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A Space Efficient Direct Access Data Compression Approach for Mass Spectrometry Imaging
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Biomolecular Mass Spectrometry Imaging)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Biomolecular Mass Spectrometry Imaging)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Biomolecular Mass Spectrometry Imaging)
2018 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 90, no 6, p. 3676-3682Article in journal (Refereed) Published
Abstract [en]

Advances in mass spectrometry imaging that improve both spatial and mass resolution are resulting in increasingly larger data files that are difficult to handle with current software. We have developed a novel near-lossless compression method with data entropy reduction that reduces the file size significantly. The reduction in data size can be set at four different levels (coarse, medium, fine, and superfine) prior to running the data compression. This can be applied to spectra or spectrum-by-spectrum, or it can be applied to transpose arrays or array-by-array, to efficiently read the data without decompressing the whole data set. The results show that a compression ratio of up to 5.9:1 was achieved for data from commercial mass spectrometry software programs and 55:1 for data from our in-house developed mslQuant program. Comparing the average signals from regions of interest, the maximum deviation was 0.2% between compressed and uncompressed data sets with coarse accuracy for the data entropy reduction. In addition, when accessing the compressed data by selecting a random m/z value using mslQuant, the time to update an image on the computer screen was only slightly increased from 92 (+/- 32) ms (uncompressed) to 114 (+/- 13) ms (compressed). Furthermore, the compressed data can be stored on readily accessible servers for data evaluation without further data reprocessing. We have developed a space efficient, direct access data compression algorithm for mass spectrometry imaging, which can be used for various data-demanding mass spectrometry imaging applications.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2018. Vol. 90, no 6, p. 3676-3682
National Category
Pharmaceutical Sciences Analytical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-328051DOI: 10.1021/acs.analchem.7b03188ISI: 000428219600007OAI: oai:DiVA.org:uu-328051DiVA, id: diva2:1134456
Funder
Swedish Research Council, 521-2013-3105 621-2014-6215The Swedish Brain FoundationSwedish Foundation for Strategic Research , RIF14-0078Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceAvailable from: 2017-08-19 Created: 2017-08-19 Last updated: 2018-06-28Bibliographically approved
In thesis
1. Development and Application of Software Tools for Mass Spectrometry Imaging
Open this publication in new window or tab >>Development and Application of Software Tools for Mass Spectrometry Imaging
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mass spectrometry imaging (MSI) has been extensively used to produce qualitative maps of distributions of proteins, peptides, lipids, neurotransmitters, small molecule pharmaceuticals and their metabolites directly in biological tissue sections. Moreover, during the last 10 years, there has been growing demand to quantify target compounds in tissue sections of various organs. This thesis focuses on development and application of a novel instrument- and manufacturer-independent MSI software suite, msIQuant, in the open access format imzML, which has been developed specifically for quantitative analysis of MSI data. The functionality of msIQuant facilitates automatic generation of calibration curves from series of standards that can be used to determine concentrations of specific analytes. In addition, it provides many tools for image visualization, including modules enabling multiple interpolation, low intensity transparency display, and image fusion and sharpening. Moreover, algorithms and advanced data management modules in msIQuant facilitate management of the large datasets generated following rapid recent increases in the mass and spatial resolutions of MSI instruments, by using spectra transposition and data entropy reduction (at four selectable levels: coarse, medium, fine or superfine) before lossless compression of the data. As described in the thesis, implementation of msIQuant has been exemplified in both quantitative (relative or absolute) and qualitative analyses of distributions of neurotransmitters, endogenous substances and pharmaceutical drugs in brain tissue sections. Our laboratory have developed a molecular-specific approach for the simultaneous imaging and quantitation of multiple neurotransmitters, precursors, and metabolites, such as tyrosine, tryptamine, tyramine, phenethylamine, dopamine, 3-methoxytyramine, serotonin, gamma-aminobutyric acid (GABA), and acetylcholine, in histological tissue sections at high spatial resolution by matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) MSI. Chemical derivatization by charge-tagging primary amines of analytes significantly increased the sensitivity, enabling mapping of neurotransmitters that were not previously detectable by MSI. The two MSI approaches have been used to directly measure changes in neurotransmitter levels in specific brain structures in animal disease models, which facilitates understanding of biochemical mechanisms of drug treatments. In summary, msIQuant software has proven potency (particularly in combination with the reported derivatization technique) for both qualitative and quantitative analyses. Further developments will enable its implementation in multiple operating system platforms and use for statistical analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 66
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 234
Keywords
mass spectrometry imaging, MALDI, DESI, msIQuant, quantitation, neurotransmitters, drugs, derivatization, brain, compression, data entropy reduction
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-328016 (URN)978-91-513-0040-5 (ISBN)
Public defence
2017-10-06, A1:111a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2017-09-13 Created: 2017-08-19 Last updated: 2018-01-13

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Källback, PatrikNilsson, AnnaAndrén, Per E.

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