Nanostructures fabricated by different methods have become increasingly important for various applications at the cellular level. In order to understand how these nanostructures “behave” and for studying their internalization kinetics, several attempts have been made at tagging and investigating their interaction with living cells. In this study, magnetic iron nanowires with an iron oxide layer are coated with (3-Aminopropyl)triethoxysilane (APTES), and subsequently labeled with a fluorogenic pH-dependent dye pHrodo™ Red, covalently bound to the aminosilane surface. Time-lapse live imaging of human colon carcinoma HCT 116 cells interacting with the labeled iron nanowires is performed for 24 hours. As the pHrodo™ Red conjugated nanowires are non-fluorescent outside the cells but fluoresce brightly inside, internalized nanowires are distinguished from non-internalized ones and their behavior inside the cells can be tracked for the respective time length. A machine learning-based computational framework dedicated to automatic analysis of live cell imaging data, Cell Cognition, is adapted and used to classify cells with internalized and non-internalized nanowires and subsequently determine the uptake percentage by cells at different time points. An uptake of 85 % by HCT 116 cells is observed after 24 hours incubation at NW-to-cell ratios of 200. While the approach of using pHrodo™ Red for internalization studies is not novel in the literature, this study reports for the first time the utilization of a machine-learning based time-resolved automatic analysis pipeline for quantification of nanowire uptake by cells. This pipeline has also been used for comparison studies with nickel nanowires coated with APTES and labeled with pHrodo™ Red, and another cell line derived from the cervix carcinoma, HeLa. It has thus the potential to be used for studying the interaction of different types of nanostructures with potentially any live cell types.
|Date of Award||May 2015|
- Biological, Environmental Science and Engineering
|Supervisor||Timothy Ravasi (Supervisor)|
- Live Cells
- Image analysis