CN113533178B - Multi-physical-characteristic fusion-sensing cell flow detection method - Google Patents
Multi-physical-characteristic fusion-sensing cell flow detection method Download PDFInfo
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Abstract
The invention discloses a multi-physical characteristic fusion perception cell flow detection method, relates to the technical field of cell detection, and solves the technical problems of low precision and efficiency when multi-physical characteristic detection is carried out on cells. The detection parameters are physical attributes of cells, the method belongs to a non-labeling technology, and the problems of complex operation procedures, high specificity, cell damage, high detection cost and the like of the traditional immune biochemical detection method are effectively solved.
Description
Technical Field
The disclosure relates to the technical field of cell detection, in particular to a multi-physical-characteristic fusion-sensing cell flow detection method.
Background
The control and detection of cells are the core technical functions of clinical examination instruments, and belong to the field of multidisciplinary cross research of mechanical engineering, information technology and life science. The traditional clinical examination instrument depends on a complex immune labeling technology for realizing cell control and detection, and has the problems of complex operation procedures, high specificity, high detection cost and the like. The non-labeled cell control and detection method based on the physical mechanical principle can effectively overcome the defects. Meanwhile, the miniaturization development from the cabinet type to the portable type is also the inevitable development trend of the next-generation new concept inspection instrument from the laboratory to the bedside analysis or the household detection. In recent years, the explosion of micro-electromechanical Systems (MEMS) provides a new idea for developing the next generation of miniaturized clinical testing instruments. As a typical application of a micro-electro-mechanical system in life science, the micro-fluidic chip is gradually applied to non-label control and detection of cells due to the advantages of high precision, low cost, integrated miniaturization and the like, and is expected to provide a core technical unit for developing a next-generation miniaturized clinical inspection instrument.
However, the complex heterogeneous nature of cells remains a significant challenge that prevents high-precision detection of cells. For the detection of rare cells, the precision still needs to be further improved, and how to improve the detection precision of complex heterogeneous cells is still a key scientific problem which needs to be solved urgently in the field of non-labeled detection. At present, the morphological and structural characteristics of cells are the most direct and common technical scale for cell identification, but the traditional morphological and structural characteristics detection depends on time-consuming microscopic examination. The mechanical properties of a cell are another important physical property of a cell and are directly related to the physiological state of the cell and the occurrence and development of a disease. Traditional techniques for detecting mechanical properties of single cells include: atomic force microscopy, micropipette technology, optical tweezers, and optical stretching and pinching methods, which generally have high precision but relatively low detection efficiency (typically less than one cell per minute), have limited their use to laboratory studies. For large sample requirements of clinical assays, a method for realizing high-throughput detection of mechanical properties of cells under flow conditions is required. Meanwhile, only the detection of a single physical property is found in the current research, and the high-precision detection of cells fusing various different physical properties is not developed yet.
Disclosure of Invention
The invention provides a multi-physical-characteristic fusion-sensing cell flow detection method, which is technically used for fusing various physical characteristics such as morphological structure characteristics of cells and mechanical characteristics of the cells on the basis of multi-element electric fingerprint characteristic detection so as to meet the high-precision requirement for accurate identification of rare cells.
The technical purpose of the present disclosure is achieved by the following technical solutions:
a multi-physical characteristic fusion perception cell flow detection method is characterized in that a detection system is used for detecting, the detection system comprises a micro-fluidic chip, a high-speed microscopic imaging system and a CPU (Central processing Unit), the micro-fluidic chip comprises an inertial focusing unit and a broadband electrical impedance hardware system, the broadband electrical impedance hardware system is connected with the inertial focusing unit and the CPU, the high-speed microscopic imaging system is connected with the CPU, and the CPU is used for controlling the broadband electrical impedance hardware system and the high-speed microscopic imaging system to complete data exchange and analysis; the high-speed microscopic imaging system is positioned in the vertical direction of the broadband electrical impedance hardware system, and the high-speed microscopic imaging system and the broadband electrical impedance hardware system work synchronously during real-time detection, and the cell flow detection method comprises the following steps:
pre-focusing the detected cells through the inertial focusing unit to obtain focused cells;
extracting shape information, deformation information and position information of the focused cell through the high-speed microscopic imaging system, calculating a first physical characteristic and a first mechanical characteristic of the focused cell, and calibrating the focused cell through the first physical characteristic and the first mechanical characteristic in a preliminary experiment;
acquiring the number and volume of the focused cells after calibration and electric fingerprint parameters under multiple frequencies through the broadband electrical impedance hardware system to obtain second physical characteristics of the focused cells; the high-flux controllable self-deformation of the focusing cells is completed by adopting the shearing effect of the fluid, and then the second mechanical property of the focusing cells is represented by the quantification of the difference of the electric signals;
the CPU collects and analyzes any one or more of the first physical characteristic, the second physical characteristic, the first mechanical characteristic and the second mechanical characteristic by selecting different working modes to realize the identification of the focused cells;
wherein the electrical fingerprint parameters comprise electrical characteristics of the focused cells under multi-field coupling, including amplitude, phase and opacity; the electrical signal difference includes a peak difference and a peak width difference.
The beneficial effect of this disclosure lies in:
(1) By means of the advantages of small volume and high integration of a biological micro-electro-mechanical system, the detection precision is guaranteed by integrating technologies such as face-to-face detection electrodes and inertial focusing, various physical characteristics are detected and integrated on a very small micro-fluidic chip, and high-precision identification of rare cells is realized by means of a model trained by tools such as machine learning.
(2) The method uses electrical drawing (deformation) to research the shear-induced deformation mechanism and the influence mechanism of the mechanical characteristics of the cell by measuring the difference of electrical signals before and after the cell is deformed to represent the multiple physical characteristics of the cell, thereby effectively reducing external detection equipment, simplifying the signal processing mode and improving the detection efficiency of the cell. The detection parameters are physical attributes of cells, the method belongs to a non-labeling technology, and the problems of complex operation procedures, high specificity, cell damage, high detection cost and the like of the traditional immune biochemical detection method are effectively solved.
(3) The method has the advantages that the high-flux controllable self-deformation of the cells is realized by means of the shearing effect of the fluid, the deformation is more obvious, the detection flux and the cell deformation are higher than those of deformation modes such as dielectrophoresis induced deformation, atomic force microscopic deformation and microtubule sucking, and the problems of low efficiency and cell damage existing in the traditional contact type deformation technology are effectively solved.
Drawings
FIG. 1 is a flow chart of a method described herein;
FIG. 2 is a schematic diagram of cell characteristics obtained by a broadband electrical impedance hardware system;
FIG. 3 is a schematic diagram of the electrical detection signal of the present invention;
FIG. 4 is a flow chart of data analysis according to the present invention.
Detailed Description
The technical scheme of the disclosure will be described in detail with reference to the accompanying drawings.
The multi-physical characteristic fusion perception cell flow detection method detects through a detection system, the detection system comprises a micro-fluidic chip, a high-speed microscopic imaging system and a CPU, the micro-fluidic chip comprises an inertial focusing unit and a broadband electrical impedance hardware system, the broadband electrical impedance hardware system is connected with the inertial focusing unit and the CPU, the high-speed microscopic imaging system is connected with the CPU, and the CPU is used for controlling the broadband electrical impedance hardware system and the high-speed microscopic imaging system to complete data exchange and analysis.
The high-speed microscopic imaging system is positioned in the vertical direction of the broadband electrical impedance hardware system, and the high-speed microscopic imaging system and the broadband electrical impedance hardware system work synchronously during real-time detection.
Fig. 1 is a flow chart of the method of the present application, and as shown in fig. 1, the cell flow detection method includes:
step S1: and pre-focusing the detected cells through the inertial focusing unit to obtain focused cells.
Specifically, the sample solution flows into the sinusoidal focusing flow channel of the inertial focusing unit from the inlet, and the cells reach a stable equilibrium position under the combined action of the inertial force and the secondary flow and are arranged in a single row. The inertial focusing unit focuses and arranges the cells into cell columns with uniform intervals by means of an inertial focusing effect, and the cells pass through the detection area one by one, so that a false detection result caused by the fact that a plurality of cells pass through the detection area at the same time is avoided.
Preferably, the inertial prefocusing unit comprises a flow channel structure of a sinusoidal focusing flow channel carved by two PVC plastic substrates and a silica gel material substrate through a laser, and the flow channel structure is packaged through a plasma bonding technology.
Step S2: shape information (such as cross-sectional area, perimeter), deformation information (such as deformability, relaxation time) and position information of the focused cell are extracted by the high-speed microscopic imaging system, and first physical characteristics and first mechanical characteristics of the focused cell, by which the focused cell is calibrated in a pre-experiment, are calculated.
Specifically, the high-speed microscopic imaging system is used for capturing and quantifying the actual deformation amount of the cell, the high-speed camera shoots the deformed image of the cell and transmits the image to the CPU for processing and analysis, the optimal electric signal parameter for deformation evaluation is optimized and selected, and the quantitative mapping relation between the electric signal and the mechanical characteristic of the cell is established. And (3) combining an imaging experiment and a simulation means to research the influence mechanism of the cell characteristics on the mechanical characteristics. And finally, finishing the structure optimization design and the performance calibration of the mechanical characteristic detection unit.
And step S3: acquiring the number and volume of the focused cells after calibration and electric fingerprint parameters under multiple frequencies through the broadband electrical impedance hardware system to obtain second physical characteristics of the focused cells; and the shearing effect of the fluid is adopted to complete the high-flux controllable self-deformation of the focused cells, and then the second mechanical property of the focused cells is characterized by the quantification of the difference of the electric signals.
Wherein the electrical fingerprint parameters comprise the electrical characteristics of the focused cells under multi-field coupling, including amplitude, phase and opacity; the electrical signal differences include peak differences and peak width differences.
Specifically, the broadband electrical impedance hardware system is used to achieve continuous flow detection of cell impedance signals (amplitude and phase) at different frequencies. Under the excitation of electric signals of different frequency bands, the measurement of multiple physical characteristic parameters of cells is realized through multiple pairs of detection electrodes. And carrying out noise reduction, benchmarking, signal characteristic identification, extraction and other processing on the multi-frequency response signal to obtain an effective broadband electrical impedance spectrum.
Preferably, the broadband electrical impedance hardware system comprises an upper electrode layer, a middle flow channel layer and a lower electrode layer which are arranged from top to bottom, and the upper electrode layer, the middle flow channel layer and the lower electrode layer are bonded through a thermal plastic sealing method.
The middle flow passage layer is a flow passage structure cut by the upper transparent layer of the thermoplastic polymer film by means of ultraviolet laser, and the outlet of the flow passage structure is connected with a cell collecting pipe. Under the limited Reynolds number, when a cell passes through a micro-channel with a diameter slightly larger than the diameter of the cell, the parabolic flow velocity distribution induces the shear force and the fluid positive pressure to act on the cell, so that the cell is deformed. The flow channel structure is designed based on the principle, and the deformation mechanism of the cells is researched by adopting a lattice Boltzmann coupling finite element method.
The flow channel structure of the middle flow channel layer needs to meet the requirement of deformability detection, the flow channel structure main body forms a narrow channel, the section of the channel is square, the side length of the channel is determined by the diameter of a measured cell, and the cell diameter is ensured to be 50% -90% of the side length of the square (namely the side length of the square is 111% -200% of the cell diameter); the cell is deformed by the fluid shear force and pressure in the narrow channel. The narrow channel can realize accurate positioning of cells in the center of the flow channel, further reduce the influence of cell position fluctuation on electrical impedance detection, and simultaneously realize joint detection of various biophysical characteristics of the cells, further realize accurate identification of tumor cells.
The upper electrode layer and the lower electrode layer each include a flexible polymer film with an indium tin oxide conductive layer, and microelectrode patterns are formed by laser lift-off of the indium tin oxide layer in non-electrode areas, and then the microelectrode patterns of the upper electrode layer and the lower electrode layer are aligned. Preferably, a plurality of pairs of differential electrodes before and after cell deformation are adopted to respectively detect the electrical fingerprint parameters before and after cell deformation, so that the influence of environmental noise and flow fluctuation on detection is reduced.
And step S4: and the CPU collects and analyzes any one or more of the first physical characteristic, the second physical characteristic, the first mechanical characteristic and the second mechanical characteristic by selecting different working modes, so as to realize the identification of the focused cells.
Specifically, the CPU is used for controlling a broadband electrical impedance hardware system and a high-speed microscopic imaging system to realize data exchange and analysis. The CPU writes a control program of the upper computer based on Labview, uses the data acquisition equipment to synchronously sample in two detection modes, comprises a manual triggering mode and an automatic triggering mode, controls two systems to represent physical and mechanical characteristics, and realizes the identification of focused cells.
The manual trigger includes: and simultaneously or with time delay, triggering the broadband electrical impedance hardware system and the high-speed microscopic imaging system to acquire a first physical characteristic, a first mechanical characteristic, a second physical characteristic and a second mechanical characteristic of the focused cells for cell identification. The triggering mode has comprehensive parameters and high identification precision.
The automatic triggering includes: firstly, the broadband electrical impedance hardware system is used for cell detection, and when focused cells pass through the broadband electrical impedance hardware system and the mutation of electrical impedance signals exceeds a set threshold value, the high-speed microscopic imaging system is triggered to shoot. The triggering mode is suitable for cell detection with low concentration, and transmission, storage and analysis processing of redundant images are reduced.
The cell image processing algorithm is realized by C + +, a CPU firstly obtains a single frame image from a camera, the image is allocated with a unique handle and is transmitted to a system responsible for image preprocessing to carry out background subtraction and thresholding so as to create a binary image. Detecting whether cells exist in the image, and if so, obtaining the outline of the cells by using a boundary tracking algorithm; the algorithm derives the cross-sectional area, perimeter and location of the cell from its contour and calculates the physical and mechanical properties of the cell including, but not limited to, roundness, stiffness, viscoelasticity, elastic modulus, instantaneous modulus, volume membrane volume, relaxation time, etc.
The working principle of the flow channel structure of the intermediate flow channel layer comprises the following steps: when no cell passes through the detection flow channel, the amplitude-frequency response of the flow channel is detected, the system impedance is analyzed and calculated, the sensitivity of the detection flow channel is checked by combining an experimental result, and the self-checking function is realized. When the cell passes through, the response signal under the excitation of the electric signals of different frequency bands reflects various different electrophysiological properties of the cell. The response ion current signal generated on the response electrode is converted into a voltage signal after being amplified by the current amplification device and then transmitted into the CPU. And the CPU stores the voltage signal into a txt text file, reads the txt text file by using an MATLAB program, and performs fast Fourier transform, noise reduction treatment and inverse Fourier transform on the acquired digital signal in each period to obtain a response signal diagram of the cell. The acquired electrical signals under multiple frequencies are converted into effective broadband electrical impedance spectrums by compiling an MATLAB program to perform noise reduction, benchmarking, signal feature identification, extraction and other processing.
In a specific embodiment, the material for preparing each flow channel is a polymer film, and may also be made of materials that do not interfere with the electrical impedance signal, such as Polydimethylsiloxane (PDMS), glass, epoxy resin, polymethyl methacrylate (PMMA), and Polycarbonate (PC). The device of PDMS can be prepared by soft lithography process, and the specific preparation process comprises the steps of soft lithography of the planar electrode, lithography of SU-8 male mold, PDMS casting, and combination and encapsulation of PDMS-glass bond by using vacuum oxygen plasma bond and technology. The electrodes can be replaced by planar electrodes arranged on the surface of the glass substrate. In addition, the preparation of the male die can also be realized by the aid of the technologies of silicon wet method/deep reactive ion etching, ultra-precision machining, metal electroplating, photosensitive circuit board etching and the like.
FIG. 2 is a schematic diagram of cell characteristics obtained by a broadband electrical impedance hardware system, in which electrical signals cannot penetrate cell membranes in a low frequency band, and measured response impedance signals represent volume and shape information of cells; when the medium-high frequency signal acts, the electric signal can partially penetrate through a cell membrane, and the measured electric signal represents the internal structural information of the cell (such as cell membrane capacitance, cytoplasm and nucleus resistance). Therefore, the information of the nuclear-to-cytoplasmic ratio can be further reflected by defining the ratio of the electric signal amplitude at the high frequency and the low frequency, and a new parameter Opacity (Opacity) can be obtained, and the calculation formula is as follows:
Opacity＝|Z High|/|Z Low|；
wherein Z High represents the amplitude of the electrical signal at High frequency and Z Low represents the amplitude of the electrical signal at Low frequency.
The characterization of the cell morphology and structure characteristics is realized by an electrical measurement method of a broadband electrical impedance hardware system, and the identification of the cell is realized by utilizing the multivariate electrical fingerprint parameters (amplitude and phase information of an electrical signal) and the mechanical characteristics of the electrical signal quantization under different frequencies of the cell. The difference of the electric signals quantifies the deformation of the cells, response time characteristics of the cells subjected to external force are analyzed, and the characterized mechanical characteristics comprise but are not limited to hardness and relaxation time.
As a preferred mode, the acquired electric signals under multiple frequencies are subjected to noise reduction, benchmarking, signal feature identification, extraction and other processing by writing an MATLAB program, and are converted into effective broadband electric impedance spectrums. The mechanical characteristics of different cells are quantified and characterized by adopting the difference of electric signals before and after cell deformation, 6 groups of peak values matched with a plurality of times of detection are obtained on the obtained response signal diagram as shown in figure 3, and the electrical impedance characteristics of the detected cells are characterized by the peak values of the electric signals. The first peak value R1 and the second peak value R2 are the primary particle sizes of the detected cells, and the average value of the primary particle sizes is used for verifying the vertical position of the focused cells in the flow channel section; the third peak value R3 and the fourth peak value R4 are the sizes of the detected cells after stretching or shearing deformation, and the ratio of the values of R3 and R4 to the values of R1 and R2 represents the deformation degree of the detected cells; the fifth peak value R5 and the sixth peak value R6 are used for comparing with R3 and R4, when R5 and R6 are restored to be the same as R1 and R2, the cell is considered to be restored to a normal shape at the moment, so that the length of the relaxation time of the cell can be obtained, and the restoration capability of the cell can be represented.
In a specific embodiment, since the cancer cell is softer than the normal epithelial cell, the deformation degree of the cancer cell is larger than that of the normal epithelial cell, and the ratio of the third peak value to the second peak value when the cancer cell passes through the cancer cell is smaller than that of the normal cell; cancer cells and common epithelial cells are different in cytoskeleton structure, and the time for recovering to a normal form after deformation caused by compression is different from that of the common epithelial cells.
And further establishing an equivalent model between the cell intrinsic electric fingerprint parameters and the impedance signals under different frequencies by means of a Maxwell mixing theory, an equivalent circuit model and a multi-shell model. The electric signal calibrated by the experimental image is used for detecting and identifying the cells, so that external detection equipment is effectively reduced by electrically measuring various irrelevant physical characteristics of the cells, the signal processing mode is simplified, and the cell detection efficiency is improved.
In this embodiment, the electrical impedance signals of the multidimensional parameters are reduced in dimension by Principal Component Analysis (PCA). PCA can reduce the dimensionality of a data set, retain the features which contribute most to the equation in the data, improve the feature density and facilitate the classification of various cells. The method comprises the steps of solving the covariance of a data matrix, calculating a characteristic value and a vector, combining the solved values to form a mapping matrix, taking the front n columns or the rear n columns as the final mapping matrix (the n-dimensional data with the largest contribution), and multiplying the final mapping matrix and the original data set to obtain a dimension reduction data set so as to achieve the purpose of reducing the dimension of the data.
As shown in FIG. 4, the detection gate partition method of the multidimensional cell scatter diagram adopts the partition of the equal probability curve based on the optimization of the simulated annealing algorithm, and the equal probability curve is formed by fitting the intersection points of confidence ellipses of two types of cell data. Assuming that the x-axis and y-axis data for the samples both satisfy a normal distribution, an ellipse is drawn at each confidence level to fit the intersection between the two samples into a curve on which the probability deviations for the two different cell groups are the same. And optimizing the curve by using a simulated annealing algorithm, wherein the simulated annealing algorithm is a heuristic random search algorithm based on Monte Carlo iterative solution and has probabilistic global optimization performance. Random factors are introduced in the searching process of the simulated annealing algorithm, the probability leap characteristic which is time-varying and finally tends to zero is achieved, a solution which is possibly worse than the current division is received and adjusted according to a certain probability after the distribution rule is adjusted, and the situation that the solution is trapped into local minimum and finally tends to global optimum is effectively avoided.
Claims (7)
1. The multi-physical characteristic fusion sensing cell flow detection method is characterized in that the cell flow detection method is used for detecting through a detection system, the detection system comprises a micro-fluidic chip, a high-speed microscopic imaging system and a CPU, the micro-fluidic chip comprises an inertial focusing unit and a broadband electrical impedance hardware system, the broadband electrical impedance hardware system is connected with the inertial focusing unit and the CPU, the high-speed microscopic imaging system is connected with the CPU, and the CPU is used for controlling the broadband electrical impedance hardware system and the high-speed microscopic imaging system to complete data exchange and analysis; the high-speed microscopic imaging system is positioned in the vertical direction of the broadband electrical impedance hardware system, and the high-speed microscopic imaging system and the broadband electrical impedance hardware system work synchronously during real-time detection, and the cell flow detection method comprises the following steps:
pre-focusing the detected cells through the inertial focusing unit to obtain focused cells;
extracting shape information, deformation information and position information of the focused cell through the high-speed microscopic imaging system, calculating a first physical characteristic and a first mechanical characteristic of the focused cell, and calibrating the focused cell through the first physical characteristic and the first mechanical characteristic in a pre-experiment; the high-speed microscopic imaging system is used for capturing and quantifying the actual deformation quantity of the cell, the high-speed microscopic imaging system shoots the deformed image of the cell and transmits the image to the CPU for processing and analysis, the optimal electric signal parameter for deformation evaluation is optimized and selected, and the quantitative mapping relation between the electric signal and the mechanical characteristic of the cell is established;
acquiring the number and volume of the focused cells after calibration and electric fingerprint parameters under multiple frequencies through the broadband electrical impedance hardware system to obtain second physical characteristics of the focused cells; the high-flux controllable self-deformation of the focusing cells is completed by adopting the shearing effect of the fluid, and then the second mechanical property of the focusing cells is represented by the quantification of the difference of the electric signals;
the CPU collects and analyzes any one or more of the first physical characteristic, the second physical characteristic, the first mechanical characteristic and the second mechanical characteristic by selecting different working modes to realize the identification of the focused cells;
wherein the electrical fingerprint parameters comprise the electrical characteristics of the focused cells under multi-field coupling, including amplitude, phase and opacity; the electrical signal difference includes a peak difference and a peak width difference.
2. The method as claimed in claim 1, wherein the inertial focusing unit comprises a flow channel structure formed by two PVC plastic substrates and a silica gel material substrate through laser etching of a sinusoidal focusing flow channel, and the flow channel structure is encapsulated by a plasma bonding technology.
3. The method according to claim 2, wherein the broadband electrical impedance hardware system comprises an upper electrode layer, a middle runner layer and a lower electrode layer which are arranged from top to bottom, and the upper electrode layer, the middle runner layer and the lower electrode layer are bonded through a thermal plastic sealing method;
the middle flow channel layer is a flow channel structure cut by the upper transparent layer of the thermoplastic polymer film by means of ultraviolet laser, and the outlet of the flow channel structure is connected with a cell collecting pipe; the flow channel structure forms a channel, the section of the channel is square, and the side length of the square is 111% -200% of the cell diameter.
4. The method of claim 3, wherein the upper electrode layer and the lower electrode layer each comprise a flexible polymer film with an indium tin oxide conductive layer, the pattern of microelectrodes is formed by laser lift-off of the indium tin oxide layer in non-electrode areas, and then the pattern of microelectrodes of the upper electrode layer and the lower electrode layer are aligned.
5. The method of claim 4, wherein characterizing the second mechanical property of the cell by quantification of the difference in the electrical signals comprises: acquiring 6 detected sets of electrical signal peaks on the response signal plot, the electrical impedance characteristics of the focused cell being characterized by the electrical signal peaks, including:
the first peak value R1 and the second peak value R2 are used for representing the primary particle size of the detected focused cell, and the average value of R1 and R2 is used for verifying the vertical position of the focused cell in the channel section;
the third peak value R3 and the fourth peak value R4 are used for detecting the size of the focused cell after stretching or shearing deformation, and the deformation degree of the focused cell is characterized by the ratio of R3 and R4 to R1 and R2;
comparing the fifth peak value R5 with the sixth peak value R6 with the values of R1 and R2, and obtaining the time when the values of R5 and R6 are the same as the values of R1 and R2, so as to obtain the relaxation time of the focused cells, wherein the relaxation time is used for representing the recovery capability of the focused cells.
6. The method of claim 5, wherein the opacity comprises: the Opacity, is represented by the ratio of the electrical signal amplitudes at high and low frequencies, which is:
Opacity＝|Z High|/|Z Low|；
wherein Z High represents the amplitude of the electrical signal at High frequency and Z Low represents the amplitude of the electrical signal at Low frequency.
7. The method of claim 1, wherein the operating mode of the CPU includes a manual trigger and an automatic trigger;
the manual trigger includes: triggering the broadband electrical impedance hardware system and the high-speed microscopic imaging system at the same time or with time delay to acquire a first physical characteristic, a first mechanical characteristic, a second physical characteristic and a second mechanical characteristic of a focused cell for cell identification;
the automatic triggering comprises: firstly, the broadband electrical impedance hardware system is used for cell detection, and when a focused cell passes through the broadband electrical impedance hardware system and the sudden change of an electrical impedance signal exceeds a set threshold value, the high-speed microscopic imaging system is triggered to shoot.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103439241A (en) * | 2013-08-23 | 2013-12-11 | 东南大学 | Micro-fluidic chip detection system based on single-cell multi-parameter representation |
| CN104251810A (en) * | 2013-12-18 | 2014-12-31 | 中国科学院电子学研究所 | System for simultaneous representation of single cell Young's modulus and cell membrane specific capacitance |
| CN111735853A (en) * | 2020-06-16 | 2020-10-02 | 东南大学 | Integrated pre-sorting cell mechanical and electrical multi-parameter joint detection device |
| CN111744565A (en) * | 2020-05-26 | 2020-10-09 | 东南大学 | Microfluidic device and system for multi-channel parallel detection of cell deformability |
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| CN103439241A (en) * | 2013-08-23 | 2013-12-11 | 东南大学 | Micro-fluidic chip detection system based on single-cell multi-parameter representation |
| CN104251810A (en) * | 2013-12-18 | 2014-12-31 | 中国科学院电子学研究所 | System for simultaneous representation of single cell Young's modulus and cell membrane specific capacitance |
| CN111744565A (en) * | 2020-05-26 | 2020-10-09 | 东南大学 | Microfluidic device and system for multi-channel parallel detection of cell deformability |
| CN111735853A (en) * | 2020-06-16 | 2020-10-02 | 东南大学 | Integrated pre-sorting cell mechanical and electrical multi-parameter joint detection device |
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