Microfluidic Impedance Sensing Devices Based Single Cell Detection And Applications

Single cell research is the foundation of life sciences.Studies on cell heterogeneity promote the development of various methods for single cell research.The state-of-art method for single cell study is quantification of phenotypic heterogeneity by fluorescence-activated flow cytometry.Recently,with the development of micro-nano fabrication and microfluidic technology,microfluidic systems based on optical,electrical,acoustic,magnetic or integrated techniques are emerging as powerful tools for single cell study.Due to the advantages of high-throughput and label-free manner,the impedance-based methods are widely applied for cell biophysical studies to reveal the electrophysiological differences between cells.In this thesis,a series of microfluidic devices and systems allowing single cell impedance detection were developed for single cell study.By designing and optimizing of microfluidic channels and electrodes,impedance signals of single cells were extracted under dynamic manner(flow cytometry)and the static manner(single cell trapping).Combined with the deeplearning assisted data analysis method,cell and bacteria classification,and biophysical parameter characterization of different types of cells were accomplished.The main topics and results of this thesis are listed as follows:1.The detection principle and design method(structural design,dimensional design,material selection,etc.)of the microfluidic impedance flow cytometry chip were introduced.Fabrication method,measuring system setup and data processing method were introduced as well.The proposed devices were then tested with polystyrene particles,He La cells,Escherichia coli,and lambda DNA molecules,for particle size detection,cell and particle distinction,bacterial concentration detection and DNA molecular concentration detection,respectively,to verify the feasibility of the proposed devices.2.According to the demand for accurate cell classification,as well as aiming at the bottle-neck issue due to the huge amount of processing data in data analysis of impedance flow cytometry,a deep-learning assisted impedance data analysis method was proposed and applied to type classifications of urinary cells and food-borne bacteria.Firstly,a deep learning algorithm model based on convolutional neural network was established to match the analysis of multi-dimensional impedance characteristic parameters.Secondly,one type of normal urothelial cell and three types of bladder cancer cells were detected and analyzed,and the cell classification and typing study was carried out with the goal of distinguishing normal cells from cancer cells and different grades of cancer cells.Thirdly,based on optimization of characteristic parameters,the deep learning assisted bacterial classification was further carried out on three different food-borne bacteria(1 bacillus,1 vibrio,and 1 cocci).3.According to the demand for multi-dimensional phenotyping of biophysical properties,an impedance flow cytometry device with a constriction channel was proposed based on the re-design of layout and dimensions of microchannel and electrodes.The capacity for simultaneous detection of cell mechanical and electrical properties extends the applications of microfluidic impedance sensing devices.Toward plant cell study,the differences of Arabidopsis and Populus protoplasts,as well as wildtype and mutant Arabidopsis protoplasts were studied by mechanical and electrical properties.Furthermore,the primary cell wall regeneration process was studied by mechanical and electrical properties at single cell level,and the effect of growth hormone on the primary cell wall regeneration was further compared.Toward animal cell study,the normal cells and cancer cells were studied and compared by mechanical properties,and verified by cytoskeleton staining.4.According to the demand for long-term in-situ single cell impedance measurement and the limitation of impedance flow cytometry device,a single cell trapping and impedance sensing device was proposed based on design of single cell trapping structure and integration of sensing electrodes.As a complementary method to impedance flow cytometry,the proposed device can provide in-situ impedance information of an individual cell.Firstly,the dimensional design of the single cell trapping structure was guided and optimized with theoretical calculations and finite element simulation.The animal cells with different viabilities and plant cells of different state were further characterized by single cell impedance spectrum.