Semi-Automated Quantification of Living Cells with Internalized Nanostructures

by Margineanu, M. B., Julfakyan, K., Sommer, C., Perez., J. E., Contreras, M. F., Khashab, N., Kosel, J, Ravasi, T.
Year: 2016


Margineanu, M. B.; Julfakyan, K.; Sommer, C.; Perez., J. E.; Contreras, M. F.; Khashab, N.; Kosel, J; Ravasi, T. Semi-Automated Quantification of Living Cells with Internalized Nanostructures. J. Nanobiotechnology 2016, 14, 4, DOI: 10.1186/s12951-015-0153-x


Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a "proof-of-concept", we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays.


Nanomedicine Nanoparticle NWs Magnetic Quantification Live-cell Imaging Machine Learning Computational Methods