Fourier transform infrared microscopy enables guidance of automated mass spectrometry imaging to predefined tissue morphologies

• Verfasst von: Rabe, Jan-Hinrich [VerfasserIn]
• Verfasst von: Hohenberger, Peter [VerfasserIn]
• Verfasst von: Marx, Alexander [VerfasserIn]
• Verfasst von: Platten, Michael [VerfasserIn]
• Verfasst von: Opitz, Christiane [VerfasserIn]
• Verfasst von: Hopf, Carsten [VerfasserIn]
• Titel: Fourier transform infrared microscopy enables guidance of automated mass spectrometry imaging to predefined tissue morphologies
• Verf.angabe: Jan-Hinrich Rabe, Denis A. Sammour, Sandra Schulz, Bogdan Munteanu, Martina Ott, Katharina Ochs, Peter Hohenberger, Alexander Marx, Michael Platten, Christiane A. Opitz, Daniel S. Ory and Carsten Hopf
• E-Jahr: 2018
• Jahr: 10 January 2018
• Umfang: 11 S.
• Fussnoten: Gesehen am 28.04.2020
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Springer Nature, 2011
• Jahr Quelle: 2018
• Band/Heft Quelle: 8(2018) Artikel-Nummer 313, 11 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-017-18477-6
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1696788102

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.