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Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Authors
  • Vinegoni, Claudio1
  • Fumene Feruglio, Paolo1, 2
  • Courties, Gabriel1
  • Schmidt, Stephen1
  • Hulsmans, Maarten1
  • Lee, Sungon3
  • Wang, Rui4, 5
  • Sosnovik, David6, 7
  • Nahrendorf, Matthias1, 8
  • Weissleder, Ralph1, 9
  • 1 Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 2 Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy , Verona (Italy)
  • 3 School of Electrical Engineering, Hanyang University, Ansan, Republic of Korea , Ansan (South Korea)
  • 4 Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 5 Department of Biostatistics, Harvard T. H. Chan School of Public Health Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 6 Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 7 Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 8 Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA , Boston (United States)
  • 9 Department of Systems Biology, Harvard Medical School, Boston, MA, USA , Boston (United States)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Mar 27, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-62233-2
Source
Springer Nature
License
Green

Abstract

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

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