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Status: Bibliographieeintrag

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Verfasst von:Gunkel, Manuel [VerfasserIn]   i
 Chung, Inn [VerfasserIn]   i
 Wörz, Stefan [VerfasserIn]   i
 Deeg, Katharina [VerfasserIn]   i
 Korshunov, Andrey [VerfasserIn]   i
 Rohr, Karl [VerfasserIn]   i
 Erfle, Holger [VerfasserIn]   i
 Rippe, Karsten [VerfasserIn]   i
Titel:Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow
Verf.angabe:Manuel Gunkel, Inn Chung, Stefan Wörz, Katharina I. Deeg, Ronald Simon, Guido Sauter, David T. W. Jones, Andrey Korshunov, Karl Rohr, Holger Erfle, Karsten Rippe
E-Jahr:2017
Jahr:7 October 2016
Umfang:14 S.
Fussnoten:Available online 7 October 2016 ; Gesehen am 09.08.2018
Titel Quelle:Enthalten in: Methods
Ort Quelle:Orlando, Fla. : Academic Press, 1990
Jahr Quelle:2017
Band/Heft Quelle:114(2017), Seite 60-73
ISSN Quelle:1095-9130
Abstract:The microscopic analysis of telomere features provides a wealth of information on the mechanism by which tumor cells maintain their unlimited proliferative potential. Accordingly, the analysis of telomeres in tissue sections of patient tumor samples can be exploited to obtain diagnostic information and to define tumor subgroups. In many instances, however, analysis of the image data is conducted by manual inspection of 2D images at relatively low resolution for only a small part of the sample. As the telomere feature signal distribution is frequently heterogeneous, this approach is prone to a biased selection of the information present in the image and lacks subcellular details. Here we address these issues by using an automated high-resolution imaging and analysis workflow that quantifies individual telomere features on tissue sections for a large number of cells. The approach is particularly suited to assess telomere heterogeneity and low abundant cellular subpopulations with distinct telomere characteristics in a reproducible manner. It comprises the integration of multi-color fluorescence in situ hybridization, immunofluorescence and DNA staining with targeted automated 3D fluorescence microscopy and image analysis. We apply our method to telomeres in glioblastoma and prostate cancer samples, and describe how the imaging data can be used to derive statistically reliable information on telomere length distribution or colocalization with PML nuclear bodies. We anticipate that relating this approach to clinical outcome data will prove to be valuable for pretherapeutic patient stratification.
DOI:doi:10.1016/j.ymeth.2016.09.014
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: http://dx.doi.org/10.1016/j.ymeth.2016.09.014
 Volltext: https://www.sciencedirect.com/science/article/pii/S1046202316303280?via%3Dihub
 DOI: https://doi.org/10.1016/j.ymeth.2016.09.014
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Alternative lengthening of telomeres
 Automation
 Child
 FFPE tissue
 Fluorescence microscopy
 Fluorescent Antibody Technique
 Glioblastoma
 Humans
 Image analysis
 Image Processing, Computer-Assisted
 Imaging, Three-Dimensional
 In Situ Hybridization, Fluorescence
 Male
 Microscopy, Confocal
 Paraffin Embedding
 Prostate cancer
 Prostatic Neoplasms
 Telomere
 Workflow
K10plus-PPN:157843081X
Verknüpfungen:→ Zeitschrift

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