Living cell sampling detection device, method and system

Abstract

The invention belongs to the technical field of microfluidic analysis and detection, and particularly relates to a living cell sampling and detecting device, method and system. The device comprises a rotary micro-fluidic chip and an amplification detection reagent, the rotary micro-fluidic chip is formed by sequentially assembling an upper electrode, a nanopore film, a sampling layer, a lower electrode and a detection layer from top to bottom; the rotary micro-fluidic chip is of a detachable structure, and the structures can be connected through a central bearing; the nanopore membrane is arranged in the electric sampling area of the sampling layer and is used for continuously sampling living cells; a gene detection area is arranged on the detection layer, and an amplification detection reagent is placed in the gene detection area and is used for detecting the expression quantity of genes in cells. The invention provides a method capable of continuously sampling genes in living cells and accurately quantifying the genes in a simple environment.

Description

Technical Field

[0001] This invention belongs to the field of microfluidic analysis and detection technology, specifically relating to a live cell sampling and detection device, method, and system. Background Technology

[0002] Pathological gene alterations in living cells under environmental stimuli are closely related to tumor development and progression. For example, mutations in certain genes can lead to drug resistance in cancer cells during tumor treatment. Real-time monitoring of these intracellular gene changes allows for precise detection of abnormal cellular states, providing valuable insights into molecular mechanisms and optimizing personalized treatment strategies. However, a major challenge in intracellular gene monitoring lies in achieving accurate detection of low-abundance genes while maintaining cell viability.

[0003] Traditional gene detection technologies, such as qPCR, require cell lysis to extract internal genes for analysis. This limits their use to endpoint gene analysis and prevents continuous gene monitoring in live cells. Currently, live-cell gene analysis technologies can be mainly divided into two categories: "introduction methods" and "extraction methods." Introduction methods involve introducing specific detection tools (such as fluorescent probes) into the cell to achieve in-situ gene identification. This method places extremely high demands on probe sensitivity and interference resistance. Furthermore, due to the poor metabolic clearance characteristics of these functional molecules, related platforms can only perform single-sample detection. Extraction methods typically employ nanotube aspiration technology, using hollow nanotubes to puncture the membrane of a single cell under micromanipulation to extract intracellular substances for subsequent gene analysis. Although this method enables continuous sampling and detection, its single-cell throughput is limited, and it relies on a lengthy setup process, which restricts its application scenarios. In summary, no existing technology provides a feasible method for real-time gene monitoring in live cells.

[0004] Some data detection methods have also been proposed in the existing technology: For example, patent application CN202210074017 discloses a method for screening tRNA methyltransferase inhibitors. The steps include constructing an in vitro reaction screening system, mixing the small molecule compound to be screened with the in vitro reaction screening system to obtain the final product, performing DotBlot analysis on the final product to obtain the DotBlot scan of the in vitro reaction screening system, classifying the small molecule compound samples in the DotBlot scan, analyzing the classified small molecule compound samples to obtain defective samples, repairing the defective samples, and screening the small molecule compounds.

[0005] For example, patent application CN201380007711.5 discloses a computer-based specimen analyzer configured to: detect gene expression levels in a cell sample by detecting spots representing different stained genes and chromosomes in the cells. The colors of the stained genes and chromosomes are enhanced and filtered to produce a dot mask defining regions in an image of genes, chromosomes, or non-genetic material. A metric is determined for the spots and / or the pixels in the cell image corresponding to the spots within those regions. The metric is fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate gene expression levels in the tissue sample.

[0006] However, the above methods are difficult to achieve accurate quantitative analysis.

[0007] For example, patent application 202311495178.3 discloses a portable intelligent detection micro-platform and method for accurate and rapid fluorescence quantitative target concentration. The micro-platform includes a microfluidic chip, a nucleic acid fluorescence quantitative reaction system, and a quantitative detection system. The quantitative detection system includes an image acquisition module for acquiring fluorescence images of the micro-reaction chamber after the target nucleic acid fluorescence reaction is completed based on the nucleic acid fluorescence quantitative reaction system, an image optimization module, a micro-reaction chamber identification module for identifying the micro-reaction chamber in the fluorescence image of the micro-reaction chamber and acquiring its RGB intensity, and a quantitative module for quantitatively detecting the target nucleic acid based on the RGB intensity of the micro-reaction chamber and a pre-constructed linear relationship between RGB and target concentration.

[0008] However, when faced with some special testing scenarios (especially simple testing scenarios with trace samples), the difficulty of quantitative analysis increases significantly.

[0009] In summary, there is a need to propose a system capable of real-time and precise monitoring of genes within living cells. Summary of the Invention

[0010] The purpose of this invention is to provide a live cell sampling and detection device, method and system, which partially solves or alleviates the above-mentioned deficiencies in the prior art. The specific technical solution adopted by this invention is as follows.

[0011] A first aspect of the present invention is to provide a live cell sampling device.

[0012] A live cell sampling and detection device includes a rotating microfluidic chip and an amplification detection reagent; the rotating microfluidic chip is composed of an upper electrode (110), a nanoporous membrane (210), a sampling layer (220), a lower electrode (120), and a detection layer (230) assembled from top to bottom; The upper electrode (110), sampling layer (220), lower electrode (120) and detection layer (230) are detachable structures and are connected by a central bearing (240); The nanoporous membrane (210) is disposed within the electrical sampling area (220a) of the sampling layer (220) for continuous sampling of live cells; The detection layer (230) is provided with a gene detection area (230a), and the amplification detection reagent is placed in the gene detection area (230a) for detecting the expression level of genes in cells; The electrical sampling area (220a) and the gene detection area (230a) are connected by an inclined microchannel (220b) on the sampling layer (220); The detection layer (230) is also provided with a first marking hole 230b and a second marking hole 230c for indicating the signal of a positive control or a negative control.

[0013] Furthermore, each of the electrical sampling regions (220a) consists of two cylindrical chambers, one larger at the top and one smaller at the bottom, separated by a nanoporous membrane (210); the gene detection region (230a) is a cylindrical chamber.

[0014] Furthermore, the lower chamber of the electrical sampling area (220a) and the upper chamber of the gene detection area (230a) are connected by an inclined microchannel (220b).

[0015] Furthermore, the primer sequences of the amplification detection reagent are shown in SEQ ID NO.1-14.

[0016] In another aspect, the present invention also provides a method for real-time quantitative analysis of genes in living cells.

[0017] The method for real-time gene quantification analysis of live cells based on the above-mentioned live cell sampling and detection device includes the following steps: S101: The cells to be tested are seeded on a nanoporous membrane (210), and cell culture medium is filled into the upper chamber of the electrical sampling area (220a) of the live cell sampling and detection device, and buffer solution is filled into the lower chamber of the electrical sampling area (220a). S102: Connect the upper electrode (110) and lower electrode (120) of the live cell sampling and detection device to an external power source, respectively, and apply a pulsed electric field to the cells in the electrical sampling area (220a) to perform cell nano-electric sampling, so that the RNA in the cells enters the lower chamber of the electrical sampling area (220a). S103: Rotate the live cell sampling and detection device so that the RNA-containing solution in the lower chamber of the electrosampling area (220a) enters the gene detection area (230a) through the microchannel (220b); S104: Remove the detection layer (230) and place it at the set temperature to allow the RNA to react with the amplification detection reagent; S105: At least one first image is captured by the live cell sampling and detection device, and the target expression level is calculated from the first image.

[0018] Furthermore, S105 includes: The first image is corrected using an image correction method, which includes: S1051, Perform a first blurring process on the first image to obtain a second image; S1052, perform grayscale processing on the second image to obtain the third image; S1053, Perform a second blurring process on the third image to obtain a fourth image; S1054, a target connected region is identified from the fourth image using a preset connected region detection rule, wherein the connected region detection rule requires the target connected region to have the following characteristics: (1) the inertia ratio is within a preset inertia ratio range; (2) the pixel scale is within a preset scale range; (3) the area is within a preset area range; and (4) the convexity is within a preset convexity range. The data correction method is used to correct the expression of the target connected region, which includes: S1055, calculate a set of gene expression levels based on the target connected region, wherein the set of gene expression levels includes: a first expression level of at least one of the gene detection regions (230a), and a second expression level of at least one marker well; S1056, Generate correction coefficients based on the standard expression level of the marked aperture and the second expression level; S1057, The target expression level is calculated based on the correction coefficient and the first expression level.

[0019] Furthermore, the method of correcting the expression of the target connected region using data correction also includes the following steps: At least one blur parameter is selected from the set blur scale to blur the first image or the third image to obtain a blurred image after blur processing, wherein the blurred image is the second image or the fourth image; wherein the numerical range of the set blur scale is [0.1,2], and the blur scale is used to define the blur radius in the blur operation process; Identify the number of target connected regions in the blurred image; Determine whether the quantity falls within a predetermined quantity range, which is defined by the number of gene detection areas and marker wells; If yes, then keep the fuzzy parameters; otherwise, it is recommended to update the fuzzy parameters.

[0020] Furthermore, prior to S1051, the method further includes: performing pixel expansion on the first image using a bilinear interpolation algorithm.

[0021] Furthermore, S1054 includes: acquiring the G / B channel ratio of at least one pixel; when the G / B channel ratio is within a preset value range, the pixel is identified as a feature point. When the distance between at least two adjacent feature points is less than the target distance, the feature points are identified as a connected region. The connected component detection rules are used to determine whether the connected component is the target connected component; And / or, the blur size of the first blurring process does not exceed 10 pixels.

[0022] In another aspect, the present invention provides a live cell sampling and detection system, comprising: a live cell sampling and detection device as described in any embodiment; A computing device includes: one or more processors; and one or more memories coupled to the one or more processors and storing instructions thereon, which, when executed individually or jointly by the one or more processors, cause the computing device to perform the method described in any of the embodiments.

[0023] Beneficial technical effects: This invention first provides a live cell sampling and detection device that integrates the traditional LAMP amplification reaction, which is carried out in a large reaction vessel, onto a microfluidic chip, enabling more accurate and efficient quantitative analysis of target genes. In addition, the reaction requires less sample volume, making it suitable for the analysis and detection of rare clinical samples.

[0024] Furthermore, based on the special scenario of small sample size and simple imaging equipment, the present invention provides a restrictive correction scheme that combines a phased correction scheme with coefficient correction (or, in other words, image correction and data correction).

[0025] In this way, the restrictive hierarchical correction works in conjunction with the coefficient correction. On the one hand, it can eliminate interference factors generated in a simplified environment to a certain extent. On the other hand, by working together with image correction (such as normalization and blurring) and data correction, it can avoid overcorrection that may introduce new data biases (for example, avoid overcorrection of small regions of a small number of samples in an image), thus affecting the accuracy and reliability of the calculation results. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0027] Figure 1 This is a schematic diagram of the overall structure of a live cell real-time sampling and detection chip device. Figure 2 This is a schematic diagram of a planar structure of a live cell real-time sampling and detection chip device; Figure 3 This is a layered structure diagram of a chip device for real-time sampling and detection of live cells. Figure 4 This is a schematic diagram of cell nanoelectroelectric sampling using the live cell real-time sampling and detection chip device of the present invention; Figure 5 This is a schematic diagram illustrating the detection process using the live cell real-time sampling and detection chip device of the present invention. Figure 6 The simulation results of electrical sampling were obtained using a simulation model; Figure 7 This is a comparison of the fluid simulation results using a simulation model and the sample distribution volume after chip rotation; Figure 8 The result is the optimized electrical sampling parameters; Figure 9 Experimental results were optimized for LAMP reaction conditions; Figure 10 Results of LAMP primer specificity test; Figure 11 Results of LAMP primer sensitivity determination experiment; Figure 12 This is a schematic diagram of the fuzzy parameter adjustment interface in an exemplary embodiment of the present invention; Figure 13 This is a schematic diagram illustrating the principle of bilinear interpolation processing of images based on the INTER_LINEAR_EXACT command. Figure 14 This is a sample result image based on image-based blob detection and quantitative analysis. Figure 15 The fitted straight line represents the relationship between RNA concentration and G / B value. Figure 16 The results are from clinical sample testing using the live cell real-time sampling and detection chip device of the present invention.

[0028] Summary of attached labeling and identification: Upper electrode 110, lower electrode 120, nanoporous membrane 210, sampling layer 220, electrical sampling area 220a, microchannel 220b, detection layer 230, gene detection area 230a, first marker well 230b, second marker well 230c, bearing 240. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0030] Example 1 This embodiment provides an example of a live cell real-time sampling and detection chip device and detection method.

[0031] The rotating microfluidic chip system provided in this embodiment consists of a chip body, amplification detection reagents (specifically reagents containing LAMP primers) and a quantitative analysis program. The chip body is composed of an upper electrode 110, a nanoporous membrane 210, a sampling layer 200, a lower electrode 120 and a sampling layer 230 stacked together in sequence. The chip body is a detachable structure and is connected by a high-precision bearing 240, which allows the chip to rotate at high speed.

[0032] In some embodiments, a nanoporous membrane 210 is disposed in the upper chamber of the electrical sampling region 220a. In some embodiments, six sets of electrical sampling regions 220a are disposed on a single chip body. In some embodiments, a plurality of gene detection regions 230a are disposed on the detection layer 230; a reagent containing LAMP primers is disposed in the detection chamber of the gene detection region 230a, wherein the LAMP primers are 14-base sequences used to detect the expression level of genes in the target cell. The number of gene detection regions 230a is greater than the number of electrical sampling regions 220a.

[0033] For example, in some embodiments, the electrical sampling area 220a may be connected to at least one detection cavity, and when the chip is packaged as a whole (i.e., when the sampling layer and the detection layer are combined), the detection cavity connected to the electrical sampling area 220a is connected to the gene detection area to form a complete detection area.

[0034] In some embodiments, at least three of the gene detection regions (230a) and one of the electrical sampling regions (220a) form a set of sample detection regions. In some specific embodiments, the detection layer 230 is provided with partitions A, B, C, D, E, and F, and each partition is provided with three sets of detection chambers.

[0035] In some embodiments, the gene detection region 230a and the electrosampling region 220a are connected via a microchannel 220b. The microchannel 220b has a certain upward angle, preventing liquid transfer in a static state. However, when the chip rotates at high speed, the test solution in the sampling chamber of the electrosampling region 220a will transfer to the detection chamber of the gene detection region 230a. When the upper electrode 110 and the lower electrode 120 are respectively connected to an external electric field, the nanopores on the nanoporous membrane 210 can focus the electric field, causing electroporation of the test cells and extracting the mRNA from the test cells into the lower chamber of the electrosampling region 220a.

[0036] In some embodiments, the detection layer 230 is further provided with a first marker hole 230b and a second marker hole 230c for indicating signals of a positive or negative control. See the chip body structure for details. Figures 1-3 .

[0037] In one specific implementation, the chip is a disk-shaped structure with a radius of 30 mm and a height of 9 mm, primarily made of polymethyl methacrylate (PMMA). A bearing 240 with an inner diameter of 1.5 mm, an outer diameter of 2 mm, and a height of 12 mm is located at the center of the chip for chip rotation, providing power for the extraction of extract from the electrosampling area 220a to the gene detection area 230a. Six identical cell culture chambers are arranged on the sampling layer 220. Each cell culture chamber consists of two cylindrical chambers with radii of 3 mm and 2 mm, separated by a nanoporous membrane 210. The lower chamber of the sampling area of ​​the electrosampling area 220a is connected to three microchannels 220b, used to evenly distribute the sampled liquid into the three detection chambers of the gene detection areas 230a. The microchannels 220b have an upward angle to prevent backflow. Platinum electrodes are attached to the upper and lower sides of the sampling layer 220 to provide an electric field of appropriate intensity for cell sampling. The gene detection area 230a, consisting of 18 detection chambers, is located on the outer side of the detection layer 230. Each detection chamber is a cylindrical cavity with a radius of 2 mm and a height of 2 mm, containing LAMP reagent for gene detection, which reflects the expression level of cell-specific mutated genes. See [link to relevant documentation]. Figure 2 .

[0038] Before assembly, all components must be sterilized at high temperature. Then, the nanoporous membrane 210 is cut into circles with a radius of 3 mm and attached to the connection between the upper and lower cell culture chambers. The bearing is embedded in the center of the sampling layer 220, and the positive and negative electrodes are positioned on the upper and lower sides of the sampling layer 220. The corresponding LAMP reagent is added to the detection chamber of the gene detection area 230a. Finally, the chip is assembled from top to bottom and securely fixed. See [link / reference] Figure 3 .

[0039] The chip in this embodiment integrates nanoelectroporation technology, LAMP isothermal amplification technology, and image recognition technology. It can perform non-destructive multiple samplings of the same cell and conduct in-situ detection. It has high sensitivity and high specificity in detecting mutated genes. The specific detection process is as follows.

[0040] S01: Cell Culture. After processing the clinical sample, primary culture of lung cancer cells is performed in the culture chamber above the sampling area. Those skilled in the art will understand that the lung cancer cells used in this invention are merely an example, and the apparatus, method, and system of this invention can also be used for sampling and detection of other tumor cells.

[0041] S02: Cellular Nanoelectro-Sampling. The upper electrode of the sampling area is connected to the negative terminal of the power supply, and the lower electrode is connected to the positive terminal. During sampling, the upper cavity (culture cavity) of the sampling area is filled with cell culture medium, and the lower cavity (sampling cavity) is filled with PBS solution. A specific pulsed electric field is applied to the sampling area using the power supply to complete the cellular nanoelectro-sampling. The parameters of the pulsed electric field are determined by previous electro-sampling parameter optimization experiments, ensuring both high sampling efficiency and the biological activity of the sampled cells.

[0042] S03: Rotation. The storage cavity below the sampling area is connected to the detection area through a microchannel with a certain tilt angle. When stationary, the liquid in the storage cavity will not flow into the detection area. When the chip rotates to a certain speed under the action of external force, the liquid in the storage cavity will be evenly dispersed and flow to the three detection cavities under the action of centrifugal force.

[0043] S04: Detection. The detection area has 20 detection chambers, including two for the negative and positive control groups, six for the GAPDH internal control, six for the DEL mutation, and six for the L858R mutation. The positive control group contains standard concentrations of RNA and LAMP amplification reagent, while the negative control group contains only LAMP amplification reagent, used to calibrate the LAMP amplification detection results. The GAPDH internal control chamber contains LAMP amplification primers for the GAPDH gene; its detection results reflect the efficiency of cell sampling and provide a reference standard for evaluating the expression level of the mutant gene. The DEL and L858R mutation detection chambers each contain LAMP amplification primers for their respective mutant genes, used to reflect the expression level of the mutant gene in the tested cells. During the detection process, the sampling layer and detection layer must be separated first. The sampling layer is placed in a cell culture incubator to continue culturing the cells in the chip. The detection layer is placed on a hot plate at 65 degrees Celsius and heated continuously for 30 minutes to ensure complete amplification. After the reaction was complete, SYBR Green I was added to the detection chamber and thoroughly mixed. Images of the detection layer were then acquired and imported into a computer. Image recognition software was used to process the amplification results, enabling qualitative and quantitative analysis of the expression levels of mutated genes in the cells. See [link to relevant documentation]. Figure 4 ,See Figure 5 .

[0044] Example 2 This embodiment provides the result analysis of sample detection in Embodiment 1.

[0045] 1. LAMP primer design The LAMP reaction primarily uses four primers: two outer primers (F3 and B3) and two inner primers (FIP and BIP). During design, it's crucial to ensure the primers bind to specific regions of the target RNA while avoiding cross-reactions with other non-target sequences. Primer lengths are generally between 18 and 30 bases. Furthermore, excessively high GC content should be avoided to prevent interference with the annealing reaction. The complexity of the amplification region should also be considered during design; simpler and more stable regions should be selected for primer design to reduce the risk of amplification failure. Following these LAMP primer design principles, the final LAMP primers selected are shown in Table 1.

[0046] Table 1 Primer Sequences 2. Electrical sampling simulation The simulation model was built using AutoCAD 2020 software. The model accurately recreates the real-world scenario of the chip performing nanometer-level electrical sampling. A schematic diagram of the model is shown below. Figure 6As shown. The cell culture chamber is 165 micrometers wide and 50 micrometers high, the storage chamber is 150 micrometers wide and 20 micrometers high, the nanoporous membrane is 35 micrometers thick and has a pore diameter of 800 nanometers, the cell attachment portion is 30 micrometers wide and 7.5 micrometers high, and the cell membrane is 10 nanometers thick.

[0047] The simulation was performed using the finite element analysis software COMSOL Multiphysics 5.5. The cell culture environment was PBS solution with a conductivity of 11.5 Siemens per meter. The nanoporous membrane was made of polycarbonate (PC) with a conductivity of 1 e.g., ... -15 Siemens per meter. The cell membrane conductivity is 5e. -7 The conductivity of the intracellular interstitial fluid is 0.2 Siemens per meter. The upper layer is the negative electrode with a voltage of 0V, and the lower layer is the positive electrode with a voltage of 15V. The overall simulation results are as follows: Figure 6 As shown, this study investigates the relationship between changes in the microstructure of cells and the electric field during nano-electric sampling.

[0048] When the electrode is just energized, the potential distribution is as follows: Figure 6 As shown in Figure A, the potential difference across the cell membrane and the electrophoretic force on the contents near the cell membrane are as follows: Figure 6 As shown in Figure D, the potential difference between the inside and outside of the adherent cell membrane is very large, and it is distributed with a high potential in the middle and low potential on both sides. The maximum potential difference can reach about 6V. When the potential difference between the inside and outside of the cell membrane exceeds 1V, nanoelectroporation occurs. According to simulation results, about 97% of the adherent cell membrane will undergo nanoelectroporation.

[0049] According to simulation results, the potential difference is greatest at the center of the cell membrane, therefore, perforation will preferentially occur at the center. Figure 6 B illustrates the electric field distribution after the cell membrane is perforated at the center. At this point, the potential difference across the cell membrane and the electrophoretic force acting on the contents near the cell membrane are as follows: Figure 6 As shown in E, the potential difference across the cell membrane at the top is very large, thus electroporation also occurs at the top.

[0050] Figure 6 C illustrates the electric field distribution after simultaneous perforation at the bottom and top of the cell membrane. The potential difference across the cell membrane and the electrophoretic force acting on the contents near the cell membrane are shown in Figure 1. Figure 6 As shown in Figure F, the potential difference at the base of the cell is approximately equal to the threshold for electroporation, thus the cell membrane is in a state of dynamic equilibrium. Simultaneously, an increase in the electric field force on the cell contents and an outward-pointing electrophoretic force indicate that the cell contents can pass through the cell membrane and nanopores into the storage cavity.

[0051] 3. Fluid Simulation of Chips A high-precision bearing is placed at the center of the chip to ensure high-speed rotation. Measurements show that the chip's rotation speed can reach 1000-1400 rpm. The rotation transfers the test solution from the storage chamber to the detection chamber. To verify that the test solution can flow completely and uniformly into the detection chamber, fluid simulation was performed using the finite element analysis software COMSOL Multiphysics 5.5. The simulation model is drawn based on the actual distribution of the liquid within the chip, with the top of the sampling area and the bottom of the detection area as open boundaries, forming the inlet and outlet for the liquid flow. Since the chip's rotation speed in actual use can reach over 1000 rpm, the rotation speed was set to 1000 rpm, and the dynamic viscosity of the liquid was set to 1.01 e. -3 Pa·s. Dynamic viscosity is an important physical quantity in fluid mechanics, used to measure the internal friction or flow resistance of a fluid, and is an important parameter for studying fluid flow characteristics. Simulation results are as follows: Figure 7 As shown, the highest flow velocity in the microchannel is 1.2 mm / s. Given that the volume of the storage cavity is 40 and the flow rate of the microchannel at this rotation speed is 3.768, it can be concluded that the chip needs to rotate at a speed of 1000 rpm for at least 10.6 seconds to ensure that the liquid in the storage cavity completely flows into the detection cavity.

[0052] 4. Optimization of electrical sampling parameters (1) A549, H1975, and HCC827 cells were placed in different culture chambers of the chip, respectively, to ensure that the cell density on the nanopore membrane was 1×10⁻⁶. 4 About 100 cells were placed in a cell culture incubator and left to stand for about 4 hours to allow the cells to adhere completely to the incubator.

[0053] (2) Connect the electrode of the chip in Example 1 to the negative terminal of the electro-rotator, and connect the lower electrode to the positive terminal of the electro-rotator. Turn on the power to complete the electro-rotation. The electric field parameters are shown in Table 2.

[0054] Table 2 Electric Field Parameters (3) Remove the power cord, rotate the chip, and ensure that the rotation speed is above 1000 rpm and the duration is greater than 11 seconds.

[0055] (4) Separate the sampling layer and the detection layer. Digest and stain the cells in the culture chamber of the sampling layer and calculate the cell viability. Take out the test solution in the detection chamber and perform qPCR amplification to analyze the sampling efficiency.

[0056] Experimental results are as follows Figure 8 As shown, by optimizing the voltage ( Figure 8 A) Number of pulses ( Figure 8 B) and pulse width ( Figure 8C) Three key parameters were used to assess the balance between electrosampling efficiency and cell viability. In the experiment, cell dyes were used to determine cell viability, and the Ct value from qPCR amplification was used to evaluate electrosampling efficiency. Combining the results from both aspects, a voltage of 20V, a pulse count of 200, and a pulse width of 15ms effectively improved cell membrane permeability, promoted substance extraction, and avoided damage to cells from excessive electrotherapy.

[0057] 5. LAMP reaction temperature optimization The LAMP reaction temperature is generally set between 60℃ and 65℃. Different experimental systems, target DNAs, and RNA templates have different temperature requirements, and the optimal temperature for the reaction system of this invention needs to be determined experimentally. This study systematically compares the amplification efficiency, specificity, and stability of LAMP reactions at different temperatures to explore the optimal temperature conditions for LAMP reactions and provide a temperature reference for practical operation. The experimental operation steps are as follows.

[0058] (1) Dissolve 2× Lamp Master Mix and primers on ice, construct the reaction system according to Table 3 and mix thoroughly.

[0059] (2) Place the mixed EP tube into the PCR instrument and amplify for 40 minutes at 63℃, 64℃, 65℃, 66℃ and 67℃ respectively.

[0060] (3) Raise the temperature to 80°C and hold for 10 minutes to inactivate DNA Polymerase.

[0061] (4) Derive the fluorescence intensity of the reaction system per minute and record the Tt value (Time to Target).

[0062] Table 3 Figure 9 The results of the LAMP reaction condition optimization experiment are shown. The results show the Tt values ​​of three LAMP primers for the GAPDH, DEL and L858R genes in the range of 63℃ to 67℃. It was found that the optimal amplification temperature for all three primers is 65℃.

[0063] 6. LAMP primer specificity test The designed primers need to have high specificity to improve the accuracy and reliability of the LAMP reaction. The reaction requires four primer pairs (forward-in, forward-out, reverse-in, and reverse-out), which initiate the reaction through specific binding mechanisms and produce circular amplification products. Inappropriate primer design can lead to non-specific amplification or false positive results, thus affecting the reliability of the experiment. A comprehensive analysis of primer specificity in LAMP experiments is crucial to ensuring experimental success. This experiment aims to conduct a detailed analysis of the specificity of LAMP primers, evaluating their binding ability in the target region and the specificity of the reaction. Effective screening of efficient and accurate primer pairs, and ensuring that the LAMP reaction amplifies only the target DNA or RNA, avoiding interference from non-target sequences, will improve the accuracy and reliability of the LAMP reaction. The experimental steps are as follows.

[0064] (1) Using a chip, nanoelectric sampling was performed on three cell lines, A549, H1975 and HCC827, and the sampled samples were saved.

[0065] (2) The three samples were placed into the three prepared LAMP reaction systems (GAPDH, DEL, L858R) respectively, and ddH2O was used to set up a negative control group.

[0066] (3) Place the mixed EP tube into the PCR instrument and amplify at 65°C for 40 minutes.

[0067] (4) Raise the temperature to 80°C and hold for 10 minutes to inactivate DNA Polymerase.

[0068] (5) Measure the final fluorescence intensity of each reaction system (the excitation wavelength of SYBR Green is 495 nm and the emission wavelength is 520 nm). Repeat the experiment three times.

[0069] The results of the LAMP primer specificity assay are as follows: Figure 10 As shown, the A549 cell line has no EGFR mutation and is a wild-type lung cancer cell line; therefore, its samples were positive only for the GAPDH primer. The H1975 cell line has an L858R mutation; therefore, its samples were positive in the GAPDH and L858R primer reaction system but negative in the DEL primer reaction system. The HCC827 cell line has a DEL mutation; therefore, its samples were positive in the GAPDH and DEL primer reaction system but negative in the L858R primer reaction system.

[0070] 7. LAMP primer amplification efficiency and sensitivity test Evaluating and optimizing LAMP primer amplification efficiency is fundamental to ensuring reaction success and data reliability. The experimental steps for determining LAMP primer amplification efficiency are as follows.

[0071] (1) Construct standards by diluting the customized RNA templates of the three target genes GAPDH, DEL and L858R to 10 pmol / L, 100 pmol / L, 1000 pmol / L, 10000 pmol / L and 100000 pmol / L respectively, and setting up a negative control group with ddH2O.

[0072] (2) Add each standard to the corresponding LAMP reaction system.

[0073] (3) Place the mixed EP tube into the PCR instrument and amplify at 65℃ for 40 minutes.

[0074] (4) Raise the temperature to 80°C and hold for 10 minutes to inactivate DNA Polymerase.

[0075] (5) Measure the amplification curves of each reaction system, monitor the reaction process in real time, and repeat the experiment three times.

[0076] The experimental results of LAMP primer sensitivity determination are as follows: Figure 11 As shown, Figure 11 A is the RNA concentration-Tt relationship graph for GAPDH primers, which shows that the minimum detection concentration of GAPDH primers is 10. 0 pmol / L ~ 10 1 Between pmol / L, a fitted straight line was generated based on the bar chart, and the slope of the line was -4.877, indicating a high amplification efficiency. Figure 11 B is the RNA concentration-Tt relationship graph for DEL primers, which shows that the minimum detection concentration of DEL primers is 10. 0 pmol / L ~ 10 1 Between pmol / L, a fitted straight line was generated based on the bar chart. The slope of the line was -5.386, indicating a relatively high amplification efficiency, but slightly lower than that of GAPDH primers. Figure 11 C is the RNA concentration-Tt relationship plot of the L858R primer, which shows that the minimum detection concentration of the L858R primer is 10. 1 pmol / L ~ 10 2 Between pmol / L, a fitted straight line was generated based on the bar chart. The slope of the line was -4.694, and the amplification efficiency was slightly higher than that of GAPDH primers.

[0077] 8. Image Recognition-Based Quantitative Analysis Platform Another aspect of the present invention provides a method for real-time gene quantification analysis of live cells using a live cell sampling and detection device, comprising the following steps: S101: The cells to be tested are seeded on the nanoporous membrane 210, and the cell culture medium is filled into the upper chamber of the electrical sampling area 220a of the live cell sampling and detection device, and the buffer solution is filled into the lower chamber of the electrical sampling area 220a. S102: Connect the upper electrode 110 and lower electrode 120 of the live cell sampling and detection device to an external power source, respectively, and apply a pulsed electric field to the cells in the electrical sampling area 220a to perform cell nano-electric sampling, so that the RNA in the cells enters the lower chamber of the electrical sampling area 220a. S103: Rotate the live cell sampling and detection device so that the RNA-containing solution in the lower chamber of the electrosampling area 220a enters the gene detection area 230a through the microchannel 220b; S104: Remove the detection layer 230 and place it at the set temperature (the set temperature can be set by the user according to the experimental conditions) to allow the RNA to react with the amplification detection reagent; S105: At least one first image is captured by the live cell sampling and detection device, and the target expression level is calculated from the first image.

[0078] For example, in some embodiments, the shooting device can be a mobile device such as a mobile phone or tablet.

[0079] For example, in some embodiments, a mathematical relationship (such as a fitted curve) between pixel values ​​and RNA concentration is obtained in advance, so the RNA concentration can be calculated from the pixel values ​​in the image, which can also be referred to as the target expression level.

[0080] In some embodiments, S105 includes: I. Correcting the first image using an image correction method, which includes: S1051, Perform a first blurring process on the first image to obtain a second image; For example, in some embodiments, the blurring process used in this invention is Gaussian blurring.

[0081] S1052, perform grayscale processing on the second image to obtain the third image; S1053, Perform a second blurring process on the third image to obtain a fourth image; S1054, a target connected region is identified from the fourth image using a preset connected region detection rule, wherein the connected region detection rule requires the target connected region to have the following characteristics: 1. Inertia ratio is within a preset inertia ratio range; 2. Roundness is within a preset roundness range; 3. Area is within a preset area range; 4. Convexity is within a preset convexity range. In this embodiment, the first image and the third image are preferably blurred in stages to reduce the impact on the real image data while filtering out interference factors to a certain extent, so as to avoid introducing too many new errors.

[0082] In some embodiments, the specific values ​​of the connected component detection rules can be adjusted by the user according to their recognition needs.

[0083] For example, dimensions such as inertia ratio, convexity, area, or roundness can be set according to the actual experimental scenario, such as adjusting the settings based on the size of the marker well or gene detection area in the live cell sampling and detection device.

[0084] II. The expression of the target connected region is corrected using data correction methods, including: S1055, calculate a set of gene expression levels based on the target connected region, wherein the set of gene expression levels includes: at least one first expression level of the gene detection region 230a, and at least one second expression level of the marker well. S1056, Generate correction coefficients based on the standard expression level of the marked aperture and the second expression level; For example, in some embodiments, the label wells are negative or positive label wells, and their standard expression levels are preset values ​​(e.g., the positive control group contains standard concentrations of RNA and LAMP amplification reagent, while the negative control group contains only LAMP amplification reagent, and the standard expression level is predefined by the concentration of the substance).

[0085] For example, in some embodiments, if the second expression level of the marked aperture is calculated as L1 based on the image, and the standard expression level is L2, then the correction coefficient = L2 / L1.

[0086] S1057, The target expression level is calculated based on the correction coefficient and the first expression level.

[0087] For example, in some embodiments, the target expression level = the first expression level * the correction factor.

[0088] For example, in some embodiments, a first correction coefficient corresponding to the negative marking hole and a second correction coefficient corresponding to the positive marking hole can be calculated, and the average of the first correction coefficient and the second correction coefficient can be calculated as the correction coefficient in S1057.

[0089] The step-by-step correction scheme in this embodiment is coordinated with the coefficient correction, which can reduce the interference caused by the simple shooting environment and the difference between the actual detection scene and the calibration scene.

[0090] It is understandable that the fractional image correction in this embodiment can improve the consistency of different chambers (such as the control group or the response group) to a certain extent. This data correction method based on fractional image correction can significantly improve the accuracy of expression level calculation.

[0091] It should be noted that this invention focuses on providing a restrictive, graded correction operation for interference factors (such as changes in lighting conditions or air bubbles) in simple detection environments (such as mobile phone photos) for trace samples.

[0092] In particular, when taking photos with simple devices such as mobile phones, the image clarity is difficult to guarantee due to differences in mobile phone models and camera performance. Furthermore, the shooting technique of the operator may introduce interference factors during the shooting process (such as noise introduced due to inaccurate focus or improper lighting selection).

[0093] This restrictive hierarchical correction operation can, on the one hand, eliminate interference factors generated in a simplified environment to a certain extent, and on the other hand, by coordinating image correction (such as normalization and blurring) and data correction, avoid overcorrection that may introduce new data biases (for example, avoid overcorrection of small regions of a small number of samples in an image), thus affecting the accuracy and reliability of the calculation results.

[0094] From another perspective, the mobile phone models used by different experimenters often vary significantly, and different mobile devices may have different pre-installed image processing algorithms, which may also blur the true information of the image to some extent. In response, the data correction method based on phased image correction adopted in this invention can limit the size of image blurring, thereby mitigating the adverse effects of shooting defects in simple environments through dual image and data correction.

[0095] Furthermore, in some embodiments, the step of correcting the first image using an image correction method further includes: Identify the first bubble quantity in the first image; Identify the amount of the second bubble in the fourth image; Calculate the bubble difference between the first bubble quantity and the second bubble quantity; If the bubble difference is greater than a preset difference threshold, it is recommended to update the fuzzy scale.

[0096] For example, in some embodiments, the amount of bubbles can be the number of bubbles. Or the amount of bubbles can be the area of ​​the bubbles.

[0097] For example, in some embodiments, bubbles are typically represented in an image as connected regions with small pixel values.

[0098] In this embodiment, the scale of image blurring is limited from the perspective of interfering factors (such as bubbles) to avoid over-processing that leads to data distortion.

[0099] In some embodiments, when correcting the expression of the target connected region using a data correction method, the method further includes the step of: (1) Select at least one blur parameter from the set blur scale to perform blur processing on the first image or the third image to obtain a blurred image after blur processing, wherein the blurred image is the second image or the fourth image; wherein the numerical range of the set blur scale is [0.1,2], and the blur scale is used to define the blur radius (i.e., sigma (radius)) in the blur operation process. For example, in some embodiments, multiple fuzzy parameters can be selected simultaneously to conduct multiple trials to select the smallest fuzzy parameter. Figure 12 An exemplary interface for adjusting fuzzy parameters is shown.

[0100] (2) Identify the number of target connected regions in the blurred image; The connected component detection rules described above can be used for identification.

[0101] (3) Determine whether the quantity belongs to a set quantity range, wherein the set quantity range is defined by the number of gene detection areas and marker wells; For example, in some embodiments, the total number of gene detection areas and marker wells is 20, and the number range is set to 20, that is, the range can specifically contain only one value.

[0102] For example, in some embodiments, the upper limit of the set quantity range is the total number of gene detection areas and marker wells. For instance, when the number of gene detection areas and marker wells is 20, the set quantity range can be 18-20 to reserve a certain tolerance range for some complex experiments or special cases. For example, if the operator fails to perform some chamber experiments due to improper operation, the number of identified target connected regions can be less than the total number of gene detection areas and marker wells.

[0103] If yes, then keep the fuzzy parameters; otherwise, it is recommended to update the fuzzy parameters.

[0104] For example, in some embodiments, the blur parameters can be selected during the blurring process of the first image and the same blur parameters can be saved during the processing of the third image.

[0105] In some embodiments, the image needs to be size normalized. For example, in some embodiments, before S1051, the method further includes: performing pixel expansion on the first image using a bilinear interpolation algorithm.

[0106] In this embodiment, a bilinear interpolation algorithm is used to expand pixels, thereby effectively eliminating interference from low-pixel images (in simple shooting environments) and resolution differences between different devices.

[0107] In some embodiments, S1054 includes: obtaining the G / B channel ratio of at least one pixel, and when the G / B channel ratio is within a preset value range, identifying the pixel as a feature point; When the distance between at least two adjacent feature points is less than the target distance, the feature points are identified as a connected region. The connected component detection rules are used to determine whether the connected component is the target connected component.

[0108] This embodiment can reduce the impact of light source intensity fluctuations in a simplified environment and standardize data under different imaging conditions.

[0109] In some embodiments, the blur size of the first blurring and / or the second blurring does not exceed 10 pixels.

[0110] In this embodiment, by coordinating and limiting the execution steps and degree of blurring, impurities and noise in the blob generated during image capture can be eliminated. For example, when identifying blobs, impurities and noise in the blob can be avoided from causing an area that should be marked as a large blob to be mistakenly marked as "two or more small blob areas".

[0111] By limiting the scale of the Gaussian blur (on the order of 10 pixels), it is made not to affect the area to be identified (2000-3000 pixels).

[0112] It is understood that the object detected in this embodiment is actually the fluorescence intensity of the solution containing RNA after fluorescence amplification. In other words, this embodiment does not directly identify RNA molecules, but rather identifies the fluorescence intensity of the entire chamber, which corresponds to the concentration of amplified RNA, and further corresponds to the RNA content of the original solution.

[0113] Taking the exemplary detection device as an example, it will ultimately produce 18 large circles (6 groups of 3) and 2 small circles (marked at 2 locations) on the image. After identifying all blobs (i.e. connected regions), approximately 20 target connected regions are selected by limiting parameters such as inertia ratio, roundness, area, and convexity.

[0114] Furthermore, it is worth noting that the image detection mechanism based on double blurring processing used in this embodiment is applicable to photos taken in simple environments such as mobile phones. Since the pixel count of mobile phones used by different operators is not fixed, the method provided by this invention can largely eliminate these differences.

[0115] In summary, unlike traditional detection methods, the detection method of this invention actually establishes a three-level quantitative relationship chain: G / B value ↔ fluorescence intensity, fluorescence intensity ↔ RNA concentration (GAPDH / L858R / DEL three-system). This achieves 10 2 -10 6 It features a wide linear range of 10 copies / μL and an ultra-high sensitivity (LOD) of 10 copies / μL. It can also directly output relative quantification results.

[0116] In another aspect, the present invention provides a live cell sampling and detection system, characterized in that it includes: a live cell sampling and detection device as described in any embodiment; a computing device including: one or more processors; and one or more memories coupled to the one or more processors and storing instructions thereon, wherein when the instructions are executed individually or jointly by the one or more processors, the computing device causes the computing device to perform the quantitative detection method described in any embodiment.

[0117] The following is an exemplary description of the above detection method using a specific implementation process: This study used Python code and the OpenCV library for image recognition and blob detection, calculated the median gray value of the detected cavities, and annotated them on the image to achieve quantitative analysis of EGFR mutation expression. The Python code is explained below.

[0118] (1) Library import and variable initialization: Import the OpenCV (cv2) and NumPy (np) libraries, the latter of which is mainly used for array and matrix operations; The color_blob directive sets the color (red) used when drawing detected blobs on an image; The `color_txt` directive sets the text color (black) for use in annotating grayscale values ​​on an image; The src and output directives are the file paths for the input image and the output result, respectively, and need to be specified according to the actual situation.

[0119] (2) Reading and resizing images: The im_org command reads the raw color image (BGR format); The im_grey command reads grayscale images, which facilitates subsequent spot detection; Obtain the original height and width of the image, adjust the image size according to the width so that the image width is 1000 pixels, and adjust the height accordingly based on the original aspect ratio; The INTER_LINEAR_EXACT command performs bilinear interpolation on an image. The principle of bilinear interpolation is as follows: Figure 13 As shown.

[0120] (3) Gaussian blur: Gaussian blurring is applied to both the grayscale image and the original image to reduce noise and smooth the image, facilitating subsequent blob detection.

[0121] (4) Set the Blob detector parameters: Configure the parameters of the Blob detector using the SimpleBlobDetector_Params() directive; The minThreshold and maxThreshold directives set the grayscale threshold range (0 to 255). The filterByArea directive limits the area of ​​the speckle to between 9,000 and 13,000 pixels; The filterByCircularity command filters based on the roundness of the spots, with a minimum roundness of 0.4; The filterByInertia and filterByConvexity instructions require the spots to have a certain inertia ratio and convexity to ensure the regularity of their shape.

[0122] (5) Create a Blob detector and perform blob detection: Create a SimpleBlobDetector object and use the previously set parameters to detect blobs in the blurred image im_pt; key_points contains information about all detected blobs, including their center position, size, and other features.

[0123] (6) Extract the RGB values ​​of the spots and perform calculations: Obtain the center position and radius of each spot; Draw a circular area on the blank mask image to represent the area of ​​the spot; Use np.where(mask == 255) to get the pixel position of the circular region; Extract the RGB values ​​of these pixels from the blurred image im_num; The median G / B of the pixel is calculated and output using value=np.median(intensity_values[1] / intensity_values[0]).

[0124] (7) Label the calculation results on the image: Use the cv2.putText() command to label the calculated ratio value next to the center of each spot.

[0125] (8) Visualized detection results: Use the `cv2.drawKeypoints()` command to draw the detected points on the image in red; use the `cv2.imshow()` command to display the final image result, which includes the detected points and their grayscale values; use the `cv2.imwrite()` command to save the result to the specified path.

[0126] This code implements an image-based blob detection and quantitative analysis method using OpenCV. First, by setting blob detection parameters, blob regions in the image are accurately identified. Then, by extracting the grayscale values ​​within the blob regions and calculating the median, the grayscale intensity of the blobs is quantitatively analyzed. Finally, the detection results are presented visually, allowing researchers to intuitively understand the blob features in the image.

[0127] Figure 14 Three typical test results are shown. Sample 1 is a wild-type sample with no mutation; the LAMP test result for GAPDH is positive, while the LAMP test results for DEL and L858R are negative. Sample 2 is an L858R mutant sample; the LAMP test result for GAPDH and L858R is positive, while the LAMP test result for DEL is negative. Sample 3 is a DEL mutant sample; the LAMP test result for GAPDH and DEL is positive, while the LAMP test result for L858R is negative.

[0128] Figure 15 The fitted lines for RNA concentration and G / B value are shown below. Analysis of the results reveals the following fitted lines: GAPDH primer: y = 0.7157x + 0.2561; DEL primer: y = 0.5918x + 0.3903; L858R primer: y = 0.5001x + 0.4840.

[0129] Example 3 This embodiment provides the clinical sample detection results of the live cell real-time sampling and detection chip device (hereinafter referred to as the chip) of Embodiment 1.

[0130] Primary culture and on-chip experiments were performed on samples from 50 lung cancer patients at Peking University Cancer Hospital. During targeted drug therapy, the expression levels of mutated genes in cells were quantitatively monitored at 0h, 24h, 48h, and 72h. The experimental results were analyzed to explore the changes in mutated gene expression levels during targeted drug therapy. The results are shown in [Table data would be inserted here]. Figure 16 .

[0131] Figure 16 A shows the results of detecting the expression levels of the mutant gene in three clinical samples (wild type, DEL, L858R). Figure 16 B shows a comparison between the chip detection results and the actual clinical classification results, finding that the accuracy rate of chip detection reached over 92%. Figure 16 C and Figure 16 D compared the ROC curves of chip detection with traditional detection methods. Because chip detection uses a completely contactless sample extraction and conversion process, its detection accuracy is significantly higher than that of the traditional LAMP detection method (see...). Figure 16 E). Our research team examined the changes in mutated gene expression levels in each sample over 72 hours during targeted drug treatment and found that 13 samples showed a rebound in expression levels (see...). Figure 16 F-16G). By comparing clinical manifestations, sample 16, which did not rebound, did not experience tumor recurrence after treatment (see F-16G). Figure 16 H-16I). Sample 5, which showed a rebound, experienced tumor recurrence three years after drug treatment (see...). Figure 16 J-16K).

[0132] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A live cell sampling and detection device, characterized in that, The invention includes a rotating microfluidic chip and an amplification detection reagent; the rotating microfluidic chip is composed of an upper electrode (110), a nanoporous membrane (210), a sampling layer (220), a lower electrode (120), and a detection layer (230) assembled from top to bottom. The upper electrode (110), sampling layer (220), lower electrode (120) and detection layer (230) are detachable structures and are connected by a central bearing (240); The nanoporous membrane (210) is disposed within the electrical sampling area (220a) of the sampling layer (220) for continuous sampling of live cells; The detection layer (230) is provided with a gene detection area (230a), and the amplification detection reagent is placed in the gene detection area (230a) for detecting the expression level of genes in cells; The electrical sampling area (220a) and the gene detection area (230a) are connected by an inclined microchannel (220b) on the sampling layer (220); The detection layer (230) is also provided with a first marking hole 230b and a second marking hole 230c for indicating the signal of a positive control or a negative control.

2. The live cell sampling and detection device as described in claim 1, characterized in that, Each of the electrical sampling regions (220a) consists of two cylindrical chambers, one larger than the other, separated by a nanoporous membrane (210); the gene detection region (230a) is a cylindrical chamber.

3. The live cell sampling and detection device as described in claim 2, characterized in that, The lower chamber of the electrical sampling area (220a) and the upper chamber of the gene detection area (230a) are connected by an inclined microchannel (220b).

4. The live cell sampling and detection device as described in claim 1, characterized in that, The primer sequences of the amplification detection reagent are shown in SEQ ID NO.1-14.

5. A method for real-time gene quantification analysis of live cells using the live cell sampling and detection device according to any one of claims 1-4, characterized in that, Includes the following steps: S101: The cells to be tested are seeded on a nanoporous membrane (210), and cell culture medium is filled into the upper chamber of the electrical sampling area (220a) of the live cell sampling and detection device, and buffer solution is filled into the lower chamber of the electrical sampling area (220a). S102: Connect the upper electrode (110) and lower electrode (120) of the live cell sampling and detection device to an external power source, respectively, and apply a pulsed electric field to the cells in the electrical sampling area (220a) to perform cell nano-electric sampling, so that the RNA in the cells enters the lower chamber of the electrical sampling area (220a). S103: Rotate the live cell sampling and detection device so that the RNA-containing solution in the lower chamber of the electrosampling area (220a) enters the gene detection area (230a) through the microchannel (220b); S104: Remove the detection layer (230) and place it at the set temperature to allow the RNA to react with the amplification detection reagent; S105: At least one first image is captured by the live cell sampling and detection device, and the target expression level is calculated from the first image.

6. The method according to claim 5, characterized in that, S105 includes: The first image is corrected using an image correction method, which includes: S1051, Perform a first blurring process on the first image to obtain a second image; S1052, perform grayscale processing on the second image to obtain the third image; S1053, Perform a second blurring process on the third image to obtain a fourth image; S1054, a target connected region is identified from the fourth image using a preset connected region detection rule, wherein the connected region detection rule requires the target connected region to have the following characteristics: (1) the inertia ratio is within a preset inertia ratio range; (2) the pixel scale is within a preset scale range; (3) the area is within a preset area range; and (4) the convexity is within a preset convexity range. The data correction method is used to correct the expression of the target connected region, which includes: S1055, calculate a set of gene expression levels based on the target connected region, wherein the set of gene expression levels includes: a first expression level of at least one of the gene detection regions (230a), and a second expression level of at least one marker well; S1056, Generate correction coefficients based on the standard expression level of the marked aperture and the second expression level; S1057, The target expression level is calculated based on the correction coefficient and the first expression level.

7. The method according to claim 5, characterized in that, The process of correcting the expression of the target connected region using data correction methods also includes the following steps: At least one blur parameter is selected from the set blur scale to blur the first image or the third image to obtain a blurred image after blur processing, wherein the blurred image is the second image or the fourth image; wherein the numerical range of the set blur scale is [0.1,2], and the blur scale is used to define the blur radius in the blur operation process; Identify the number of target connected regions in the blurred image; Determine whether the quantity falls within a predetermined quantity range, which is defined by the number of gene detection areas and marker wells; If yes, then keep the fuzzy parameters; otherwise, it is recommended to update the fuzzy parameters.

8. The detection method based on a live cell sampling and detection device according to claim 7, characterized in that, Before S1051, the method further includes: performing pixel expansion on the first image using a bilinear interpolation algorithm.

9. The detection method based on a live cell sampling and detection device according to claim 7, characterized in that, S1054 includes: Obtain the G / B channel ratio of at least one pixel. When the G / B channel ratio falls within a preset value range, the pixel is identified as a feature point. When the distance between at least two adjacent feature points is less than the target distance, the feature points are identified as a connected region. The connected component detection rules are used to determine whether the connected component is the target connected component; And / or, the blur size of the first blurring process does not exceed 10 pixels.

10. A live cell sampling and detection system, characterized in that, include: The live cell sampling and detection device as described in any one of claims 1-4; Computing devices, including: One or more processors; as well as One or more memories coupled to the one or more processors and storing instructions thereon, which, when executed individually or jointly by the one or more processors, cause the computing device to perform the method of any one of claims 6-9.

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