Label-free single-cell phenotyping to determine tumor cell heterogeneity in pancreatic cancer in real time
Katja Wittenzellner, … , Klaus Diepold, Maximilian Reichert
Katja Wittenzellner, … , Klaus Diepold, Maximilian Reichert
Published May 27, 2025
Citation Information: JCI Insight. 2025;10(13):e169105. https://doi.org/10.1172/jci.insight.169105.
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Research Article Gastroenterology Oncology

Label-free single-cell phenotyping to determine tumor cell heterogeneity in pancreatic cancer in real time

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.

Authors

Katja Wittenzellner, Manuel Lengl, Stefan Röhrl, Carlo Maurer, Christian Klenk, Aristeidis Papargyriou, Laura Schmidleitner, Nicole Kabella, Akul Shastri, David E. Fresacher, Farid Harb, Nawal Hafez, Stefanie Bärthel, Daniele Lucarelli, Carmen Escorial-Iriarte, Felix Orben, Rupert Öllinger, Ellen Emken, Lisa Fricke, Joanna Madej, Patrick Wustrow, I. Ekin Demir, Helmut Friess, Tobias Lahmer, Roland M. Schmid, Roland Rad, Günter Schneider, Bernhard Kuster, Dieter Saur, Oliver Hayden, Klaus Diepold, Maximilian Reichert

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Figure 2

DHM-based identification of TGF-β– and genetically induced EMT.

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DHM-based identification of TGF-β– and genetically induced EMT. (A) Phas...
(A) Phase contrast images of control and TGF-β–treated epithelial PDAC cells. Scale bars represent 200 μm. (B) Representative DHM phase images in false colors of control and TGF-β–treated PDAC cells in suspension. Scale bar represents 10 μm. (C) Accuracy for separating control and TGF-β–treated PDAC cells individually for every cell line using different classification methods: random forest (RF), support vector machine (SVM), k-nearest neighbors (K-NN), and neural network (NN). Shown are the median and upper and lower quartiles. (D) Unsupervised clustering of control and TGF-β–treated PDAC cells based on DHM phase images and visualized using UMAP plots. (E) Representative phase contrast (left) and DHM phase images in false colors (right) of cells with p120catenin wild-type (p120+/+) or homozygous (p120–/–) deletion. Scale bars represent 200 μm (left) and 10 μm (right). (F) Accuracy for separating p120+/+ and p120–/– cells using different classification methods as in C. Shown are the median and upper and lower quartiles. (G) Unsupervised clustering of p120+/+ and p120–/– cells based on DHM phase images and visualized using UMAP plots.