CN113567326A - High-throughput real-time single-cell electrical intrinsic parameter measurement system and method - Google Patents
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Abstract
The invention discloses a high-throughput real-time single-cell electricity intrinsic parameter measuring system and a method. The microfluidic system is responsible for driving a large number of single-cell bunching queues to pass through the measurement area at a high speed; the multi-frequency impedance measurement system is responsible for measuring and collecting impedance values of single cells at a plurality of different frequency points; the real-time processing algorithm system is responsible for filtering the impedance value, detecting and extracting pulses and acquiring intrinsic parameters of the single cell to be detected, such as cell radius, cytoplasm conductivity, cell membrane unit membrane capacitance and the like in real time. The measuring system can realize high-flux real-time single-cell electrical intrinsic parameters and has wide application prospect in life science instruments.
Description
Technical Field
The invention belongs to the field of single-cell electrical parameter measurement, and particularly relates to a high-throughput real-time single-cell electrical intrinsic parameter measurement system and method.
Background
The single cell representation provides the basic structure, functional information and pathological state of the cell, plays an important role in revealing cell heterogeneity and has great significance for life science research, disease diagnosis and personalized medicine. Fluorescent labels have been the primary tool for single cell analysis and imaging, and identify components and their states in cells by using detection labels (e.g., fluorescent dyes, quantum dots, magnetic beads, stable isotopes, etc.) that are molecularly specific for cell markers. Fluorescence labeling of cells not only requires prior knowledge of cell specificity, but also invasive manipulation of the labeling process can change the state of the cells, complicating the analysis process and limiting subsequent analysis. In contrast, the biophysical properties (such as electrical and mechanical properties) of cells are also related to the molecular composition in cells, and have been proved to be effective biomarkers for diagnosing diseases (cancer, malaria, diabetes, sickle cell anemia and the like), and the biophysical property characterization does not need to label cells, the cell state is basically unchanged in the characterization process, and the cells can still be subjected to further operations and analyses such as sorting, culturing, omics analysis and the like after characterization, so the method is widely concerned.
As one of the biophysical characteristics, the cell electrical characteristics reflect the characteristics of membrane morphology, ion channel state, nuclear size, and cytoplasm. The basic principle of electrical characterization is to apply alternating current excitation to cells using electrical rotation, electrical impedance spectroscopy, and impedance flow, to characterize the electrical properties of the cells with cytokinematic or electrical measurement signals. In the electric rotation technology, single cells are positioned in a plurality of electrodes of a microfluidic device and rotated under the action of dielectric, and the electrical intrinsic parameters (such as cell size, cytoplasm conductivity and unit membrane capacitance) of the single cells are calculated by fitting a rotating speed-electric signal frequency curve of the cells. The technology has complicated operation process for positioning the cells, longer measurement time, flux of only 1 cell/second, and is not suitable for real cell buffer solution with high conductivity. In the electrical impedance spectroscopy technology, a single cell is captured in an electrode, a sweep frequency signal is applied to the electrode, output current is measured, and after an impedance spectrum of the single cell is obtained, electrical intrinsic parameters of the cell are calculated from a cell electrical model. This technique enables fast scanning of the captured cells due to the fast response of the electrical signals, but the measurement results are susceptible to the capture structure and the throughput cannot be increased due to the capture operation. In contrast, in the impedance flow technology, a single cell continuously passes through an electrode detection area at a high speed, electrical characteristics of the single cell are directly represented by absolute or relative impedance values of a few frequency points of the cell, the flux is up to 1000 cells/second, but the existing intrinsic parameter calculation method based on model fitting is long in time consumption, and a measurement system can only give phenomenological parameters (such as impedance amplitude, phase and impedance transparency) to represent the single cell when being used online, so that the measurement result is highly dependent on a measurement platform, and comparison between different platforms cannot be carried out.
The prior art has at least the following disadvantages: 1) due to the limitations of single cell operation, such as complicated positioning and capturing operation processes of cells and long measurement time, the measurement system is complex and the single cell measurement flux is low; 2) in the impedance flow analysis technology, single cells continuously pass through an electrode detection area at a high speed, single-frequency point measurement causes insufficient information, and the single cells can be represented only by phenomenological parameters (such as impedance amplitude, phase and impedance transparency), so that the measurement result is highly dependent on a measurement platform and cannot be compared among different platforms; 3) under the background of high throughput, the traditional algorithm based on gradient descent fitting or decision tree and the like has large calculation amount and long required time, and cannot meet the requirement of real-time processing, so that the prior art cannot realize high-throughput real-time online measurement of the electrical intrinsic parameters of single cells.
Therefore, a high-throughput real-time single-cell electrical intrinsic parameter measurement system and method are needed to be found, the capability of effectively analyzing original impedance data by using a single-cell impedance flow technology under a high-throughput scene can be improved, and the high-throughput real-time online measurement of the electrical intrinsic parameters of the single cells is realized.
Object of the Invention
The invention aims to solve the defects in the prior art and provide a high-throughput real-time single-cell electrical intrinsic parameter measurement system and method, wherein the system and method improve the flux of a single-cell electrical characterization system and reduce the complexity of the measurement system by utilizing a microfluidic technology, an impedance flow analysis technology and a locking amplification technology; secondly, obtaining the multi-frequency impedance of the single cell and solving the intrinsic electrical parameter by using a multi-frequency locking amplification principle; thirdly, the neural network technology is utilized to rapidly analyze complex input data and predict and output with high precision, so that the high-throughput real-time on-line measurement of the electrical intrinsic parameters of the single cells is realized.
Disclosure of Invention
According to one aspect of the invention, a high-throughput real-time single-cell electrical intrinsic parameter measurement system is provided, which comprises three subsystems in total: the system comprises a micro-fluidic system, a multi-frequency impedance measurement system and a real-time processing algorithm system;
the microfluidic system consists of a microfluidic device and a sample injection driving device and is responsible for driving a large number of single-cell bunching arrays to pass through a measurement area at a high speed;
the multi-frequency impedance measurement system consists of a DDS signal generation module, a lock-in amplifier module and a signal acquisition module and is responsible for measuring and acquiring impedance values of single cells at a plurality of different frequency points;
the real-time processing algorithm system consists of a digital filtering algorithm, an event detection and pulse extraction algorithm and a neural network regression algorithm and is responsible for filtering an impedance value, detecting and extracting pulses and acquiring intrinsic parameters of cell radius, cytoplasm conductivity and cell membrane unit membrane capacitance of a single cell to be detected in real time.
Preferably, the microfluidic device in the microfluidic system is designed into a microfluidic device chip in a chip manner, the microfluidic device chip is designed into two layers, wherein the bottom layer uses glass as a substrate and is provided with a pair of planar electrodes for impedance detection, the top layer is provided with a polydimethylsiloxane PDMS microchannel, so that single cell bunching queues sequentially pass through a detection area, and the size of a flow channel in the detection area is determined by the size of cells.
More preferably, for mammalian cells having a diameter of 8-18 μm, the width and height of the measurement region flow channel are both 20 μm; a columnar array with the interval of 20 mu m is arranged at the entrance of the measuring area to be used as an on-chip filter, and the filter is designed to be funnel-shaped according to the Bernoulli principle; the pair of planar electrodes are configured to be 30 μm long, 20 μm wide and 20 μm spaced.
Preferably, the micro-channel layer of the micro-fluidic device chip is manufactured by a soft lithography technology and is formed by utilizing PDMS (polydimethylsiloxane) reverse mold; the planar electrode layer of the top layer of the microfluidic device chip is patterned on a glass substrate by lift-off technology, treated with oxygen plasma and baked, then the planar electrode layer and the microchannel layer are aligned and firmly bonded together, and then the planar electrode is soldered with a customized printed circuit board with a matched pattern.
Preferably, in the microfluidic system, the cell suspension inlet and outlet are connected with the fluid driving device through plastic hoses, and the cell suspension is pushed and injected into the micro-channel on the top layer of the microfluidic device chip through the fluid driving device.
Preferably, the DDS signal generating module is a four-frequency signal generator built based on a DDS chip AD 9958; the locking amplifier module comprises a transimpedance amplifier TIA built on the basis of a current operational amplifier chip OPA657, a demodulator built on the basis of a broadband four-quadrant voltage output multiplier AD835 chip, a low-pass filter with 5KHz cut-off frequency and an amplifier built by an operational amplifier OPA 227; the signal acquisition module is a data acquisition card connected with a computer; the direct current signal finally output by the multi-frequency impedance measuring system is sampled and obtained by the computer through the data acquisition card at the sampling rate of 20KHz, and is subsequently processed on the computer by a time processing algorithm system;
preferably, the real-time processing algorithm system is based on Python, Matlab and LabView mixed programming, and the processing process is as follows: firstly, carrying out digital filtering on a data stream through a digital low-pass filter so as to further improve the signal-to-noise ratio (SNR); detecting single cell events in the Matlab script and extracting corresponding single cell impedance peak values; the extracted impedance peak value is sent to a Python node through a TCP/IP protocol so as to calculate the intrinsic parameters of the single cell through a trained neural network, and the calculation result is displayed in a graphical interface, and the whole processing process is carried out in a real-time online mode.
According to another aspect of the present invention, there is provided a method for measuring the electrical intrinsic parameters of a single cell in real time by using the above high-throughput real-time single cell electrical intrinsic parameter measuring system, comprising the following steps:
step S1: modifying a flow channel, specifically, pretreating the micro flow channel of the micro flow device chip for 15 minutes by using 1 wt% of Pluronic F-127 surfactant, namely 1x PBS;
step S2: cell feeding, in particular using a syringe pump to pass a single cell sample into a microchannel at a constant flow rate of 1 μ L/min, wherein the equivalent velocity at the measurement zone is 20mm/s, thereby achieving a high throughput of over 1000 cells/min;
step S3: signal measurement, specifically, when a single cell passes through a measurement region in a flow channel in a queue manner, the generated electric signal is collected by an impedance measurement system and is solved by a real-time processing algorithm system to obtain the intrinsic parameters of the single cell;
step S4: and displaying the intrinsic parameters of the single cell obtained in the step S3 on a user interface in real time.
Preferably, the process of acquiring the electrical signal by the impedance measuring system and solving the intrinsic parameter of the single cell by the real-time processing algorithm system in step S3 includes:
substep S31: the electrical properties of the cell-solution mixed system are expressed by the properties of single cells and suspension medium, the complex impedance of which is given by equation (1):
where κ is the cell constant, depending on the geometric parameters, l is the electrode length,
is the complex dielectric constant of a cell-solution mixed system, wherein sigma is the conductivity, epsilon is the dielectric constant, and omega is the angular frequency, and is calculated as shown in formula (2):
wherein
And
is a complex of medium and cellThe dielectric constant of the glass is constant,
is the volume fraction of the total amount of the polymer,
is the Clausius-Mossotti coefficient, given by equation (3):
substep S32: constructing an equivalent circuit model ECM when a single cell is located between a pair of planar measurement electrodes of the microfluidic device chip, wherein the impedance is represented by a solution resistance RmedSolution capacitor CmedCytoplasmic resistance RiCell membrane capacitance CmemAnd an electric double layer capacitor CDLAnd stray capacitance CsComposition, the impedance of a cell-solution mixed system is characterized as shown in formula (4):
considering CsAnd CDLThe model total impedance is expressed as shown in equation (5):
substep S33: model fitting and parameter extraction are performed at four frequencies of 45KHz, 250KHz, 750KHz and 1200KHz, respectively, where 45KHz and 250KHz are used to determine CDLAnd cell size, 750KHz for determining cell membrane permittivity, 1200KHz for determining cytoplasmic conductivity and Cs(ii) a Measuring impedance values by minimizing using least squares and gradient descent
And the model estimate, and the model total impedance
The variance between them determines the intrinsic parameters of a single cell as shown in equation (6):
step S34: based on the mixing principle, the cosine signals of the four frequency points in step S33 are modulated by an adder, which is expressed as shown in equation (7):
S(t)＝cosω1t+cosω2t+cosω3t+cosω4t (7)，
and then demodulating the response signal R (t) into real voltage and virtual voltage through four demodulators respectively, wherein the equation (8) is as follows:
using a known reference feedback resistor R used in a transimpedance amplifier TIAFAnd measuring the calibration coefficients of the system, thereby calculating the complex impedance.
Preferably, the processing procedure of the real-time processing algorithm system comprises the following sequence:
sequence 1: a deep full-connection neural network FCN is built under an open-source frame Pythrch and used for processing impedance data streams, so that real-time solution of the intrinsic parameters of the single cells is realized; the designed FCN consists of five layers, wherein the first layer is an input layer and is responsible for receiving input data; the middle three layers are hidden layers and are responsible for mapping input data to output data; the last layer is an output layer and is responsible for exporting output results; each layer of the FCN is composed of a plurality of neurons, the neurons obtain weighted sums of previous layers, and the weighted sums are converted into outputs by utilizing a linear rectification ReLU activation function;
sequence 2: setting the input of the FCN as the single-cell impedance amplitude of four frequency points, setting the output as an intrinsic parameter, selecting a mean square error function as a loss function to calculate the deviation between a tag value and an output value, and performing supervision training on the FCN by using a random gradient descent SGD optimizer to back-propagate errors; obtaining intrinsic parameter estimation values through fitting based on a gradient descent algorithm model, and using the intrinsic parameter estimation values as target labels of training data; the complete data set is divided into two data sets, namely a training set accounting for 70% and a testing set accounting for 30%, which are respectively used for training and testing.
Drawings
FIG. 1 is a schematic block diagram of a high-throughput real-time single-cell real-time electrical intrinsic parameter real-time measurement system according to the present invention.
Fig. 2 is a schematic structural diagram of a microfluidic system device in a measurement system: (a) a schematic structural diagram of the microfluidic device is measured; (b) the microfluidic device was measured for size map.
Fig. 3 is a schematic and exemplary illustration of microfluidic system device processing in a measurement system: (a) a microfluidic device processing schematic; (b) an example of a device is shown.
FIG. 4 is a single cell equivalent circuit model and electrical impedance spectroscopy: (a) a single cell equivalent circuit model; (b) single cell electrical impedance spectroscopy
FIG. 5 is a schematic of the electrical signal generated by a single cell through a measurement electrode and a lock-in amplifier schematic: (a) a schematic diagram of the electrical signals generated by the single cells passing through the measurement electrodes; (b) lock-in amplifier schematic.
FIG. 6 is a schematic diagram of a hardware system and a software system process flow diagram for multi-frequency impedance measurement in a measurement system: (a) a multi-frequency impedance measurement hardware system schematic; (b) software system processing flow diagram.
Fig. 7 is a schematic diagram of a neural network structure applied in the embodiment of the present invention.
FIG. 8 is a block diagram of the flow chart of the real-time measurement system for the electrical intrinsic parameters of single cells in high throughput and real-time.
Detailed Description
The following detailed description of the present invention will be given in conjunction with the accompanying drawings, and it will be understood by those skilled in the art that the present invention is described by way of example only, and should not be taken as limiting the scope of the present invention.
FIG. 1 is a schematic block diagram of a high-throughput real-time single-cell real-time electrical intrinsic parameter real-time measurement system according to the present invention. As can be seen from the figure, the high-throughput real-time single-cell real-time electrical intrinsic parameter measuring system comprises three subsystems in total: a microfluidic system, a multi-frequency impedance measurement system and a real-time processing algorithm system. The structural design of each subsystem is described in detail below.
Microfluidic system and device structure design
As can be seen from fig. 1, the microfluidic system consists of a microfluidic device and a sample introduction device. Fig. 2 is a schematic structural view of a microfluidic system device in a measurement system, and as shown in fig. 2, the microfluidic device chip in this embodiment is designed in two layers in consideration of convenience in manufacturing and optical observation, and the schematic structural view is shown in fig. 2 (a). The bottom layer uses glass as a substrate and is provided with a pair of planar electrodes for impedance detection, the top layer is provided with a Polydimethylsiloxane (PDMS) microchannel so that single cells are bunched and arranged in a queue to sequentially pass through a detection area, and the size of a flow channel of the detection area is determined by the size of the cells. In particular, for common mammalian cells, in the typical diameter range of 8-18 μm, in order to achieve highly sensitive and accurate electrical characterization of single cells and avoid cell blockage and current leakage problems, as shown in fig. 2(b), the width and height of the flow channel of the measurement region are both 20 μm. Also in order to reduce the possibility of multiple cells passing through the measurement region at the same time, the entrance of the measurement region was configured with a 20 μm-spaced columnar array as an on-chip filter and was designed to be funnel-shaped according to the bernoulli principle to create cells with too close a pressure separation distance. In addition, the electrode dimensions optimized by analysis of the single cell electrical model were 30 μm long, 20 μm wide and 20 μm pitch. It should be noted that the structure and the size are not limited to the specific ones, and may be adjusted as appropriate according to the application.
Fig. 3 is a schematic and exemplary illustration of the processing of the microfluidic system components in the measurement system. As shown in fig. 3, one fabrication process for a microfluidic device is shown in fig. 3 (a). The micro-channel layer is manufactured by a soft lithography technology, PDMS is used for reverse mold formation, and the electrode layer is patterned on the glass substrate by a lift-off technology. After oxygen plasma treatment and baking, the electrode layer and microchannel layer are securely bonded together in alignment. Finally, to facilitate the application of the excitation signal and the acquisition of the measurement signal, the electrodes are soldered together with a custom printed circuit board that matches their pattern. FIG. 3(b) is an example of a device in which the cell suspension inlet and outlet are connected to a fluid driving means through plastic hoses, and the cell suspension is injected into microchannels through the fluid driving means.
Single cell electrical parameter measuring principle
The principle of measuring the intrinsic electrical parameters in this embodiment can be a spherical single-shell model, and practically any model can be realized by the system. In the spherical monocoque model, the cells can be represented as a uniform conducting cytoplasm surrounded by a dielectric membrane. The electrical properties of a cell-solution mixed system can be expressed by the properties of single cells and suspension medium when the suspended cells are in an alternating electric field by Maxwell's Mixing Theory (MMT), whose complex impedance is given by equation (1):
where κ is the cell constant, depending on the geometric parameters, l is the electrode length,
is the complex dielectric constant of a cell-solution mixed system, wherein sigma is the conductivity, epsilon is the dielectric constant, and omega is the angular frequency, and is calculated as shown in formula (2):
wherein
And
is the complex permittivity of the medium and the cells,
is the volume fraction of the total amount of the polymer,
is the Clausius-Mossotti coefficient, given by equation (3):
FIG. 4 shows an equivalent circuit model of a single cell and an electrical impedance spectroscopy, from which an equivalent circuit model ECM can be derived when the cell is located between a pair of measurement electrodes, as shown in FIG. 4 (a). In the simplest analytical model, the impedance is given by the solution resistance RmedSolution capacitance CmedCytoplasmic resistance RiCell membrane capacitance CmemDouble electric layer capacitance CDLAnd stray capacitance CsAnd (4) forming. Using these parameters, the impedance of the cell-solution mixed system is calculated as shown in equation (4):
considering CsAnd CDLThe model total impedance is expressed as shown in equation (5):
as shown in fig. 4(b), the calculated electrical impedance spectrum has four regions. In the first area (<0.1MHz), the impedance is increased by CDLLeading to the impedance of the cell-solution mixing system being submerged in a high impedance; with increasing frequency, CDLThe influence of (2) decreases exponentially in the second region (0.1 MHz-0.5 MHz), the dielectric film starts to be penetrated by an electric field resulting in an impedance modulation by the capacitance of the film, the dielectric constant of which is typically 10mF/m2(ii) a When the frequency is continuously increased in the third region (0.5 MHz-5 MHz), the dielectric film is completely penetrated by the electric field, and the conductivity of the cytoplasm is at impedanceThe spectrum is dominant; however, the fourth region (>5MHz) is subject to CsResulting in difficulties in exploring the properties of the nucleus. Fig. 4(b) shows how the electrical impedance of a single cell varies with intrinsic parameters and provides guidance for frequency selection for multi-frequency impedance measurements. In light of the above discussion, the method of the present invention takes four of these frequencies (i.e., 45KHz, 250KHz, 750KHz, and 1200KHz) as examples for model fitting and parameter extraction, where 45KHz and 250KHz are used to determine CDLAnd cell size, 750KHz for determining cell membrane permittivity, 1200KHz for determining cytoplasmic conductivity and Cs. Measuring impedance values by minimizing using least squares and gradient descent
And model estimation (model total impedance)
The variance between them determines the intrinsic parameters of a single cell as shown in equation (6):
multi-frequency impedance measurement system design and principle
As the cell passes through the measurement zone, it displaces an equal volume of medium and perturbs the electric field, thereby causing a change in impedance in the circuit. FIG. 5 is a schematic of the electrical signal generated by a single cell passing through a measurement electrode and a lock-in amplifier schematic. As shown in fig. 5(a), the signal has a gaussian shape with peaks corresponding to the impedance of the cell at a particular frequency. As shown in fig. 5(b), the basic principle of impedance measurement is to convert a current signal into a voltage signal through a transimpedance amplifier TIA, and detect the resulting weak response signal through a lock-in amplifier LIA, thereby realizing the measurement of impedance of a single cell. In this embodiment, the four-frequency cosine signal in the single cell measurement principle based on the frequency mixing principle is modulated by an adder, and is expressed as shown in formula (7):
S(t)＝cosω1t+cosω2t+cosω3t+cosω4t (7)，
then, the response signal r (t) is demodulated into real voltage and imaginary voltage by four demodulators, respectively, as shown in formula (8):
using a known reference feedback resistor R used in a transimpedance amplifier TIAFAnd measuring the calibration coefficient of the system to calculate the complex impedance.
Fig. 6 is a schematic diagram of a hardware system and a software system process flow diagram for multi-frequency impedance measurement in a measurement system, wherein the hardware system for multi-frequency impedance measurement is shown in fig. 6(a) and is responsible for generating an excitation signal, demodulating a response signal and obtaining an output dc signal. As an example, a four-frequency signal generator is built on a DDS chip AD9958(ADI, usa), a transimpedance amplifier TIA is built on a high-gain-bandwidth, low-bias-current operational amplifier chip OPA657(TI, usa), a demodulator is built on a wideband four-quadrant voltage output multiplier AD835 chip (ADI, usa) and a low-pass filter and amplifier with a 5KHz cutoff frequency are built by a high-precision, low-noise operational amplifier OPA227(TI, usa). The finally output direct current signal is sampled and obtained by a computer through a data acquisition card (NI, PCI-6289) at a sampling rate of 20KHz, and is subsequently processed on the computer by a software system. The software system processing flow is shown in fig. 6(b), and is responsible for processing, displaying and recording data in real time. The software system is based on Python, Matlab (Math Works, usa) and LabView (NI, usa) hybrid programming, first digitally filters the data stream through a digital low pass filter to further improve the signal-to-noise ratio (SNR), then detects single cell events in the Matlab script and extracts the corresponding single cell impedance peaks. The extracted impedance peak value is sent to a Python node through a TCP/IP protocol, so that intrinsic parameters of the single cell are calculated through a trained neural network, a calculation result is displayed in a graphical interface, and the whole working process is carried out in a real-time online mode.
Real-time algorithm design and principles
The present embodiment takes advantage of the neural network technology to quickly analyze complex input data and predict output with high accuracy. Fig. 7 is a schematic structural diagram of a neural network applied in this embodiment, and as shown in fig. 7, a deep fully-connected neural network FCN is built under an open-source framework pitorch for processing an impedance data stream, so as to implement real-time solution of intrinsic parameters of single cells. The designed FCN consists of five layers, the first layer being the input layer (responsible for receiving input data), the middle three layers being the hidden layer (responsible for mapping input data to output data), and the last layer being the output layer (responsible for deriving output results). Each layer is composed of a number of neurons that take the weighted sum of the previous layer and convert the weighted sum to an output using a linearly rectifying ReLU activation function. The ReLU activation function introduces non-linear behavior into the neural network system. In order to solve intrinsic parameters of the single cell, the input of the FCN is single cell impedance amplitude of four frequency points, the output of the FCN is the intrinsic parameters, therefore, a mean square error function is selected as a loss function to calculate deviation of a tag value and an output value, and the FCN is supervised and trained by using a random gradient descent (SGD) optimizer to back-propagate errors. Intrinsic parameter estimates obtained by conventional gradient descent algorithm-based model fitting are used as target labels for the training data. The complete data set is divided into two data sets, a training set (70%) and a testing set (30%), which are used for training and testing, respectively. In the testing process, the derivation time of the single cell event is about 0.3ms, and the intrinsic parameters can be obtained only by the gradient descent-based model fitting method in several seconds, so that the processing speed is improved by several orders of magnitude, and the real-time online solution of the intrinsic parameters of the single cell is realized.
FIG. 8 is a block diagram of the flow chart of the real-time measurement system for the electrical intrinsic parameters of single cells in high throughput and real-time. As shown in the figure, when the measuring system is used for measuring the single-cell electricity intrinsic parameters in real time, the method sequentially comprises the following steps:
step S1: flow channel modification, specifically, the microchannel with 1 wt%, 1x PBS Pluronic F-127 surfactant pretreatment 15 minutes;
step S2: cell feeding, in particular using a syringe pump to pass a single cell sample into a microchannel at a constant flow rate of 1 μ L/min, wherein the equivalent velocity at the measurement zone is 20mm/s, thereby achieving a high throughput of over 1000 cells/min;
step S3: signal measurement, specifically, when a single cell passes through a measurement region in a flow channel in a queue manner, the generated electric signal is collected by an impedance measurement system and is solved by a real-time processing algorithm to obtain the intrinsic parameters of the single cell;
step S4: and displaying the intrinsic parameters of the single cell obtained in the step S3 on a user interface in real time.
In addition to the electrical measurement process, the system can also be used to monitor and record the movement of cells during use using an inverted microscope equipped with a CCD camera and synchronized with the electrical signal by time stamps so that it can be analyzed off-line to correlate the electrical impedance signal with microscopic observations.
Compared with the prior art, the invention has the following advantages:
1) by utilizing a microfluidic technology, an impedance flow analysis technology and a locking amplification technology, the flux of a single-cell electrical characterization system is improved, the complexity of a measurement system is reduced, and a set of single-cell characterization method and system with sample input and result output is constructed;
2) by utilizing a multi-frequency locking amplification principle, the electrical intrinsic parameters are calculated off-line by measuring the impedance values of four frequency points based on a gradient descent model fitting method;
3) the neural network technology is utilized to rapidly analyze complex input data and predict and output with high precision, the resolving time is reduced from second level to sub-millisecond level, and high-throughput real-time on-line measurement of electrical intrinsic parameters of single cells is realized.
In conclusion, the impedance flow type chip structure design, the electrical intrinsic parameter extraction method, the artificial intelligence, machine learning and other data analysis methods are combined, the online real-time measurement of the electrical characteristic intrinsic parameters of the single cell is realized, a single cell characterization system for sample input-output is constructed, and a technical basis is constructed for subsequent specificity sorting and further metabonomics analysis. The invention has multiple meanings, can solve the problem of high-flux real-time accurate measurement of the intrinsic parameters of the single cells, and can provide a technical basis for further exploring a deep molecular level regulation mechanism of the biophysical characteristics of the cells through omics research, thereby promoting the deep development of the single cell analysis to the instant diagnosis and clinical application. The technical scheme including crossing of multiple fields such as a microfluidic technology, single cell measurement, artificial intelligence, machine learning and the like is expected to improve single cell analysis to a new level, and has great significance to life science research, disease diagnosis and personalized medicine.
Claims (10)
1. A high-throughput real-time single-cell electrical intrinsic parameter measurement system is characterized by comprising three subsystems: the system comprises a micro-fluidic system, a multi-frequency impedance measurement system and a real-time processing algorithm system;
the microfluidic system consists of a microfluidic device and a sample injection driving device and is responsible for driving a large number of single-cell bunching arrays to pass through a measurement area at a high speed;
the multi-frequency impedance measurement system consists of a DDS signal generation module, a lock-in amplifier module and a signal acquisition module and is responsible for measuring and acquiring impedance values of single cells at a plurality of different frequency points;
the real-time processing algorithm system consists of a digital filtering algorithm, an event detection and pulse extraction algorithm and a neural network regression algorithm and is responsible for filtering an impedance value, detecting and extracting pulses and acquiring intrinsic parameters of cell radius, cytoplasm conductivity and cell membrane unit membrane capacitance of a single cell to be detected in real time.
2. The system of claim 1, wherein the microfluidic device in the microfluidic system is designed into a microfluidic device chip in a chip manner, the microfluidic device chip is designed into two layers, wherein the bottom layer uses glass as a substrate and is provided with a pair of planar electrodes for impedance detection, the top layer is provided with a Polydimethylsiloxane (PDMS) microchannel, so that the single-cell bunched train sequentially passes through a detection area, and the size of the flow channel of the detection area is determined by the size of the cell.
3. The high-throughput real-time single-cell electrical intrinsic parameter measurement system according to claim 2, wherein the width and height of the flow channel of the measurement region are both 20 μm for mammalian cells with a diameter of 8-18 μm; a columnar array with the interval of 20 mu m is arranged at the entrance of the measuring area to be used as an on-chip filter, and the filter is designed to be funnel-shaped according to the Bernoulli principle; the pair of planar electrodes are configured to be 30 μm long, 20 μm wide and 20 μm spaced.
4. The system for measuring the high-throughput real-time single-cell electrical intrinsic parameters of claim 2, wherein the micro-channel layer of the micro-fluidic device chip is manufactured by a soft lithography technology and is formed by a PDMS reverse mold; the planar electrode layer of the top layer of the microfluidic device chip is patterned on a glass substrate by lift-off technology, treated with oxygen plasma and baked, then the planar electrode layer and the microchannel layer are aligned and firmly bonded together, and then the planar electrode is soldered with a customized printed circuit board with a matched pattern.
5. The high-throughput real-time single-cell electrical intrinsic parameter measurement system according to any one of claims 1 to 4, wherein in the microfluidic system, the cell suspension inlet and outlet are connected to the fluid driving device through plastic hoses, and the cell suspension is pushed and injected into the micro-channel on the top layer of the microfluidic device chip through the fluid driving device.
6. The high-throughput real-time single-cell electrical intrinsic parameter measurement system according to claim 1, wherein the DDS signal generation module is a four-frequency signal generator built based on a DDS chip AD 9958; the locking amplifier module comprises a transimpedance amplifier TIA built on the basis of a current operational amplifier chip OPA657, a demodulator built on the basis of a broadband four-quadrant voltage output multiplier AD835 chip, a low-pass filter with 5KHz cut-off frequency and an amplifier built by an operational amplifier OPA 227; the signal acquisition module is a data acquisition card connected with a computer; the direct current signal finally output by the multi-frequency impedance measuring system is sampled and obtained by the computer through the data acquisition card at the sampling rate of 20KHz, and is subsequently processed on the computer by the time processing algorithm system.
7. The system of claim 1, wherein the real-time processing algorithm system is based on Python, Matlab and LabView mixed programming and comprises the following processing procedures: firstly, carrying out digital filtering on a data stream through a digital low-pass filter so as to further improve the signal-to-noise ratio (SNR); detecting single cell events in the Matlab script and extracting corresponding single cell impedance peak values; the extracted impedance peak value is sent to a Python node through a TCP/IP protocol so as to calculate intrinsic parameters of the single cell through a trained neural network, and a calculation result is displayed in a graphical interface in the whole processing process.
8. A method for measuring the single-cell electrical intrinsic parameters in real time by using the high-throughput real-time single-cell electrical intrinsic parameter measuring system according to any one of claims 1 to 7, which comprises the following steps:
step S1: modifying a flow channel, specifically, pretreating the micro flow channel of the micro flow device chip for 15 minutes by using 1 wt% of Pluronic F-127 surfactant, namely 1x PBS;
step S2: cell feeding, in particular using a syringe pump to pass a single cell sample into a microchannel at a constant flow rate of 1 μ L/min, wherein the equivalent velocity at the measurement zone is 20mm/s, thereby achieving a high throughput of over 1000 cells/min;
step S3: signal measurement, specifically, when a single cell passes through a measurement region in a flow channel in a queue manner, the generated electric signal is collected by an impedance measurement system and is solved by a real-time processing algorithm system to obtain the intrinsic parameters of the single cell;
step S4: and displaying the intrinsic parameters of the single cell obtained in the step S3 on a user interface in real time.
9. The method of claim 8, wherein the step of acquiring the electrical signal by the impedance measurement system and obtaining the intrinsic parameters of the single cell by the real-time processing algorithm system in step S3 comprises:
substep S31: the electrical properties of the cell-solution mixed system are expressed by the properties of single cells and suspension medium, the complex impedance of which is given by equation (1):
where κ is the cell constant, depending on the geometric parameters, l is the electrode length,
is the complex dielectric constant of a cell-solution mixed system, wherein sigma is the conductivity, epsilon is the dielectric constant, and omega is the angular frequency, and is calculated as shown in formula (2):
wherein
And
is the complex permittivity of the medium and the cells,
is the volume fraction of the total amount of the polymer,
is the Clausius-Mossotti coefficient, given by equation (3):
substep S32: constructing an equivalent circuit model ECM when a single cell is located between a pair of planar measurement electrodes of the microfluidic device chip, wherein the impedance is represented by a solution resistance RmedSolution capacitor CmedCytoplasmic resistance RiCell membrane capacitance CmemAnd an electric double layer capacitor CDLAnd stray capacitance CsComposition, the impedance of a cell-solution mixed system is characterized as shown in formula (4):
considering CsAnd CDLThe model total impedance is expressed as shown in equation (5):
substep S33: model fitting and parameter extraction are performed at four frequencies of 45KHz, 250KHz, 750KHz and 1200KHz, respectively, where 45KHz and 250KHz are used to determine CDLAnd cell size, 750KHz for determining cell membrane permittivity, 1200KHz for determining cytoplasmic conductivity and Cs(ii) a Measuring impedance values by minimizing using least squares and gradient descent
And the model estimate, and the model total impedance
The variance between them determines the intrinsic parameters of a single cell as shown in equation (6):
step S34: based on the mixing principle, the cosine signals of the four frequency points in step S33 are modulated by an adder, which is expressed as shown in equation (7):
S(t)＝cosω1t+cosω2t+cosω3t+cosω4t (7)，
and then demodulating the response signal R (t) into real voltage and virtual voltage through four demodulators respectively, wherein the equation (8) is as follows:
using a known reference feedback resistor R used in a transimpedance amplifier TIAFAnd measuring the calibration coefficients of the system, thereby calculating the complex impedance.
10. The method for measuring the electrical intrinsic parameters of the single cell in real time as claimed in claim 8, wherein the processing procedure of the real-time processing algorithm system comprises the following sequence:
sequence 1: a deep full-connection neural network FCN is built under an open-source frame Pythrch and used for processing impedance data streams, so that real-time solution of the intrinsic parameters of the single cells is realized; the designed FCN consists of five layers, wherein the first layer is an input layer and is responsible for receiving input data; the middle three layers are hidden layers and are responsible for mapping input data to output data; the last layer is an output layer and is responsible for exporting output results; each layer of the FCN is composed of a plurality of neurons, the neurons obtain weighted sums of previous layers, and the weighted sums are converted into outputs by utilizing a linear rectification ReLU activation function;
sequence 2: setting the input of the FCN as the single-cell impedance amplitude of four frequency points, setting the output as an intrinsic parameter, selecting a mean square error function as a loss function to calculate the deviation between a tag value and an output value, and performing supervision training on the FCN by using a random gradient descent SGD optimizer to back-propagate errors; obtaining intrinsic parameter estimation values through fitting based on a gradient descent algorithm model, and using the intrinsic parameter estimation values as target labels of training data; the complete data set is divided into two data sets, namely a training set accounting for 70% and a testing set accounting for 30%, which are respectively used for training and testing.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114112808A (en) * | 2021-11-05 | 2022-03-01 | 国家纳米科学中心 | Method for characterizing cytoplasmic mechanical properties |
| CN114350490A (en) * | 2021-12-30 | 2022-04-15 | 杭州电子科技大学 | Detection platform for measuring cell S parameters and detection method thereof |
| CN117487883A (en) * | 2023-12-29 | 2024-02-02 | 嘉兴朝云帆生物科技有限公司 | Microfluidic detection method and system for nucleic acid analysis |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102460114A (en) * | 2009-06-05 | 2012-05-16 | 皇家飞利浦电子股份有限公司 | Multi -frequency impedance method and apparatus for discriminating and counting particles expressing a specific marker |
| CN103439241A (en) * | 2013-08-23 | 2013-12-11 | 东南大学 | Micro-fluidic chip detection system based on single-cell multi-parameter representation |
| CN104941704A (en) * | 2015-05-27 | 2015-09-30 | 东南大学 | Method for integrating focusing and detection of cells and miniaturized system thereof |
| CN109321456A (en) * | 2018-10-12 | 2019-02-12 | 江苏大学 | A kind of micro-fluidic chip cell culture control device and method |
| CN109682745A (en) * | 2019-01-07 | 2019-04-26 | 清华大学 | A kind of unicellular measurement method of parameters and device |
| CN111564183A (en) * | 2020-04-24 | 2020-08-21 | 西北工业大学 | Single cell sequencing data dimension reduction method fusing gene ontology and neural network |
| WO2020180252A1 (en) * | 2019-03-05 | 2020-09-10 | Singapore University Of Technology And Design | Microfluidic device for single cell processing and method and system for single cell biophysical phenotyping using the microfludic device |
| CN113029917A (en) * | 2021-02-22 | 2021-06-25 | 中国科学院空天信息创新研究院 | Cell and cell nucleus bioelectricity characteristic detection device and method |
-
2021
- 2021-07-19 CN CN202110821829.8A patent/CN113567326A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102460114A (en) * | 2009-06-05 | 2012-05-16 | 皇家飞利浦电子股份有限公司 | Multi -frequency impedance method and apparatus for discriminating and counting particles expressing a specific marker |
| CN103439241A (en) * | 2013-08-23 | 2013-12-11 | 东南大学 | Micro-fluidic chip detection system based on single-cell multi-parameter representation |
| CN104941704A (en) * | 2015-05-27 | 2015-09-30 | 东南大学 | Method for integrating focusing and detection of cells and miniaturized system thereof |
| CN109321456A (en) * | 2018-10-12 | 2019-02-12 | 江苏大学 | A kind of micro-fluidic chip cell culture control device and method |
| CN109682745A (en) * | 2019-01-07 | 2019-04-26 | 清华大学 | A kind of unicellular measurement method of parameters and device |
| WO2020180252A1 (en) * | 2019-03-05 | 2020-09-10 | Singapore University Of Technology And Design | Microfluidic device for single cell processing and method and system for single cell biophysical phenotyping using the microfludic device |
| CN111564183A (en) * | 2020-04-24 | 2020-08-21 | 西北工业大学 | Single cell sequencing data dimension reduction method fusing gene ontology and neural network |
| CN113029917A (en) * | 2021-02-22 | 2021-06-25 | 中国科学院空天信息创新研究院 | Cell and cell nucleus bioelectricity characteristic detection device and method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114112808A (en) * | 2021-11-05 | 2022-03-01 | 国家纳米科学中心 | Method for characterizing cytoplasmic mechanical properties |
| CN114112808B (en) * | 2021-11-05 | 2024-02-20 | 国家纳米科学中心 | Characterization method of cytoplasmic mechanical properties |
| CN114350490A (en) * | 2021-12-30 | 2022-04-15 | 杭州电子科技大学 | Detection platform for measuring cell S parameters and detection method thereof |
| CN114350490B (en) * | 2021-12-30 | 2024-03-22 | 杭州电子科技大学 | Detection platform and detection method for measuring cell S parameters |
| CN117487883A (en) * | 2023-12-29 | 2024-02-02 | 嘉兴朝云帆生物科技有限公司 | Microfluidic detection method and system for nucleic acid analysis |
| CN117487883B (en) * | 2023-12-29 | 2024-03-29 | 嘉兴朝云帆生物科技有限公司 | Microfluidic detection method and system for nucleic acid analysis |
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