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Updated: Apr 12, 2026
Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry
Published on: April 19, 2017
A novel method for measuring cellular antibody uptake using imaging flow cytometry reveals distinct uptake rates for
John Hazin1, Gerhard Moldenhauer2, Peter Altevogt2
1Department of Translational Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Surgery, Medical Faculty, University of Heidelberg, Heidelberg, Germany; Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Monoclonal antibodies (mAbs) have emerged as a promising tool for cancer therapy. Differing approaches utilize mAbs to either deliver a drug to the tumor cells or to modulate the host's immune system to mediate tumor kill. The rate by which a therapeutic antibody is being internalized by tumor cells is a decisive feature for choosing the appropriate treatment strategy. We herein present a novel method to effectively quantitate antibody uptake of tumor cells by using image-based flow cytometry, which combines image analysis with high throughput of sample numbers and sample size. The use of this method is established by determining uptake rate of an anti-EpCAM antibody (HEA125), from single cell measurements of plasma membrane versus internalized antibody, in conjunction with inhibitors of endocytosis. The method is then applied to two mAbs (L1-9.3, L1-OV52.24) targeting the neural cell adhesion molecule L1 (L1CAM) at two different epitopes. Based on median cell population responses, we find that mAb L1-OV52.24 is rapidly internalized by the ovarian carcinoma cell line SKOV3ip while L1 mAb 9.3 is mainly retained at the cell surface. These findings suggest the L1 mAb OV52.24 as a candidate to be further developed for drug-delivery to cancer cells, while L1-9.3 may be optimized to tag the tumor cells and stimulate immunogenic cancer cell killing. Furthermore, when analyzing cell-to-cell variability, we observed L1 mAb OV52.24 rapidly transition into a subpopulation with high-internalization capacity. In summary, this novel high-content method for measuring antibody internalization rate provides a high level of accuracy and sensitivity for cell population measurements and reveals further biologically relevant information when taking into account cellular heterogeneity.
Monoclonal antibodies (mAbs) have emerged as a promising tool for cancer therapy. Differing approaches utilize mAbs to either deliver a drug to the tumor cells or to modulate the host's immune system to mediate tumor kill. The rate by which a therapeutic antibody is being internalized by tumor cells is a decisive feature for choosing the appropriate treatment strategy. We herein present a novel method to effectively quantitate antibody uptake of tumor cells by using image-based flow cytometry, which combines image analysis with high throughput of sample numbers and sample size. The use of this method is established by determining uptake rate of an anti-EpCAM antibody (HEA125), from single cell measurements of plasma membrane versus internalized antibody, in conjunction with inhibitors of endocytosis. The method is then applied to two mAbs (L1-9.3, L1-OV52.24) targeting the neural cell adhesion molecule L1 (L1CAM) at two different epitopes. Based on median cell population responses, we find that mAb L1-OV52.24 is rapidly internalized by the ovarian carcinoma cell line SKOV3ip while L1 mAb 9.3 is mainly retained at the cell surface. These findings suggest the L1 mAb OV52.24 as a candidate to be further developed for drug-delivery to cancer cells, while L1-9.3 may be optimized to tag the tumor cells and stimulate immunogenic cancer cell killing. Furthermore, when analyzing cell-to-cell variability, we observed L1 mAb OV52.24 rapidly transition into a subpopulation with high-internalization capacity. In summary, this novel high-content method for measuring antibody internalization rate provides a high level of accuracy and sensitivity for cell population measurements and reveals further biologically relevant information when taking into account cellular heterogeneity.
