K) and the IM-derived collision cross section (CCS). However, the maximum amount of information obtained in IM-MS measurements is not currently transferred into analytical databases including the full mobility spectra (CCS distributions) as well as capturing of additional ion species (e.g., adducts) into the same compound entry. We introduce CCSfind, a new tool for building comprehensive databases from experimental IM-MS measurements of small molecules. CCSfind allows predicted ion species to be chosen for input chemical formulae, which are then targeted by CCSfind after parsing open source mzML input files to provide a unified set of results within a single data processing step. CCSfind can handle both chromatographically separated isomers and IM separation of isomeric ions (e.g., "protomers" or conformers of the same ion species) with simple user control over the output for new database entries in SQL format. Files of up to 1 GB can be processed in less than 2 min on a desktop computer with 32 GB RAM with computational time scaling linearly with the size of the input mzML file or the number of input molecular formulae. Results are manually reviewed, annotated with experimental settings, before committing the database where the full dataset can be retrieved.

Results of the first international quality control programme for oral targeted oncolytics

AIMS

With the rising number of oral targeted oncolytics and growing awareness of the benefits of therapeutic drug monitoring (TDM) within the field of oncology, it is expected that the requests for quantifying concentrations of these drugs will increase. It is important to (cross-)validate available assays and ensure its quality, as results may lead to altered dosing recommendations. Therefore, we aimed to evaluate the performance of laboratories measuring concentrations of targeted oral oncolytics in a one-time international quality control (QC) programme.

METHODS

Participating laboratories received a set of plasma samples containing low, medium and high concentrations of imatinib, sunitinib, desethylsunitinib, pazopanib, cabozantinib, olaparib, enzalutamide, desmethylenzalutamide and abiraterone, with the request to report their results back within five weeks after shipment. Accuracy was defined acceptable if measurements where within 85%-115% from the weighed-in reference concentrations. Besides descriptive statistics, an exploratory ANOVA was performed.

RESULTS

Seventeen laboratories from six countries reported 243 results. Overall, 80.7% of all measurements were within the predefined range of acceptable accuracy. Laboratories performed best in quantifying imatinib and poorest in quantifying desethylsunitinib (median absolute inaccuracy respectively 4.0% (interquartile range (IQR) 1.8%-6.5%) and 15.5% (IQR 8.8%-34.9%)). The poorest performance of desethylsunitinib might be caused by using the stable-isotope-labelled sunitinib instead of desethylsunitinib as an internal standard, or due to the light-induced cis(Z)/trans(E) isomerization of (desethyl)sunitinib. Overall, drug substance and performing laboratory seemed to influence the absolute inaccuracy (F = 16.4; p < 0.001 and F = 35.5; p < 0.001, respectively).

CONCLUSION

Considering this is the first evaluation of an international QC programme for oral targeted oncolytics, an impressive high percentage of measurements were within the predefined range of accuracy. Cross-validation of assays that are used for dose optimization of oncolytics will secure the performance and will protect patients from incorrect advices.

A Novel Multi-ion Evaluation Scheme to Determine Stable Chlorine Isotope Ratios (37Cl/35Cl) of Chlordecone by LC-QTOF

Organochlorinated pesticides are highly persistent organic pollutants having important adverse effects in the environment. To study their fate, compound-specific isotope analysis (CSIA) may be used to investigate their degradation pathways and mechanisms but is currently limited to 13C isotope ratios. The assessment of 37Cl isotope ratios from mass spectra is complicated by the large number of isotopologues of polychlorinated compounds. For method development, chlordecone (C10Cl10O2H2; hydrate form), an organochlorine insecticide that led to severe contamination of soils and aquatic ecosystems of the French West Indies, was taken as a model analyte. Chlorine isotope analysis of chlordecone hydrate was evaluated using high-resolution liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS), enabling smooth ionization to detect the molecular ion. First, a new evaluation scheme is presented to correct for multiple isotope presence in polychlorinated compounds. The scheme is based on probability calculations of the most frequent isotopologues, distributions by binomial probability functions, and corrections for the presence of nonchlorine heavy isotopes. Second, mobile-phase modifiers, ionization energy (sampling cone tension) and scan time were optimized for accurate chlorine isotope ratios. Chlordecone standard samples were measured up to 10-fold and bracketed with a second chlordecone external standard. δ37Cl values were obtained after conversion to the SMOC scale by a two-point calibration. The robustness of the analysis method and evaluation scheme were tested and gave satisfactory results with standard errors (σm) of ±0.34‰ for precision and ±0.89‰ for long-term accuracy of chlorine isotope ratios of chlordecone hydrate. This work opens perspectives for applications of the C-Cl CSIA approach to investigate the fate of highly toxic and low reactive polychlorinated compounds in the environment.

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.

The isotope distribution: A rose with thorns

The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing.

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

The primary goal of mass spectrometry data processing pipelines in the proteomic analysis of complex biological samples is to identify peptides accurately and comprehensively with abundance across a broad dynamic range. It has been reported that detection of low-abundance peptides for early-disease biomarkers in complex fluids is limited by the sensitivity of biomarker discovery platforms [1], the dynamic range of plasma abundance, which can exceed ten orders of magnitude [2], and the fact that lower abundance proteins provide the most insight in disease processes [3]. As mass spectrometry hardware improves, the corresponding increase in amounts of data for analysis pushes legacy software analysis methods out of their designed specification. Additionally, experimentation with new algorithms to analyse raw data produced by instruments such as the Bruker timsTOF Pro has been hampered by the paucity of modular, open-source software pipelines written in languages accessible by the large community of data scientists. Here we present several algorithms for simplifying MS1 and MS2 spectra that are written in Python. We show that these algorithms are effective to help improve the quality and accuracy of peptide identifications.

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.

Improving Signal to Noise Ratios in Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations (SLIM) using a High Dynamic Range Analog-to-Digital Converter

Signal digitization is a commonly overlooked part of ion mobility-mass spectrometry (IMS-MS) workflows, yet it greatly affects signal-to-noise ratio and MS resolution measurements. Here, we report on the integration of a 2 GS/s, 14-bit ADC with structures for lossless ion manipulations (SLIM-IMS-MS) and compare the performance to a commonly used 8-bit ADC. The 14-bit ADC provided a reduction in the digitized noise by a factor of ∼6, owing largely to the use of smaller bit sizes. The low baseline allowed threshold voltage levels to be set very close to the MCP baseline voltage, allowing for as much signal to be acquired as possible without overloading or excessive digitization of MCP baseline noise. Analyses of Agilent tuning mixture ions and a mixture of heavy labeled phosphopeptides showed that the 14-bit ADC provided a ∼1.5-2× signal-to-noise (S/N) increase for high intensity ions, such as the Agilent tuning mixture ions and the 2+ and 3+ charge states of many phosphopeptide constituents. However, signal enhancements were as much as 10-fold for low intensity ions, and the 14-bit ADC enabled discernible signal intensities otherwise lost using an 8-bit digitizer. Additionally, the 14-bit ADC required ∼14-fold fewer mass spectra to be averaged to produce a mass spectrum with a similar S/N as the 8-bit ADC, demonstrating ∼10× higher measurement throughput. The high resolution, low baseline, and fast speed of the new 14-bit ADC enables high performance digitization of MS, IMS-MS, and SLIM-IMS-MS spectra and provides a much better picture of analyte profiles in complex mixtures.

Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

UHPLC-IM-Q-ToFMS analysis of maradolipids, found exclusively in Caenorhabditis elegans dauer larvae

Lipid identification is one of the current bottlenecks in lipidomics and lipid profiling, especially for novel lipid classes, and requires multidimensional data for correct annotation. We used the combination of chromatographic and ion mobility separation together with data-independent acquisition (DIA) of tandem mass spectrometric data for the analysis of lipids in the biomedical model organism Caenorhabditis elegans. C. elegans reacts to harsh environmental conditions by interrupting its normal life cycle and entering an alternative developmental stage called dauer stage. Dauer larvae show distinct changes in metabolism and morphology to survive unfavorable environmental conditions and are able to survive for a long time without feeding. Only at this developmental stage, dauer larvae produce a specific class of glycolipids called maradolipids. We performed an analysis of maradolipids using ultrahigh performance liquid chromatography-ion mobility spectrometry-quadrupole-time of flight-mass spectrometry (UHPLC-IM-Q-ToFMS) using drift tube ion mobility to showcase how the integration of retention times, collisional cross sections, and DIA fragmentation data can be used for lipid identification. The obtained results show that combination of UHPLC and IM separation together with DIA represents a valuable tool for initial lipid identification. Using this analytical tool, a total of 45 marado- and lysomaradolipids have been putatively identified and 10 confirmed by authentic standards directly from C. elegans dauer larvae lipid extracts without the further need for further purification of glycolipids. Furthermore, we putatively identified two isomers of a lysomaradolipid not known so far.

An algorithm to calibrate ionic isotopes using data mining strategy in hyphenated chromatographic datasets from herbal samples

The bottleneck of analytical instrument itself and non-ideal instrumental performance will produce a certain degree of drifts between the measured isotopes and the true values. An AAID-IC algorithm was thereby proposed to keep the isotopic distributions more accurate in hyphenated instruments, e.g. Gas Chromatography (GC)/ Liquid Chromatography (LC) - Mass Spectrometry (MS). During this data mining process, chemical information will be fully used from dozens of data points in retention time (rt) dimension: the target isotopes were firstly re-constructed in mass charge ratio (m/z) dimension; their re-calculation values were then averaged from an interesting rt zone; the calibration functions were followed established based on a well-defined series of calibration ions. It is worth mentioning that natural metabolites in complex samples can be identified as reference materials to amend the target isotopes. Next, the corrected mass axes (m/z values)/isotope abundances were transformed into an ionic isotopic curve using Gaussian box. Taking herbal sample as an example, AAID-IC can better reduce the systematic and random errors of the m/z ions in one run environment, whether it's profile or bar graph from any type of MS and any ionization method employed. Finally, the calibrated values can be utilized to deduce the elemental compositions of molecular (fragment) ions in GC/LC-MS determination.

Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review

Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.

Improved Sensitivity and Separations for Phosphopeptides using Online Liquid Chromotography Coupled with Structures for Lossless Ion Manipulations Ion Mobility-Mass Spectrometry

Phosphoproteomics greatly augments proteomics and holds tremendous potential for insights into the modulation of biological systems for various disease states. However, numerous challenges hinder conventional methods in terms of measurement sensitivity, throughput, quantification, and capabilities for confident phosphopeptide and phosphosite identification. In this work, we report the first example of integrating structures for lossless ion manipulations ion mobility-mass spectrometry (SLIM IM-MS) with online reversed-phase liquid chromatography (LC) to evaluate its potential for addressing the aforementioned challenges. A mixture of 51 heavy-labeled phosphopeptides was analyzed with a SLIM IM module having integrated ion accumulation and long-path separation regions. The SLIM IM-MS provided limits of detection as low as 50-100 pM (50-100 amol/μL) for several phosphopeptides, with the potential for significant further improvements. In addition, conventionally problematic phosphopeptide isomers could be resolved following an 18 m SLIM IM separation. The 2-D LC-IM peak capacity was estimated as ∼9000 for a 90 min LC separation coupled to an 18 m SLIM IM separation, considerably higher than LC alone and providing a basis for both improved identification and quantification, with additional gains projected with the future use of longer path SLIM IM separations. Thus, LC-SLIM IM-MS offers great potential for improving the sensitivity, separation, and throughput of phosphoproteomics analyses.