Abstract

Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.

Highlights

  • Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine

  • This represents a substantial advantage when compared to previous image fusion approaches[12,14,40] that rely on hematoxylin and eosin (H&E) images of adjacent sections for interpretation

  • Data acquisition on the same tissue section represents a significant upgrade by further improving registration efficiency and minimizing deficiencies caused by sectioning artifacts, such as tears and folds

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Summary

Introduction

Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Final interpretation of the resulting fusion images is eventually left to a scientist’s or physician’s annotation that is typically based on only a small part of the acquired data Another concept of multimodality is currently emerging, according to which one modality such as manual annotation of digitized H&E stained slices predefines advantageous features (such as spatial distribution patterns), which are used to guide data acquisition and interpretation by another complementary modality[13,14,15,16,17]. This extension of the general multimodal imaging concept holds the potential to drastically decrease data volumes and, computational cost.

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