Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Glenn Stone,David Clifford,Peter Hoffmann,Shaun R Mccoll,Johan Or Gustafsson

doi:10.1186/1756-0500-5-419

Abstract

BackgroundImaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose another; the minimum noise fraction (MNF) transform which is popular in remote sensing.FindingsThe MNF transform is able to extract spatially coherent information from IMS data. The MNF transform is implemented through an R-package which is available together with example data from http://staﬀ.scm.uws.edu.au/∼glenn/∖#Software.ConclusionsIn our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.

Highlights

Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample
Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of drug metabolite, lipid, peptide and protein features on the surface of a sectioned tissue sample
We propose the use of the minimum noise fraction (MNF) transform [8] to, firstly, determine the most interesting spatial representations of IMS data, and secondly, form the basis of data reduction for subsequent analysis

Summary

Background

Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of drug metabolite, lipid, peptide and protein features on the surface of a sectioned tissue sample (see [1] and references therein). IMS methods utilise freshly frozen sections of tissue mounted onto conductive slides. These are coated with matrix followed by MALDI-ToF/ToF spectra acquisition at anywhere from hundreds to thousands of positions across a tissue, the spatial locations of which are annotated. The data can be thought of in two ways, firstly a set of mass spectra acquired at a spatial array of spots, and secondly as a stack of ion intensity maps, each map being akin to a low resolution image. Software such as Biomap and flexImaging (Bruker Daltonics) view IMS data as ion. The MNF transform has previously been used on hyper-spectral images of tissue samples [9] but this is the first use of such a technique on IMS data

Findings

Results

Conclusion