A novel method for measuring cellular antibody uptake using imaging flow cytometry reveals distinct uptake rates for two different monoclonal antibodies targeting L1.

Research paper by John J Hazin, Gerhard G Moldenhauer, Peter P Altevogt, Nathan R NR Brady

Indexed on: 15 May '15Published on: 15 May '15Published in: Journal of Immunological Methods


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.