Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon

Frontiers in Physics

Paul Slezak¹  Angelika Unterhuber²  Martina Marchetti-Deschmann³  Manfred Ogris⁴  Katja Bühler⁵  Dror Fixler⁶  Wolfgang J. Weninger⁷  Stefan H. Geyer⁷  Stephan Handschuh⁸  Martin Glösmann⁸  Thomas Wanek⁹  Andreas Walter^1,10  Ludek Sefc^1,11  Julia G. Mannheim^1,12  Perrine Paul-Gilloteaux^1,15  Paul Verkade^1,16  Birgit Plochberger^1,18 

[1] 0Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, BioImaging Austria/CMI, Vienna, Austria;1Center for Medical Physics and Biomedical Engineering, BioImaging Austria/CMI, Medical University of Vienna, Vienna, Austria;2Mass Spectrometric Bio- and Polymeranalysis, Institute of Chemical Technologies and Analytics, BioImaging Austria/CMI, TU Wien, Vienna, Austria;3Laboratory of MacroMolecular Cancer Therapeutics (MMCT), Department of Pharmaceutical Chemistry, Center of Pharmaceutical Sciences, BioImaging Austria/CMI, University of Vienna, Vienna, Austria;4VRVis Zentrum für Virtual Reality und Visualisierung Forschungs GmbH, BioImaging Austria/CMI, Vienna, Austria;5Laboratory of Advance Microscopy LAB, Faculty of Engineering, Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel;6Division of Anatomy, Center for Anatomy and Cell Biology, BioImaging Austria/CMI, Medical University of Vienna, Vienna, Austria;7VetCore Facility for Research, Imaging Unit, BioImaging Austria/CMI, University of Veterinary Medicine Vienna, Vienna, Austria;8Preclinical Molecular Imaging, BioImaging Austria/CMI, AIT Austrian Institute of Technology GmbH, Seibersdorf, Austria;BioImaging Austria/CMI, Vienna BioCenter Core Facilities, Vienna, Austria;Center for Advanced Preclinical Imaging (CAPI), First Faculty of Medicine, Charles University, Prague, Czechia;Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumor Therapies”, University of Tuebingen, Tübingen, Germany;Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada;Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard-Karls University Tübingen, Tübingen, Germany;Nantes Université, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, Nantes, France;School of Biochemistry, University of Bristol, Bristol, United Kingdom;Université de Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France;Upper Austria University of Applied Sciences, Medical Engineering, BioImaging Austria/CMI, Linz, Austria;

关键词: bioimaging;    correlated multimodal imaging;    CMI;    COMULIS;    CLEM;    correlative microscopy;   

DOI  :  10.3389/fphy.2020.00047

来源: DOAJ

【 摘 要 】

The frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that—in combination—create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three main challenges for its routine use in biomedical research: (1) Sample handling and preparation procedures that are compatible across modalities without compromising data quality, (2) soft- and hardware solutions to relocate the same region of interest (ROI) after transfer between imaging platforms including fiducial markers, and (3) automated software solutions to correlate complex, multiscale, multimodal and volumetric image data including reconstruction, segmentation and visualization. This review goes beyond preclinical imaging and puts accessible information into a broader imaging context. We present a comprehensive overview of the field of CMI from preclinical hybrid imaging to correlative microscopy, highlight requirements for optimization and standardization, present a synopsis of current solutions to challenges of the field and focus on current efforts to bridge the gap between preclinical and biological imaging (from small animals down to single cells and molecules). The review is in line with major European initiatives, such as COMULIS (CA17121), a COST Action to promote and foster Correlated Multimodal Imaging in Life Sciences.

【 授权许可】

Unknown