3D Cell Kinetics Analyses for Cancer Cell Identification Based on Micro-Vision

Description

We propose to develop an automated, effective and flexible micro vision-based method to analyze a 3D cell kinetic model for identification of cancer cells on an optically-induced dielectropheresis (ODEP) platform. Our group has uniquely observed self-rotation only in cells containing Melan-A using a fluidic-based dielectrophoresis (DEP) system. Melan-A is used to recognize melanocytic differentiation by pathologists for identification of skin cancer cells.Cell kinetic analysis and identification is fundamental to many biology and biotechnology areas, such as diagnostic testing, cell-based screenings for basic science, and surface immunophenotyping for diagnosis. Compared with DEP, optoelectronic tweezers (OET) using the ODEP force solves the problem of the complicated process for microelectrodes fabrication for the conventional manipulation methods of micro-scale cells or particles. OET do not require complex fabrication or preparation processes and the microelectrode pattern can be generated dynamically.In general, cancers are defined by unregulated cell growth. We propose that it is possible to positively identify, grade, and determine the stage of a melanoma, by the 3D cell kinetic analysis including translation motion speed, self-rotation speed and revolution rate around the ODEP electrode for Melan-A cells. The exact mechanism behind this rotation will also be studied in this proposal.In order to automatically estimate the cell translation motion speed, self-rotation speed and revolution rate for rapid cell identification, we propose using micro vision-based algorithms to analyze the image sequences which are acquired by a charge coupled device (CCD)-based microscope system. According to accurate estimates of cell motion, we can build a cell kinetic model for theoretical analytics and assess the criteria eventually for the skin cancer cell identification.The ultimate goal of this project is to demonstrate automated and effective micro vision methods for the ODEP platform, which links a microfluidic system with an embedded ODEP chip, to rapidly identify Melan-A cells. In order to achieve the ultimate goal of this project, we must explore several fundamental issues related to this novel identification technique: 1) understand and accurately model the electrokinetics phenomena under and ODEP force field; 2) develop robust motion tracking algorithms to detect the cell 3D motion including translation and rotation for the microscopic image sequences analysis; 3) study the relationship between the growth stage of a melanoma and the cell kinetic performances for quantitative analysis of the skin cancer cells.The project team will performed detailed experimental work and numerical simulations to understand and solve these fundamental problems through the funding of this project. The key deliverables of this project are: 1) a customized ODEP platform to enable the automated identification of skin cancer cells; 2) micro vision-based algorithms in a software package for the cell motion tracking and 3D kinetic model analysis; 3) an in-depth understanding of the ODEP-based skin cancer cell rotation mechanism through cross-validation of experimental and simulation data obtained from the project.