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@ARTICLE{Gunkel:119334,
      author       = {M. Gunkel and I. Chung$^*$ and S. Wörz$^*$ and K. Deeg$^*$
                      and R. Simon and G. Sauter and D. Jones$^*$ and A.
                      Korshunov$^*$ and K. Rohr$^*$ and H. Erfle and K. Rippe$^*$},
      title        = {{Q}uantification of telomere features in tumor tissue
                      sections by an automated 3{D} imaging-based workflow.2},
      journal      = {Methods},
      volume       = {114},
      issn         = {1046-2023},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DKFZ-2017-00089},
      pages        = {60 - 73},
      year         = {2017},
      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.},
      cin          = {B066 / B080 / B062 / G380},
      ddc          = {540},
      cid          = {I:(DE-He78)B066-20160331 / I:(DE-He78)B080-20160331 /
                      I:(DE-He78)B062-20160331 / I:(DE-He78)G380-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27725304},
      doi          = {10.1016/j.ymeth.2016.09.014},
      url          = {https://inrepo02.dkfz.de/record/119334},
}