| Biosensor has been rapidly used in an extensive area especially for non-invasive and long process detection on living cell, as the technology of cell culture and semiconductor micro-processing developed. Recently, the Microelectrode array sensor (MEA), light-addressable potentiometric sensor (LAPS) and electric cell-substrate impedance sensor (ECIS) as well as other extracellular detection methods have been considered as an important means of real-time and dynamic studies on cell activity and drug research. However, these traditional instruments for cell physiological parameters testing can only analyze a physiological parameter which is not enough for the comprehensive evaluation of cells, drugs or the environment. Therefore, we developed an automatic multifunctional cell physiological analyzer. This instrument can detect a new type of cell physiological sensor consisting of three types of sensors (LAPS, MEA and ECIS) which could accomplish the analysis of drugs effects at the cellular level, and detect several cell physiological parameters at the same time.The main task of my studies was to design the software system for automatic multifunctional cell physiological analyzer. The system combined with an integrated chip of cell-based biosensors and the cell physiological analyzer was utilized to detect a variety of physiological parameters. The software is divided into five modules according to the function, which are MEA module, ECIS module, LAPS module, environmental monitoring module and flow injection analysis module. The MEA module is used to detect the action potentials of cells grew at the MEA chip in order to record the cell electrical activity. The ECIS module relies on the interdigital electrodes to detect changes in cell impedance which reflects the cell growth, adhesion, proliferation, apoptosis and effects of drugs on cell activity. The LAPS module calculates the changes of extracellular ions concentrations which reflects the cellular metabolic. The environmental monitoring module monitors the various changes of cell cultural environment, such as temperature, humidity and CO2 concentration, and the module controls the environment in real-time to ensure that the cells will grow in normal situation. The flow injection analysis module is used to accomplish the sampling and cleaning of the medium and drugs with the instrument.The interface of the software is greatly improved compared with traditional data acquisition software. The whole framework was based on the Microsoft.NET Framework 3.5 which supported diverse operating system platforms of Microsoft Windows series. Several functions and modules are able to run simultaneously due to the Multi-task approach is adopted. Basic functional modules were sealed and packaged in order to expand the scope of application of the software and reduce the difficulties of secondary development.In addition, identification algorithms based on the multi-parameter cell physiology were investigated. Automatic detection and identification algorithms for the peak of action potential were designed for MEA. Real-time extraction of peaks was achieved with the moving window integration method. The algorithm for cell index and the drug 50% inhibitory concentration were programmed for ECIS and the LOGIT model combined with the least squares fit completed the online drug 50% inhibitory concentration calculation. The algorithms for automatic detection of operating points on I-V curves which detected by LAPS were laid out and the cubic spline interpolation with smoothing integral filter were used to determine the operating points of the I-V curve and calculate the ion concentration. The combination of the algorithms can detected the effects of the drugs on cell action potential morphology and duration, the impacts of drugs on cell growth status and the number of cells, as well as the influences on cell metabolism.The testing experiments of drugs and cells with our analyzer verified that the real-time peak extraction algorithm acquired a reasonable range of detection threshold and the accuracy rate over 90% in signal to noise ratio between 1.5 and 3.5. Automatic identification algorithm for the drug 50% inhibitory concentration calculates the IC50 curve of the doxorubicin effect on rat cardiac muscle cells. Besides, it is determined that 30mV is the optimal sampling interval for I-V curve and the relative error is 13.5±1.9% after analyzing the errors of the automatic operating point detection algorithms in a variety of ion conditions. Therefore, results of experiment verified the correctness of algorithms and reliability of the system. The software with a flexible and convenient user interface can be widely used in pharmacological, biomedical research and environment monitoring. |
PDF Full Text Request