Title

Metabolite mapping by consecutive nanostructure and silver-assisted mass spectrometry imaging on tissue sections

Document Type

Article

Publication details

Gustafsson, OJR, Guinan, TM, Rudd, D, Kobus, H, Benkendorff, K & Voelcker, NH 2017, 'Metabolite mapping by consecutive nanostructure and silver-assisted mass spectrometry imaging on tissue sections', Rapid Communications in Mass Spectrometry, vol. 31, no. 12, pp. 991-1000.

Published version available from:

https://dx.doi.org/10.1002/rcm.7869

Peer Reviewed

Peer-Reviewed

Abstract

RATIONALE: Nanostructure-based mass spectrometry imaging (MSI) is a promising technology for molecular imaging of small molecules, without the complex chemical background typically encountered in matrix-assisted molecular imaging approaches. Here, we have enhanced these surfaces with silver (Ag) to provide a second tier of MSI data from a single sample.

METHODS: MSI data was acquired through the application of laser desorption/ionization mass spectrometry to biological samples imprinted onto desorption/ionization on silicon (DIOS) substrates. Following initial analysis, ultra-thin Ag layers were overlaid onto the followed by MSI analysis (Ag-DIOS MSI). This approach was first demonstrated for fingermark small molecules including environmental contaminants and sebum components. Subsequently, this bimodal method was translated to lipids and metabolites in fore-stomach sections from a 6-bromoisatin chemopreventative murine mouse model.

RESULTS: DIOS MSI allowed mapping of common ions in fingermarks as well as 6-bromoisatin metabolites and lipids in murine fore-stomach. Furthermore, DIOS MSI was complemented by the Ag-DIOS MSI of Ag-adductable lipids such as wax esters in fingermarks and cholesterol in murine fore-stomach. Gastrointestinal acid condensation products of 6-bromoisatin, such as the 6,6'-dibromoindirubin mapped herein, are very challenging to isolate and characterize. By re-analyzing the same tissue imprints, this metabolite was readily detected by DIOS, placed in a tissue-specific spatial context, and subsequently overlaid with additional lipid distributions acquired using Ag-DIOS MSI.

CONCLUSIONS: The ability to place metabolite and lipid classes in a tissue-specific context makes this novel method suited to MSI analyses where the collection of additional information from the same sample maximises resource use, and also maximises the number of annotated small molecules, in particular for metabolites that are typically undetectable with traditional platforms. Copyright © 2017 John Wiley & Sons, Ltd.