Rapid development of improved data-dependent acquisition strategies

Davies, V. , Wandy, J. , Weidt, S., van der Hooft, J. J.J. , Miller, A. , Daly, R. and Rogers, S. (2021) Rapid development of improved data-dependent acquisition strategies. Analytical Chemistry, 93(14), pp. 5676-5683. (doi: 10.1021/acs.analchem.0c03895) (PMID:33784814) (PMCID:PMC8047769)

Abstract

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 intensities observed in MS1 survey scans and typically only fragments 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 optimize approaches in busy MS facilities. In this work, we first 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 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 (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.

• Item Type: Articles
• Additional Information: V.D., J.W., S.W., R.D., and S.R. acknowledge EPSRC Project EP/R018634/1 on “Closed-loop data science for complex, computationally and data-intensive analytics”. J.J.J.v.d.H. was partly funded by an ASDI eScience Grant, ASDI.2017.030, from The Netherlands eScience Center (NLeSC).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Weidt, Dr Stefan and Davies, Dr Vinny and Miller, Professor Alice and Daly, Dr Ronan and Wandy, Dr Joe and Van Der Hooft, Mr Justin and Rogers, Dr Simon
• Authors: Davies, V., Wandy, J., Weidt, S., van der Hooft, J. J.J., Miller, A., Daly, R., and Rogers, S.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences; College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Science and Engineering > School of Computing Science
• Journal Name: Analytical Chemistry
• Publisher: American Chemical Society
• ISSN: 0003-2700
• ISSN (Online): 1520-6882
• Published Online: 30 March 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Analytical Chemistry 93(14): 5676-5683
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science