Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Computational detection and quantification of human and mouse neutrophil extracellular traps in flow cytometry and confocal microscopy
Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Immunologi/immunkemi. Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). (Constantin Urban)
Visa övriga samt affilieringar
2017 (Engelska)Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, nr 1, artikel-id 17755Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Neutrophil extracellular traps (NETs) are extracellular defense mechanisms used by neutrophils, where chromatin is expelled together with histones and granular/cytoplasmic proteins. They have become an immunology hotspot, implicated in infections, but also in a diverse array of diseases such as systemic lupus erythematosus, diabetes, and cancer. However, the precise assessment of in vivo relevance in different disease settings has been hampered by limited tools to quantify occurrence of extracellular traps in experimental models and human samples. To expedite progress towards improved quantitative tools, we have developed computational pipelines to identify extracellular traps from an in vitro human samples visualized using the ImageStream® platform (Millipore Sigma, Darmstadt, Germany), and confocal images of an in vivo mouse disease model of aspergillus fumigatus pneumonia. Our two in vitro methods, tested on n = 363/n =145 images respectively, achieved holdout sensitivity/specificity 0.98/0.93 and 1/0.92. Our unsupervised method for thin lung tissue sections in murine fungal pneumonia achieved sensitivity/specificity 0.99/0.98 in n = 14 images. Our supervised method for thin lung tissue classified NETs with sensitivity/specificity 0.86/0.90. We expect that our approach will be of value for researchers, and have application in infectious and inflammatory diseases.

Ort, förlag, år, upplaga, sidor
Nature Publishing Group, 2017. Vol. 7, nr 1, artikel-id 17755
Nationell ämneskategori
Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)
Identifikatorer
URN: urn:nbn:se:umu:diva-143398DOI: 10.1038/s41598-017-18099-yISI: 000418359600005PubMedID: 29259241OAI: oai:DiVA.org:umu-143398DiVA, id: diva2:1168792
Tillgänglig från: 2017-12-21 Skapad: 2017-12-21 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

Open Access i DiVA

fulltext(11604 kB)101 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 11604 kBChecksumma SHA-512
3e4cdb6a0289b3cd382d6d4c8b9e4788008c79b51ad55dd3dff6d3b1fea28822d5aa055db4f34f97df18018461a35986666341290fde532b8c022c0120007525
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextPubMed

Sök vidare i DiVA

Av författaren/redaktören
Urban, Constantin F
Av organisationen
Immunologi/immunkemiUmeå Centre for Microbial Research (UCMR)
I samma tidskrift
Scientific Reports
Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 101 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
pubmed
urn-nbn

Altmetricpoäng

doi
pubmed
urn-nbn
Totalt: 457 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf