Biology subjects

Davies, V.

Publications and source records attributed to Davies, V..

6 recordsLinked to original sources

bioRxiv2025

Comprehensive LC-MS/MS Data Acquisition in Metabolomics via Maximum Bipartite Matching

BackgroundIn untargeted metabolomics studies, liquid chromatography tandem mass spectrometry (LC-MS/MS) is a powerful analytical platform. The fragmentation spectra produced can be used as "molecular fingerprints" to identify unknown metabolites. However, the high number of analytes that may be co-eluting limits the number of fragmentation spectra that can be collected and potentially identified, presenting a serious bottleneck for many studies. There is a need for new fragmentation strategies which are comprehensive, interpretable and robust, meaning they produce high-quality fragmentation spectra for as many analytes as possible while operating within the constraints of notoriously noisy mass spectrometry data. ResultsWe present a data acquisition workflow which uses a bipartite graph to represent the relationship between opportunities for fragmentation and desired fragmentation targets. This method allows a schedule for data acquisition to be optimally allocated by a standard algorithm. We augment this existing technique by allowing it to solve for multiple samples collectively, allowing it to optimise target intensity (and hence spectral quality) via the use of a weighted matching and by assigning leftover scans redundantly to improve robustness. We also show how this workflow can be used flexibly to generate inclusion windows for Data-Dependent Acquisition (DDA) methods. Our experiments show that several thousand peaks identified in a realistic biological sample can be targeted using only two LC-MS/MS runs. We also further investigate the trade-off between offline workflows and DDA methods by exposing our target list of peaks to realistic variation across samples. We find in those circumstances that our new method has performance (measured by number of peaks targeted comparable to state-of-the-art DDA methods). However, this competitive performance is only possible with our additions to the base maximum matching technique, which provide extra resistance against inter-sample variations. ConclusionsWe have proposed a workflow for LC-MS/MS data acquisition which can be used flexibly for entirely pre-scheduled acquisition or which may generate inclusion windows for online DDA methods. Our results show that the maximum matching workflow with our improvements is state-of-the-art where pre-scheduling is concerned, and in future this foundation may be developed to build more powerful DDA methods which can action the promise of truly comprehensive data acquisition.

bioinformatics↗

bioRxiv2023

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments

MotivationLiquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to identify metabolites. However, current Data-Dependent Acquisition (DDA) approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input. ResultsWe present TopNEXt, a real-time scan prioritisation framework that improves data acquisition in multi-sample LC-MS/MS metabolomics experiments. TopNEXt extends traditional DDA exclusion methods across multiple samples by using a Region of Interest (RoI) and intensity-based scoring system. Through both simulated and lab experiments we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts. AvailabilityTopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914. Data can be found at 10.5525/gla.researchdata.1382. Contactr.mcbride.1@research.gla.ac.uk or vinny.davies@glasgow.ac.uk Supplementary informationSupplementary data are available at Bioarxiv online.

molecular biology↗

bioRxiv2023

Simulated-to-real Benchmarking of Acquisition Methods in Metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their performance due to the difficulty and cost of performing comparisons with real experimental data due to the lack of ground truth and the costs involved in running large number of acquisitions. Here, we present a systematic in-silico comparison of these two acquisition methods. To do so, we extended our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. Embedding this work within ViMMS also allows for researchers to easily simulate DDA and DIA LC-MS/MS runs, validate them on actual instrument, and potentially prototype novel methods that best combine the characteristics of both approaches. We believe that this work provides a useful guide to the choice of DIA or DDA for scientists involved in metabolomics data acquisition.

systems biology↗

bioRxiv2022

A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses

Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharactrised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.

microbiology↗

bioRxiv2020

Rapid Development of Improved Data-dependent Acquisition Strategies.

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data Dependent Acquisition (DDA) chooses which ions to fragment based upon intensity observed in MS1 survey scans and typically only fragment a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimise approaches in busy MS facilities. In this work, we firstly provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost efficient development of new DDA acquisition strategies using a previously developed Virtual Metabolomics Mass Spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an instrument application programming interface (API). We demonstrate this framework through the development and optimisation of two new DDA methods which introduce new advanced ion prioritisation strategies. Upon application of the here developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA acquisition strategies.

bioinformatics↗

bioRxiv2019

In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

Liquid-Chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimise strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in-silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data in-silico to allow the testing of different fragmentation strategies. It allows the comparison of different fragmentation strategies on real data, with the resulting scan results extractable as mzML files. To demonstrate its utility, we show how our proposed framework can be used to take the output of a real tandem mass spectrometry analysis and examine the effect of varying parameters in Top-N Data Dependent Acquisition protocol. We also demonstrate how ViMMS can be used to compare a recently published Data-set-Dependent Acquisition strategy with a standard Top-N strategy. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular experiment.

bioinformatics↗