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Automated platform for high-resolution tissue imaging using nanospray desorption electrospray ionization mass spectrometry
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2012 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, no 19, 8351-8356 p.Article in journal (Refereed) Published
Abstract [en]

An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours by precisely controlling the distance between the sample and the nano-DESI probe. This is achieved by mounting the sample holder onto an automated XYZ stage, defining the tilt of the sample plane, and recalculating the vertical position of the stage at each point. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSI QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. Low background noise enables simultaneous detection of lipids and metabolites in the tissue section. High-resolution mass analysis combined with tandem mass spectometry (MS/MS) experiments enabled identification of the observed species. In addition, the high dynamic range (>2000) of the technique allowed us to generate ion images of low-abundance isobaric lipids. A high-spatial resolution image was acquired over a small region of the tissue section revealing the distribution of an abundant brain metabolite, creatine, on the boundary between the white and gray matter. The observed distribution is consistent with the literature data obtained using magnetic resonance spectroscopy.

Place, publisher, year, edition, pages
2012. Vol. 84, no 19, 8351-8356 p.
National Category
Chemical Sciences
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URN: urn:nbn:se:uu:diva-228332DOI: 10.1021/ac301909aPubMedID: 22954319OAI: oai:DiVA.org:uu-228332DiVA: diva2:733733
Available from: 2014-07-11 Created: 2014-07-11 Last updated: 2017-12-05Bibliographically approved

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Lanekoff, Ingela

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