= Emerging. More than 5 years before clinical availability. (9.82%)
= Expected to be clinically available in 1 to 4 years. (12.95%)
= Clinically available now. (22.77%)
MSACL 2018 EU : Wehrli

MSACL 2018 EU Abstract

Topic: Tissue Imaging

Cross-Modality Correlation of Multimodal Imaging Mass Spectrometry Data at Single-Pixel Resolution

Patrick Wehrli (Presenter)
University of Gothenburg


Presenter Bio: Patrick Wehrli is a postdoctoral researcher in analytical neurochemistry at the University of Gothenburg, Sweden. He has a background in organic and medicinal chemistry and joined the group of Dr. Jörg Hanrieder at the University of Gothenburg in 2017. There, his current research is focused on the investigation of biomolecular mechanisms that underlie neurodegenerative diseases by means of imaging mass spectrometry.

Authors: Patrick M. Wehrli (1), Wojciech Michno (1), Laurent Guerard (2), Kaj Blennow (1,3), Henrik Zetterberg (1,3,4,5) and Jörg Hanrieder (1,4,6)
(1) Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden (2) IMCF Biozentrum, University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland (3) Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, S-431 80 Mölndal, Sweden (4) Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, United Kingdom (5) UK Dementia Research Institute at UCL, London WC1N 3BG, United Kingdom (6) Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Kemivägen 10, S-412 96 Gothenburg, Sweden

Short Abstract

Our group has previously been investigating neuropathology associated distribution patterns of lipids and proteins in transgenic mouse models of Alzheimer’s disease (AD) using multimodal imaging strategies.1 In here, were present a continuation of these efforts by means of a data processing routine that enables direct correlation of chemical information across multiple imaging mass spectrometry (IMS) modalities. Here brain sections of transgenic AD mice (tgSwe) were analyzed using matrix-assisted laser desorption/ionization (MALDI) IMS in three modalities: lipids in (1) negative and (2) positive ion mode, and (3) peptides in positive ion mode. The three IMS data cubes were processed by spectral pretreatment and co-registration prior to multivariate image data analysis. This strategy enabled direct and unsupervised cross-modality correlation at the measured image resolution.

Long Abstract


Imaging mass spectrometry (IMS) is used to visualize the spatial distribution of molecules on a sample surface. In particular, matrix-assisted laser desorption/ionization (MALDI) IMS is a popular approach for studying biochemical processes related to disease pathology, pharmacotherapy and drug metabolism. [1,2] Biochemical distribution maps obtained from the analysis of biological tissues can be matched to histological features, the technique has thus also been referred to as molecular histology [3,4].

In order to comprehensively interrogate a tissue section, multiple imaging modalities are required. In MALDI IMS, the choice of the sample preparation method (type of washings, type of applied matrix), allows to select for classes of molecules i.e. analytes of interest, such as lipids and proteins. For this purpose, consecutive tissue sections with various preparations are typically analyzed to obtain multimodal information. Recently, our group has demonstrated an alternative approach, in which trimodal IMS measurements are performed on the same tissue section.[5]

IMS data analysis commonly includes qualitative comparative analyses of single or multiple ion images also in combination with images from other techniques, statistical analysis of regions of interest (ROIs), and multivariate image analysis methods on single modalities. Correlative interpretation across the modalities is, however, largely left to human judgment. In here, we applied a multimodal MALDI IMS approach to delineate and directly correlate chemical information across all IMS modalities in a transgenic mouse model of Alzheimer’s disease (AD). Our approach includes co-registration of IMS data cubes from imaging consecutive tissue sections and multiple measurements on the same tissue section with subsequent multivariate image analysis.


We applied the trimodal MALDI imaging workflow as previously described.[5] Briefly, fresh frozen transgenic AD mouse brain tissue (tgSwe) was cut into 12 μm thick sections in a cryostat microtome and collected onto conductive glass slides. The matrix was applied immediately prior to MALDI MS analysis using a dedicated matrix sprayer. For lipid analysis, 1,5-diaminonaphthalene was used as matrix. Thereby, repeated analysis in negative and positive ion mode was performed in reflector mode. Subsequently, the matrix was removed through washing and 2,5-dihydroxyacetophenone was applied as matrix for peptide analysis which was performed in linear positive ion mode.

MALDI IMS data were imported in to MATLAB. Spectral preprocessing including baseline correction and normalization was done on each modality individually. Further preprocessing steps were deemed not necessary. Reference images of each modality were created directly or via image principal components analysis (PCA) for improved contrast. The reference images were used to calculate an affine transformation matrix via fiducial point selection and then applied on to the IMS data cubes function. Registered data were reshaped and concatenated prior to multivariate data analysis. PCA scores images were reconstructed and loadings obtained for interpretation.


Brain tissue sections from a transgenic mouse model of Alzheimer’s disease were imaged using MALDI IMS in three modalities including (1) lipids in negative ion mode, (2) lipids in positive ion mode, and (3) peptides in positive ion mode. The choice of the sample preparation method allowed for selection of analytes. The analysis was done on consecutive tissue sections and multiple measurements on the same tissue section. The in-house developed MATLAB routine allowed IMS data processing including data pretreatment, co-registration and concatenation of the three modalities. This enabled direct unsupervised multivariate analysis of the trimodal dataset.

Conclusions & Discussion

We have previously been developing multimodal imaging strategies to investigate pathology associated distribution patterns of lipids and proteins in transgenic mouse models of Alzheimer’s disease (tgArcSwe and tgSwe).[5] In here, were present a continuation of these efforts by means of a data processing routine for direct and unbiased multivariate analysis of multimodal IMS data. The data processing workflow includes efficient data pretreatment, accurate co-registration, and combination of the three 3-dimensional IMS modalities. This enabled multivariate analysis i.e. PCA to be performed on all three IMS data sets simultaneously which presents a solution to direct cross-modality correlation of data at the measured image resolution.

References & Acknowledgements:

1. McDonnell, L. A.; Heeren, R. M. A., Imaging mass spectrometry. Mass Spectrometry Reviews 2007, 26 (4), 606-643.

2. Karlsson, O.; Hanrieder, J., Imaging mass spectrometry in drug development and toxicology. Archives of toxicology 2017, 91 (6), 2283-2294.

3. Cornett, D. S.; Mobley, J. A.; Dias, E. C.; Andersson, M.; Arteaga, C. L.; Sanders, M. E.; Caprioli, R. M., A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer. Mol. Cell. Proteomics 2006, 5 (10), 1975-1983.

4. Schwamborn, K.; Caprioli, R. M., INNOVATION Molecular imaging by mass spectrometry - looking beyond classical histology. Nature Reviews Cancer 2010, 10 (9), 639-646.

5. Kaya, I.; Brinet, D.; Michno, W.; Başkurt, M.; Zetterberg, H.; Blenow, K.; Hanrieder, J., Novel Trimodal MALDI Imaging Mass Spectrometry (IMS3) at 10 μm Reveals Spatial Lipid and Peptide Correlates Implicated in Aβ Plaque Pathology in Alzheimer’s Disease. ACS Chemical Neuroscience 2017, 8 (12), 2778-2790.

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