TY  - JOUR
AU  - Wittenzellner, Katja
AU  - Lengl, Manuel
AU  - Röhrl, Stefan
AU  - Maurer, Carlo
AU  - Klenk, Christian
AU  - Papargyriou, Aristeidis
AU  - Schmidleitner, Laura
AU  - Kabella, Nicole
AU  - Shastri, Akul
AU  - Fresacher, David E
AU  - Harb, Farid
AU  - Hafez, Nawal
AU  - Bärthel, Stefanie
AU  - Lucarelli, Daniele
AU  - Escorial-Iriarte, Carmen
AU  - Orben, Felix
AU  - Öllinger, Rupert
AU  - Emken, Ellen
AU  - Fricke, Lisa
AU  - Madej, Joanna
AU  - Wustrow, Patrick
AU  - Demir, I Ekin
AU  - Friess, Helmut
AU  - Lahmer, Tobias
AU  - Schmid, Roland M
AU  - Rad, Roland
AU  - Schneider, Günter
AU  - Kuster, Bernhard
AU  - Saur, Dieter Karl Maximilian
AU  - Hayden, Oliver
AU  - Diepold, Klaus
AU  - Reichert, Maximilian
TI  - Label-free single-cell phenotyping to determine tumor cell heterogeneity in pancreatic cancer in real time.
JO  - JCI insight
VL  - 10
IS  - 13
SN  - 2379-3708
CY  - Ann Arbor, Michigan
PB  - JCI Insight
M1  - DKFZ-2025-01364
SP  - e169105
PY  - 2025
AB  - 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.
KW  - Pancreatic Neoplasms: pathology
KW  - Pancreatic Neoplasms: genetics
KW  - Single-Cell Analysis: methods
KW  - Humans
KW  - Animals
KW  - Mice
KW  - Carcinoma, Pancreatic Ductal: pathology
KW  - Carcinoma, Pancreatic Ductal: genetics
KW  - Phenotype
KW  - Holography: methods
KW  - Cell Line, Tumor
KW  - Epithelial-Mesenchymal Transition
KW  - Machine Learning
KW  - Cell Differentiation
KW  - Cancer (Other)
KW  - Gastroenterology (Other)
KW  - Oncology (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40424076
DO  - DOI:10.1172/jci.insight.169105
UR  - https://inrepo02.dkfz.de/record/302824
ER  -