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New automatic quantification method of immunofluorescence and histochemistry in whole histological sections.
Cell. Signal. 62:109335 (2019)
Immunofluorescent staining is a widespread tool in basic science to understand organ morphology and (patho-) physiology. The analysis of imaging data is often performed manually, limiting throughput and introducing human bias. Quantitative analysis is particularly challenging for organs with complex structure such as the kidney. In this study we present an approach for automatic quantification of fluorescent markers and histochemical stainings in whole organ sections using open source software. We validate our novel method in multiple typical challenges of basic kidney research and demonstrate its general relevance and applicability to other complex solid organs for a variety of different markers and stainings. Our newly developed software tool "AQUISTO", applied as a standard in primary data analysis, facilitates efficient large scale evaluation of cellular populations in various types of histological samples. Thereby it contributes to the characterization and understanding of (patho-) physiological processes.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
3.388
0.862
4
4
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Immunofluorescence ; Histochemistry ; Automatic Quantification ; Cell Count ; Morphology; Renin Lineage Cells; Beta
Sprache
englisch
Veröffentlichungsjahr
2019
HGF-Berichtsjahr
2019
ISSN (print) / ISBN
0898-6568
e-ISSN
0898-6568
Zeitschrift
Cellular Signalling
Quellenangaben
Band: 62,
Artikelnummer: 109335
Verlag
Elsevier
Verlagsort
Ste 800, 230 Park Ave, New York, Ny 10169 Usa
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Pancreatic Islet Research (IPI)
POF Topic(s)
90000 - German Center for Diabetes Research
Forschungsfeld(er)
Helmholtz Diabetes Center
PSP-Element(e)
G-502600-005
WOS ID
WOS:000480669100011
Scopus ID
85067235530
PubMed ID
31170471
Erfassungsdatum
2019-06-12