Literature DB >> 17511421

Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method.

Andrew D Southam1, Tristan G Payne, Helen J Cooper, Theodoros N Arvanitis, Mark R Viant.   

Abstract

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS) offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse space-charge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection of multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 x 10(5) charges. SIM-stitching and wide-scan range (WSR; Thermo Electron) were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3- and 4.3-fold, and increased the peak count by 5.3- and 1.8-fold, versus WSR (AGC targets of 1 x 10(5) and 5 x 10(5), respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high-throughput metabolomics.

Mesh:

Year:  2007        PMID: 17511421     DOI: 10.1021/ac062446p

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  40 in total

1.  A signal filtering method for improved quantification and noise discrimination in fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data.

Authors:  Tristan G Payne; Andrew D Southam; Theodoros N Arvanitis; Mark R Viant
Journal:  J Am Soc Mass Spectrom       Date:  2009-02-07       Impact factor: 3.109

2.  Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer.

Authors:  Reinaldo Almeida; Josch Konstantin Pauling; Elena Sokol; Hans Kristian Hannibal-Bach; Christer S Ejsing
Journal:  J Am Soc Mass Spectrom       Date:  2014-11-13       Impact factor: 3.109

3.  Improved segmented-scan spectral stitching for stable isotope resolved metabolomics (SIRM) by ultra-high-resolution Fourier transform mass spectrometry.

Authors:  Woo-Young Kang; Patrick T Thompson; Salim S El-Amouri; Teresa W M Fan; Andrew N Lane; Richard M Higashi
Journal:  Anal Chim Acta       Date:  2019-06-11       Impact factor: 6.558

4.  A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics.

Authors:  Andrew D Southam; Ralf J M Weber; Jasper Engel; Martin R Jones; Mark R Viant
Journal:  Nat Protoc       Date:  2017-01-12       Impact factor: 13.491

5.  Untargeted metabolomic analysis and pathway discovery in perinatal asphyxia and hypoxic-ischaemic encephalopathy.

Authors:  Niamh M Denihan; Jennifer A Kirwan; Brian H Walsh; Warwick B Dunn; David I Broadhurst; Geraldine B Boylan; Deirdre M Murray
Journal:  J Cereb Blood Flow Metab       Date:  2017-08-25       Impact factor: 6.200

6.  Direct infusion mass spectrometry of oxylipin-containing Arabidopsis membrane lipids reveals varied patterns in different stress responses.

Authors:  Hieu Sy Vu; Pamela Tamura; Nadezhda A Galeva; Ratnesh Chaturvedi; Mary R Roth; Todd D Williams; Xuemin Wang; Jyoti Shah; Ruth Welti
Journal:  Plant Physiol       Date:  2011-11-15       Impact factor: 8.340

7.  Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'.

Authors:  John Draper; David P Enot; David Parker; Manfred Beckmann; Stuart Snowdon; Wanchang Lin; Hassan Zubair
Journal:  BMC Bioinformatics       Date:  2009-07-21       Impact factor: 3.169

8.  Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research.

Authors:  Augustin Scalbert; Lorraine Brennan; Oliver Fiehn; Thomas Hankemeier; Bruce S Kristal; Ben van Ommen; Estelle Pujos-Guillot; Elwin Verheij; David Wishart; Suzan Wopereis
Journal:  Metabolomics       Date:  2009-06-12       Impact factor: 4.290

9.  Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry.

Authors:  Jun Han; Ryan M Danell; Jayanti R Patel; Dmitry R Gumerov; Cameron O Scarlett; J Paul Speir; Carol E Parker; Ivan Rusyn; Steven Zeisel; Christoph H Borchers
Journal:  Metabolomics       Date:  2008-06       Impact factor: 4.290

10.  Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research.

Authors:  Yanlong Zhu; Benjamin Wancewicz; Michael Schaid; Timothy N Tiambeng; Kent Wenger; Yutong Jin; Heino Heyman; Christopher J Thompson; Aiko Barsch; Elizabeth D Cox; Dawn B Davis; Allan R Brasier; Michelle E Kimple; Ying Ge
Journal:  J Proteome Res       Date:  2020-10-15       Impact factor: 4.466

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