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Spatial Chemometrics and Comprehensive Chemical Imaging based Molecular Histopathology Delineates Anatomical Heterogeneity at Single Pixel Resolution

submitted on 04.02.2020, 16:01 and posted on 05.02.2020, 09:32 by Patrick Wehrli, Wojciech Michno, Laurent Guerard, Julia Fernandez-Rodriguez, Anders Bergh, Kaj Blennow, Henrik Zetterberg, Jörg Hanrieder

Imaging mass spectrometry (IMS) is a powerful tool for spatially-resolved chemical analysis and thereby offers novel perspectives for applications in biology and medicine. The understanding of chemically complex systems, such as biological tissues, benefits from the combination of multiple imaging modalities contributing with complementary molecular information. Effective analysis and interpretation of multimodal IMS data is challenging and requires both, precise alignment and combination of the imaging data as well as suitable statistical analysis methods to identify cross-modal correlations. Commonly applied IMS data analysis methods include qualitative comparative analysis where cross-modal interpretation is subject to human judgement; Workflows that incorporate image registration procedures are usually applied for co-representing data rather than to mine data across modalities.

Here, we present an IMS-based, histology-driven strategy for comprehensive interrogation of biological tissues by spatial chemometrics. Our workflow implements a 1+1-evolutionary image registration method enabling direct correlation of chemical information across multiple modalities at single pixel resolution. Comprehensive multimodal imaging data were evaluated using a novel approach based on orthogonal multiblock component analysis (OnPLS). Finally, we present a novel image fusion method by implementing consecutively acquired pathological staining data to enhance histological interpretation.

We demonstrate the method’s potential in two biomedical applications where trimodal matrix-assisted laser desorption/ionization (MALDI) IMS delineates pathology associated co-localization patterns of lipids and proteins in (1) a transgenic Alzheimer’s disease (AD) mouse model, and in (2) a human xenograft rat model of prostate cancer. The presented image analysis paradigm allows to comprehensively interrogate complex biological systems with single pixel resolution at cellular length scales.


Swedish Research Council (VR, #2014-6447, #2018-02181 JH; #2018-02532, HZ; #2017-00915, KB), Alzheimerfonden (JH, KB), Alzheimer Research UK (ARUK, JH), Hjärnfonden (HZ, KB), Knut and Alice Wallenberg Foundation (HZ), European Research Council (ERC, #681712 HZ), Swedish State Support for Clinical Research (#ALFGBG-720931, HZ), Åhlén-Stiftelsen (JH), Åke Wiberg Foundation (JH), Stiftelsen Gamla Tjänarinnor (PW, JH, KB, WM), Stohnes Stiftelse (PW, JH, WM), Torsten Söderberg Foundation (KB), Demensfonden (PW, WM) and Frimurarestiftelsen (HZ) and the Swedish Cancer Foundation (AB)


Email Address of Submitting Author


University of Gothenburg



ORCID For Submitting Author


Declaration of Conflict of Interest

HZ has served at scientific advisory boards for Roche Diagnostics, Samumed, CogRx and Wave, has given lectures in symposia sponsored by Biogen and Alzecure, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg (all unrelated to the submitted work). KB has served as a consultant or at advisory boards for Abcam, Axon, Biogen, Lilly, MagQu, Novartis and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg, all unrelated to the work presented in this paper. The other authors declare no conflict of interest.

Version Notes

v1.0 (Presubmission Manuscript)