VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.
Identifieur interne : 000047 ( PubMed/Curation ); précédent : 000046; suivant : 000048VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.
Auteurs : René H Gerling [Allemagne] ; Dominik Drees ; Aaron Scherzinger ; Cathrin Dierkes [Allemagne] ; Silvia Martin-Almedina [Royaume-Uni] ; Stefan Butz [Allemagne] ; Kristiana Gordon [Royaume-Uni] ; Michael Sch Fers [Allemagne] ; Klaus Hinrichs [Allemagne] ; Pia Ostergaard [Royaume-Uni] ; Dietmar Vestweber [Allemagne] ; Tobias Goerge [Allemagne] ; Sahar Mansour [Royaume-Uni] ; Xiaoyi Jiang ; Peter S. Mortimer [Royaume-Uni] ; Friedemann Kiefer [Allemagne]Source :
- JCI insight [ 2379-3708 ] ; 2017.
Abstract
Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections.
DOI: 10.1172/jci.insight.93424
PubMed: 28814672
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Dominik Drees<affiliation><nlm:affiliation>Pattern Recognition and Image Analysis Group, Department of Computer Science, and.</nlm:affiliation>
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<affiliation><nlm:affiliation>Pattern Recognition and Image Analysis Group, Department of Computer Science, and.</nlm:affiliation>
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<front><div type="abstract" xml:lang="en">Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections.</div>
</front>
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<DateCreated><Year>2017</Year>
<Month>08</Month>
<Day>17</Day>
</DateCreated>
<DateRevised><Year>2017</Year>
<Month>08</Month>
<Day>17</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2379-3708</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>2</Volume>
<Issue>16</Issue>
<PubDate><Year>2017</Year>
<Month>Aug</Month>
<Day>17</Day>
</PubDate>
</JournalIssue>
<Title>JCI insight</Title>
<ISOAbbreviation>JCI Insight</ISOAbbreviation>
</Journal>
<ArticleTitle>VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.</ArticleTitle>
<ELocationID EIdType="doi" ValidYN="Y">10.1172/jci.insight.93424</ELocationID>
<ELocationID EIdType="pii" ValidYN="Y">93424</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%).</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin.</AbstractText>
<AbstractText Label="FUNDING" NlmCategory="BACKGROUND">Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hägerling</LastName>
<ForeName>René</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Mammalian Cell Signaling Laboratory, Max Planck Institute for Molecular Biomedicine, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Drees</LastName>
<ForeName>Dominik</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Pattern Recognition and Image Analysis Group, Department of Computer Science, and.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Scherzinger</LastName>
<ForeName>Aaron</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Pattern Recognition and Image Analysis Group, Department of Computer Science, and.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Visualization and Computer Graphics Group, Department of Computer Science, University of Münster, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Dierkes</LastName>
<ForeName>Cathrin</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Mammalian Cell Signaling Laboratory, Max Planck Institute for Molecular Biomedicine, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Martin-Almedina</LastName>
<ForeName>Silvia</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Molecular and Clinical Sciences Institute, St. George's University of London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Butz</LastName>
<ForeName>Stefan</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Department Vascular Cell Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Gordon</LastName>
<ForeName>Kristiana</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Molecular and Clinical Sciences Institute, St. George's University of London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Schäfers</LastName>
<ForeName>Michael</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>European Institute for Molecular Imaging, University of Münster, Münster, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>DFG Cells-in-Motion Cluster of Excellence 1003, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hinrichs</LastName>
<ForeName>Klaus</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Visualization and Computer Graphics Group, Department of Computer Science, University of Münster, Münster, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>DFG Cells-in-Motion Cluster of Excellence 1003, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ostergaard</LastName>
<ForeName>Pia</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Molecular and Clinical Sciences Institute, St. George's University of London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vestweber</LastName>
<ForeName>Dietmar</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department Vascular Cell Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Goerge</LastName>
<ForeName>Tobias</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Department of Dermatology, University Hospital of Münster, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mansour</LastName>
<ForeName>Sahar</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Molecular and Clinical Sciences Institute, St. George's University of London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jiang</LastName>
<ForeName>Xiaoyi</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Pattern Recognition and Image Analysis Group, Department of Computer Science, and.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>DFG Cells-in-Motion Cluster of Excellence 1003, Münster, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mortimer</LastName>
<ForeName>Peter S</ForeName>
<Initials>PS</Initials>
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</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Kiefer</LastName>
<ForeName>Friedemann</ForeName>
<Initials>F</Initials>
<AffiliationInfo><Affiliation>Mammalian Cell Signaling Laboratory, Max Planck Institute for Molecular Biomedicine, Münster, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>European Institute for Molecular Imaging, University of Münster, Münster, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>DFG Cells-in-Motion Cluster of Excellence 1003, Münster, Germany.</Affiliation>
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<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2017</Year>
<Month>08</Month>
<Day>17</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>JCI Insight</MedlineTA>
<NlmUniqueID>101676073</NlmUniqueID>
<ISSNLinking>2379-3708</ISSNLinking>
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<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year>
<Month>05</Month>
<Day>05</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2017</Year>
<Month>07</Month>
<Day>14</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2017</Year>
<Month>8</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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<PubMedPubDate PubStatus="pubmed"><Year>2017</Year>
<Month>8</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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<PubMedPubDate PubStatus="medline"><Year>2017</Year>
<Month>8</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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<PublicationStatus>aheadofprint</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">28814672</ArticleId>
<ArticleId IdType="pii">93424</ArticleId>
<ArticleId IdType="doi">10.1172/jci.insight.93424</ArticleId>
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