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@ARTICLE{Wittenzellner:302824,
      author       = {K. Wittenzellner and M. Lengl and S. Röhrl and C. Maurer
                      and C. Klenk and A. Papargyriou and L. Schmidleitner and N.
                      Kabella and A. Shastri and D. E. Fresacher and F. Harb and
                      N. Hafez and S. Bärthel$^*$ and D. Lucarelli$^*$ and C.
                      Escorial-Iriarte and F. Orben and R. Öllinger and E. Emken
                      and L. Fricke and J. Madej and P. Wustrow and I. E. Demir
                      and H. Friess and T. Lahmer and R. M. Schmid and R. Rad and
                      G. Schneider and B. Kuster and D. K. M. Saur$^*$ and O.
                      Hayden and K. Diepold and M. Reichert$^*$},
      title        = {{L}abel-free single-cell phenotyping to determine tumor
                      cell heterogeneity in pancreatic cancer in real time.},
      journal      = {JCI insight},
      volume       = {10},
      number       = {13},
      issn         = {2379-3708},
      address      = {Ann Arbor, Michigan},
      publisher    = {JCI Insight},
      reportid     = {DKFZ-2025-01364},
      pages        = {e169105},
      year         = {2025},
      abstract     = {Resistance to chemotherapy of pancreatic ductal
                      adenocarcinoma (PDAC) is largely driven by intratumoral
                      heterogeneity (ITH) due to tumor cell plasticity and clonal
                      diversity. To develop alternative strategies to overcome
                      this defined mechanism of resistance, tools to monitor and
                      quantify ITH in a rapid and scalable fashion are needed
                      urgently. Here, we employed label-free digital holographic
                      microscopy (DHM) to characterize ITH in PDAC. We established
                      a robust experimental and machine learning analysis pipeline
                      to perform single-cell phenotyping based on DHM-derived
                      phase images of PDAC cells in suspension. Importantly, we
                      were able to detect dynamic changes in tumor cell
                      differentiation and heterogeneity of distinct PDAC subtypes
                      upon induction of epithelial-mesenchymal transition and
                      under treatment-imposed pressure in murine and
                      patient-derived model systems. This platform allowed us to
                      assess phenotypic ITH in PDAC on a single-cell level in real
                      time. Implementing this technology into the clinical
                      workflow has the potential to fundamentally increase our
                      understanding of tumor heterogeneity during evolution and
                      treatment response.},
      keywords     = {Pancreatic Neoplasms: pathology / Pancreatic Neoplasms:
                      genetics / Single-Cell Analysis: methods / Humans / Animals
                      / Mice / Carcinoma, Pancreatic Ductal: pathology /
                      Carcinoma, Pancreatic Ductal: genetics / Phenotype /
                      Holography: methods / Cell Line, Tumor /
                      Epithelial-Mesenchymal Transition / Machine Learning / Cell
                      Differentiation / Cancer (Other) / Gastroenterology (Other)
                      / Oncology (Other)},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40424076},
      doi          = {10.1172/jci.insight.169105},
      url          = {https://inrepo02.dkfz.de/record/302824},
}