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Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow

Manuel Gunkel, Inn Chung, Stefan Wörz, Katharina I. Deeg, Ronald Simon, Guido Sauter, David T.W. Jones, Andrey Korshunov, Karl Rohr, Holger Erfle, View ORCID ProfileKarsten Rippe
doi: https://doi.org/10.1101/053132
Manuel Gunkel
1 VIROQUANT CellNetworks RNAi Screening Facility, Bioquant Center, University of Heidelberg
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Inn Chung
2 Research Group Genome Organization & Function, German Cancer Research Center (DKFZ) and Bioquant Center
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Stefan Wörz
3 Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Bioquant Center and IPMB, University of Heidelberg and German Cancer Research Center (DKFZ)
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Katharina I. Deeg
2 Research Group Genome Organization & Function, German Cancer Research Center (DKFZ) and Bioquant Center
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Ronald Simon
4 Department of Pathology, University Medical Center Hamburg-Eppendorf
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Guido Sauter
4 Department of Pathology, University Medical Center Hamburg-Eppendorf
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David T.W. Jones
5 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ)
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Andrey Korshunov
6 Department of Neuropathology, Heidelberg University Hospital
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Karl Rohr
3 Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Bioquant Center and IPMB, University of Heidelberg and German Cancer Research Center (DKFZ)
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Holger Erfle
1 VIROQUANT CellNetworks RNAi Screening Facility, Bioquant Center, University of Heidelberg
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Karsten Rippe
2 Research Group Genome Organization & Function, German Cancer Research Center (DKFZ) and Bioquant Center
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  • ORCID record for Karsten Rippe
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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 provides 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 sub-populations 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.

3D-TIM
3D targeted imaging
ALT
alternative lengthening of telomeres
APB
ALT-associated PML-NB
CLSM
confocal laser scanning fluorescence microscopy
ECTR
extrachromosomal telomeric repeat
FFPE
formalin-fixed, paraffin-embedded
FISH
fluorescence in situ hybridization
IF
Immunofluorescence
pedGBM
pediatric glioblastoma
PML
promyelocytic leukemia
PML-NB
PML nuclear body
PNA
peptide nucleic acid
ROI
region of interest
TMA
tissue microarray
TMM
telomere maintenance mechanism
SMLM
single molecule localization microscopy

Footnotes

  • ↵# shared first authors

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 12, 2016.
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Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow
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
bioRxiv 053132; doi: https://doi.org/10.1101/053132
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Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow
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
bioRxiv 053132; doi: https://doi.org/10.1101/053132

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