A method for patch-clamp operation on a cell in a body based on electrode resistance guidance

Abstract

The application discloses a kind of in-vivo cell patch clamp operation method based on electrode resistance guide, steps are as follows: first, the theoretical model of microtubule electrode resistance and the distance between nozzle-cell surface is established, and the key parameters of model are calibrated and verified using finite element simulation;Then, based on the theoretical model combined with transverse scanning, the dynamic three-dimensional positioning of cell is realized;Based on the target cell position information obtained by real-time positioning, a dynamic cell opposite grabbing strategy is proposed, so that the microtubule electrode contacts the cell at the farthest point of cell jumping, thereby maximizing the residence time after contact and promoting the formation of high resistance seal;Finally, based on the above, an electrode resistance guided robotic in-vivo neural cell patch clamp operation process is established.The application improves the degree of automation, positioning accuracy and high resistance seal success rate of in-vivo dynamic cell patch clamp operation.

Description

Technical Field

[0001] This invention belongs to the field of cell-level micromanipulation technology, specifically relating to an in vivo cell patch-clamp manipulation method guided by electrode resistance. Background Technology

[0002] Patch-clamp technology is able to detect picoamperes (10) in a single ion channel. -14 A) Electrical current has become the "gold standard" for studying the function of ion channels in nerve cells in neuroscience. Compared with adherent cells and brain slice neurons, neurons in the brain of living animals are in a physiological state with intact functional connections between cells, making them ideal subjects for neuroscience research. Using in vivo patch-clamp techniques to detect electrophysiological signals in neurons of living animal brains is of great significance for studying the patterns of neuronal activity and the mechanisms of brain function.

[0003] In in vivo patch-clamp manipulation, microtubule electrodes are used to approach and grasp the target nerve cell before performing patch-clamp operations such as sealing and membrane rupture. However, due to the poor light transmittance of the in vivo environment, conventional bright-field microscopy imaging methods struggle to observe the location of in vivo neurons in the cerebral cortex, posing a challenge to the localization of target neurons. Furthermore, even if the animal's head is pre-fixed during in vivo patch-clamp manipulation, the in vivo nerve cell remains pulsating due to physiological disturbances such as heartbeat and respiration, further complicating the grasping of in vivo neurons.

[0004] While existing two-photon microscopy imaging techniques possess a certain degree of tissue penetration, they suffer from drawbacks such as reliance on fluorescence staining, inability to dynamically locate cells, limited imaging depth, and high hardware costs. Therefore, their application in cell localization during in vivo patch-clamp manipulation is significantly limited. Current blind patch-clamp manipulation achieves cell capture through a significant increase in electrode resistance after the microtube contacts the cell. However, this method involves a high degree of randomness in the moment of contact. If the cell is moving away from the microtube after contact, it is highly likely that the two will detach, resulting in cell capture failure and affecting the success rate of cell capture and subsequent patch-clamp operations. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by proposing an in vivo patch-clamp manipulation technique that enables dynamic cell localization and precise grasping without visual dependence. This technique is of great significance for improving the success rate of in vivo patch-clamp manipulation and promoting its application in in vivo brain science research.

[0006] This invention is achieved through the following technical solution: To solve the above problems, the present invention adopts the following technical solution: A method for in vivo cell patch-clamp manipulation guided by electrode resistance, the method comprising the following steps: S1: Establish a theoretical model of the resistance of the microtube electrode and the distance between the tube opening and the cell surface, and use the finite element method to calibrate and verify the key parameters of the model.

[0007] A quantitative model of the distance relationship between the tube opening and the cell surface was established for real-time proximity estimation. Specifically, the total resistance of the microtube electrode was expressed as the series resistance R within the tube. s The gap resistance R formed between the microtubule electrode opening and the cell to be approached a The sum of series.

[0008] The gap resistance model introduces a dimensionless correction parameter. k 1 , k 2 and n These are used to capture electric field line bending, boundary condition modification, and nonlinear dependence on distance under non-ideal conditions. The additional physical displacement caused by cell membrane deformation resulting from the combined effects of microtubule injection pressure, cell membrane Young's modulus, and the distance between the tube opening and the cell surface is also considered. Combining these modifications to the model yields a complete formula for the total resistance as a function of the tube opening-cell surface distance.

[0009] The finite element method was used to simulate the process of a microtubular electrode gradually approaching a target cell. The inner and outer radii and cone angle of the microtubular electrode were measured by scanning electron microscopy and imported into the finite element model. In the simulation, the neuronal cell was approximated as an elastic body with an insulating surface. The total resistance was calculated and the analytically obtained series resistance was subtracted to obtain the gap resistance.

[0010] The correction parameters and distance correction function were solved by fitting using the least squares method, and based on this, the inflection point of the resistance when the microtubule electrode approaches the cell surface and the corresponding distance change with the Young's modulus of the cell membrane and the injection pressure inside the microtubule electrode were obtained.

[0011] S2: Dynamic three-dimensional localization of cells is achieved by combining a distance model with lateral scanning.

[0012] When the microtubule electrode approaches the target area, the axial distance between the microtubule electrode opening and the cell surface is first estimated using the distance model based on the real-time detected changes in electrode resistance. Then, the microtubule electrode is controlled to perform a lateral scan within a predetermined plane, acquiring resistance response data at different scanning positions. Since the presence of the target cell affects the gap resistance, the resistance changes at different lateral positions differ; therefore, the position of the target cell within the plane can be determined based on the resistance distribution characteristics obtained during the scan.

[0013] By combining the axial distance information output by the distance model with the planar position information obtained by the transverse scan, the spatial position of the target cell relative to the microtube electrode can be reconstructed, thereby achieving dynamic three-dimensional positioning of the target cell.

[0014] S3: Based on the real-time positioning of the target cell, a dynamic cell-facing grasping strategy is proposed to make the microtube electrode contact the cell at the farthest point of cell jumping, maximizing the dwell time after contact and promoting the formation of high-resistance seal.

[0015] For dynamically moving cells in living tissue, a dynamic cell-facing grasping strategy based on motion phase detection is proposed. By analyzing the relative motion relationship between the microtube electrode orifice and the target cell under different phase states and comparing it with the effective residence time after contact, it is determined that contact should be made with the target phase that is farthest from the microtube electrode orifice during the distance measurement in the cell movement cycle, so as to maximize the residence time after contact and promote the formation of high-resistance seal.

[0016] S4: Based on the above work, a robotic in vivo neural cell patch-clamp operation procedure guided by electrode resistance was established.

[0017] Using the resistance at the microtube electrode orifice as a feedback signal in the operation process, the microtube electrode resistance-distance theoretical model constructed and calibrated in step S1, the dynamic three-dimensional positioning of cells in step S2, and the dynamic cell opposing grasping strategy in step S3 are integrated into the patch clamp control system to construct an automated robotic patch clamp operation process. This process controls the microtube electrode to complete the real-time positioning, phase-synchronous contact, sealing, and recording operations of the target cell, and achieves whole-cell recording and cell-attached recording.

[0018] Furthermore, the series resistance model is expressed as: ; Where R s Indicates series resistance. r i It is the radius of the internal opening of the microtube electrode. θ It is the cone angle of the inner wall of the microtube electrode orifice. z The length of the microtube electrode cone. ρ It is the conductivity of the solution inside the microtube electrode.

[0019] Furthermore, in the experimental setup, when z >> r i When the series resistance is such that: ; Furthermore, in the gap resistance model, based on the gap resistance expression when the cell is located at infinity and the gap resistance expression when the distance between the tube opening and the cell surface is equal to the opening size of the microtube electrode tube, a composite model containing three dimensionless correction parameters is introduced to correct for non-ideal effects and derive a transitional gap resistance model expression: ; in k 1 , k 2 and n R is a dimensionless correction parameter. a For gap resistance, ρ The conductivity of the solution inside the microtube electrode. r i and r o These are the inner and outer radii of the microtube electrode opening, respectively. d This represents the axial distance between the microtubule electrode opening and the cell surface. f (·) is the distance correction function. E It is the cell's Young's modulus. P It is the pressure applied at the opening of the microtube electrode.

[0020] The gap resistance model includes a distance correction function to correct for additional distance changes caused by cell membrane surface deformation as the microtubule electrode approaches the cell. This distance correction function considers the pressure applied by the microtubule electrode, the cell's Young's modulus, and the initial distance between the microtubule electrode opening and the cell surface.

[0021] In the finite element simulation of microtubular electrodes approaching cells, the cell is simplified as an elastic, flattened ellipse with an insulated surface. The microtubular electrode wall is set as the insulating boundary. By measuring the change in total resistance at different approximation distances and calculating the theoretical gap series resistance, gap resistance data is extracted. The obtained gap resistance data is then fitted to a composite model, and all unknown correction coefficients and correction functions in the model are calculated.

[0022] The calculation process for the correction parameters and distance correction function in the gap resistance model is as follows: S1.1: Construct the geometric model of the microtube electrode nozzle in finite element software; S1.2: Use scanning electron microscope and optical microscope to physically image multiple real microtube electrodes, quantitatively measure their internal opening radius, external opening radius and cone angle, and substitute the measured real geometric parameters into the finite element model; S1.3: Run finite element software to perform simulation, calculate and extract the total resistance change data of the microtube electrode orifice at different approximation distances above the cell; S1.4: Calculate the fixed series resistance using theoretical formulas and subtract it from the total resistance obtained from the simulation to obtain the gap resistance data that varies with distance; S1.5: Apply the least squares fitting algorithm to fit the gap resistance data into the gap resistance calculation formula, and use this to calculate the correction parameters and distance correction function.

[0023] In step S2, the distance information between the microtube electrode and the target cell is obtained based on the distance theory model, and the resistance response changes of the target cell at different scanning positions are obtained by combining the lateral scanning. Based on this, the planar position and height information of the target cell are determined, thereby achieving dynamic three-dimensional positioning of the target cell. Specifically, as follows: The microtubule electrode first moves forward axially within the target region, and the resistance change at the microtubule electrode orifice is detected in real time. The real-time resistance value is converted into an estimated distance from the microtubule electrode orifice to the cell surface, thus obtaining the axial position information of the cell relative to the microtubule electrode. When the estimated distance indicates that the microtubule electrode has entered the vicinity of the cell, the microtubule electrode is controlled to perform a scanning motion in the lateral direction, and the resistance signal at each position is continuously acquired during the scanning process. Based on the resistance response at different lateral positions, the position coordinates of the target cell in the lateral plane are determined. Combining the lateral position coordinates with the estimated axial distance, the three-dimensional position of the target cell relative to the microtubule electrode is obtained.

[0024] For cells in a dynamic state, resistance detection, distance estimation, and lateral scanning localization are repeatedly performed to continuously update the cell's three-dimensional coordinates and achieve dynamic tracking and localization.

[0025] Furthermore, the opposing grasping strategy in step S3 is to determine a sealing window period from the quasi-periodic cell movement, that is, the stage when the cell moves toward the microtube electrode and the acceleration is directed toward the microtube electrode, and to synchronize the contact between the microtube electrode opening and the cell membrane to this window period.

[0026] The optimal capture phase is selected within the sealing window period for cell capture. The optimal capture phase is the phase corresponding to the cell moving to the farthest position relative to the measured microtube electrode opening during dynamic tracking and positioning. The influence of different capture strategies on high-resistance sealing formation is evaluated by comparing the velocity direction, acceleration direction, and dwell time of different capture strategies at the moment of contact and after contact.

[0027] Compared with existing in vivo patch-clamp techniques, the advantages of this invention are: 1. The quantitative mathematical model for the distance between the microtubule opening and the cell surface established in this invention comprehensively considers the geometric parameters of the microtubule, the bending effect of the electric field lines, and the combined effects of internal injection pressure and Young's modulus of the cell membrane, and performs high-precision calibration using the finite element method (FEM). This achieves real-time, high-precision proximity estimation of living cells without visual dependence.

[0028] 2. The dynamic cell-facing grasping strategy proposed in this invention significantly extends the attachment dwell time after contact by accurately calculating the position (FP) farthest from the microtube electrode during the cell movement cycle and making synchronous contact, and can maintain the physical integrity of the cell membrane to a certain extent.

[0029] 3. The automated patch-clamp system designed in this invention, which integrates a quantitative resistance-distance model and a dynamic cell-to-cell grasping strategy, improves the success rate of high-resistance sealing in live patch-clamp operations to 81.8%. Compared with the results of existing robotic blind patch-clamp operations (51% and 24%), its high-resistance sealing success rate is increased by 30.8 percentage points and 57.8 percentage points, respectively, indicating that this invention can effectively improve the efficiency of acquiring neuronal electrical signals.

[0030] In summary, this invention addresses the problem of dynamic neuronal movement caused by physiological activities in living animals, which makes it difficult for microtubule electrodes to effectively seal with target neurons in a moving state. It proposes an in vivo cell patch-clamp manipulation method guided by electrode resistance. This method establishes a quantitative relationship model between the resistance of the microtubule electrode and the distance between the microtubule opening and the cell surface, enabling quantitative estimation of cell proximity without visual dependence. Based on the established resistance-distance relationship, dynamic three-dimensional localization of the target cell is achieved by combining lateral scanning of the microtubule electrode. Furthermore, after identifying the target cell, the system executes a dynamic cell-facing grasping strategy based on cell motion phase detection. The microtubule electrode contacts the position furthest from the microtubule electrode at the time of detection during the cell's motion cycle to maximize the dwell time after contact and promote high-resistance seal formation. Using this method improves the automation level and high-resistance seal success rate of in vivo dynamic cell patch-clamp manipulation, and enables whole-cell recording and cell-attachment recording. Attached Figure Description

[0031] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the equivalent circuit of the microtube electrode resistor of the present invention; Figure 3 This is a finite element simulation result of the microtube electrode of the present invention applying internal pressure and approaching a cell model; Figure 4 This is a schematic diagram of the dynamic cell-facing grasping strategy of the present invention; Figure 5 This is a flowchart of the robotic in vivo patch clamp operation of the present invention; Figure 6 The electrophysiological signals were obtained in the mouse brain using the robotic patch-clamp system of this invention. Detailed Implementation

[0032] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the invention to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0033] like Figures 1 to 6 As shown, this invention discloses an embodiment of an in vivo cell patch-clamp manipulation method based on electrode resistance guidance, primarily for experiments on 6-week-old C57BL / 6J mice, specifically including the following steps: S1: Establish a theoretical relationship model between the resistance of the microtube electrode and the distance between the tube opening and the cell surface, and use the finite element method to calibrate and verify the key parameters of the model.

[0034] First, it is determined that the resistance of the microtube electrode mainly originates from the resistance inside the microtube electrode and the resistance generated by the exchange of ions with extracellular fluid at the microtube electrode opening. Therefore, the overall resistance of the microtube electrode can be expressed as a series resistance R. s and gap resistance R a The sum of the series resistance R. s Determined by the geometry of the microtube electrode, it can be expressed as:

[0035] in r i It is the radius of the internal opening of the microtube electrode. θ The cone angle of the inner wall of the microtube electrode orifice. z The length of the microtube electrode cone. ρ This refers to the conductivity of the solution inside the microtube electrode. In the experimental setup, z >> r i Therefore, the expression can be approximated as:

[0036] And the gap resistance R a This describes the resistance encountered by ions flowing at the inlet of a microtubule electrode. Since the distance between the microtubule electrode and the cell constantly changes, the physical model can be divided into a distal approximation state and a proximal approach state. When the microtubule electrode is extremely far from the cell, it can be considered that the inlet of the microtubule electrode faces an unbounded conductive plane. The gap resistance at this point can be expressed as:

[0037] As the microtubule electrode opening gets closer to the cell, the distance between the opening and the cell surface decreases. d Reduced to the inner diameter of the pipe opening r i If the same applies, the tiny gap between the electrode port and the cell surface needs to be modeled as a hollow cylinder filled with a conductive solution, with its inner and outer diameters corresponding to the inner and outer radii of the electrode port, respectively. r i and r o And relative to the radius r The differential resistance is:

[0038] By integrating this differential equation, the theoretical gap resistance is obtained as follows:

[0039] Secondly, considering that in actual operation, the microtube electrode is usually located at an intermediate distance between two limiting states, the local potential and current distribution will change within this range, thus causing deviations in the actual gap resistance. To correct these non-ideal effects, a set of three dimensionless correction parameters is introduced. k 1 , k 2 and n A composite model was constructed. The parameters were used to capture the fieldline bending effect, the modification of the boundary conditions, and the nonlinear dependence of the gap resistance on the orifice-cell surface distance, respectively. Therefore, the distance-dependent composite gap resistance was corrected as follows:

[0040] Furthermore, a positive pressure is applied in the in vivo environment to prevent blockage of the electrode port. When the microtubule electrode port approaches the cell membrane, hydrodynamic forces also cause deformation of the cell membrane, thus introducing an additional physical displacement Δ. d This displacement depends on the injection pressure. P Young's modulus of cell membrane E and the actual distance between the tube opening and the cell surface d :

[0041] Finally, by combining the above optimizations and corrections to the resistance model, a complete picture of the tube opening-cell surface distance is obtained. d Functions:

[0042] This allows for a quantitative estimate of the proximity of cells to the microtubule electrode opening.

[0043] The key parameters of the model were calibrated and verified using the finite element method as follows: Since the resistance of the microtubule electrode increases as the electrode opening approaches the cell, this process involves electrochemical effects and fluid-structure interaction. This invention employs finite element method simulation to model the process of the microtubule electrode approaching the cell, thereby calibrating the parameters in the theoretical model. The specific process is as follows: (1) Construct the geometric model of the microtube electrode nozzle in the finite element software COMSOL Multiphysics; (2) Use scanning electron microscope and optical microscope to physically image multiple real microtube electrodes and quantitatively measure their internal opening radius, external opening radius and cone angle; (3) Substitute the actual geometric parameters obtained from the above experiments into the finite element model; (4) Place the microtubule electrode above the deformable surface representing the cell, simplify the cell into an elastic flattened ellipsoid with an insulated surface, and set the wall of the microtubule electrode as an insulated boundary. (5) Run finite element simulation to calculate and extract the total resistance variation data of the microtube electrode orifice at different approximation distances; (6) Calculate the fixed series resistance using theoretical formulas and subtract it from the total resistance obtained from the simulation to extract the gap resistance data that varies with distance. (7) Apply the least squares fitting algorithm to fit the extracted gap resistance data into the theoretically proposed composite model, and calculate all unknown correction coefficients and distance correction functions in the model.

[0044] S2: Dynamic three-dimensional positioning of cells based on a distance model combined with lateral scanning. The distance information between the microtube electrode opening and the target cell is obtained based on the aforementioned distance theory model. Combined with lateral scanning, the resistance response changes of the target cell at different scanning positions are obtained. Based on this, the planar position and height information of the target cell are determined, thereby achieving dynamic three-dimensional positioning of the target cell.

[0045] In this embodiment, step S2 includes the following process: the microtubule electrode first moves axially forward within the target brain region, and the change in orifice resistance is detected in real time; based on the orifice resistance-cell surface distance model established in step S1, the real-time resistance value is converted into an estimated distance from the microtubule electrode orifice to the cell surface to obtain the axial position information of the cell relative to the microtubule electrode orifice. When the estimated distance indicates that the microtubule electrode orifice has entered the vicinity of the cell, the microtubule electrode is controlled to perform a lateral scanning motion, and resistance signals at various positions are continuously acquired during the scanning process. Based on the resistance response at different lateral positions, the position coordinates of the target cell in the lateral plane are determined. Combining the lateral position coordinates with the estimated axial distance, the three-dimensional position of the target cell relative to the microtubule electrode orifice is obtained. For cells in a dynamic state, resistance detection, distance estimation, and lateral scanning positioning are repeated to continuously update the three-dimensional coordinates of the cell and achieve dynamic tracking and positioning.

[0046] S3: Based on the real-time positioning of the target cell, a dynamic cell-facing grasping strategy is proposed.

[0047] In this embodiment, a counter-grabbing strategy is proposed. First, a sealing window period is determined from quasi-periodic cell movement, that is, the stage when the cell moves towards the microtubule electrode opening with acceleration directed towards the microtubule electrode opening. Then, the contact between the microtubule electrode opening and the cell membrane is synchronized to this window period to maximize the residence time after contact and reduce the possibility of immediate separation.

[0048] The optimal capture phase was then established, which is the farthest position (FP) of the cell relative to the measured microtubule electrode orifice during dynamic tracking and positioning. At capture, the microtubule electrode orifice moves to this position to make contact with the cell, at which point the cell's instantaneous velocity is close to zero and its subsequent acceleration points towards the microtubule electrode. Three other representative capture phases were selected as controls: closest position (NP), when the cell is closest to the microtubule electrode orifice; furthest position (AP), when the cell is furthest from the microtubule electrode orifice; and inward position (TP), when the cell moves towards the microtubule electrode orifice.

[0049] Furthermore, the displacement of the cell relative to the microtube electrode orifice was approximated as a sine curve, and an idealized sinusoidal model was introduced to compare the qualitative differences among the four phases. Based on the sinusoidal model, kinematic analyses were performed on the velocity direction, acceleration direction, and dwell time at the instant of contact and after contact for each of the four strategies. Phase capture experiments were conducted in anesthetized mice for the four phases, and the sealing resistance of each experimental group was statistically analyzed and compared. The experiments showed that the sealing resistance at the farthest position (FP) phase was significantly higher than that at the nearest position (NP), inward phase (TP), or farthest phase (AP) contact, approximately 55 times, 12 times, and 4 times that of NP, AP, and TP, respectively, demonstrating the effectiveness of the capture strategy proposed in this embodiment.

[0050] S4: An electrode resistance-guided robotic patch-clamp operation procedure for in vivo neural cells was established.

[0051] Using the microtube electrode orifice resistance as feedback in the operation process, the robotic in vivo patch-clamp operation procedure integrating orifice resistance-cell surface distance model, cell 3D localization, and opposing grasping strategy is as follows: (1) The microtube electrode is first lowered at a speed of 10 μm / s, and then stopped after reaching the target depth of 50 μm. Then it performs low-speed cell search at a speed of 1 μm / s. (2) Continuously monitor the resistance fluctuations of the microtube electrodes. When the system detects quasi-periodic resistance fluctuations, it determines that a potential cell has been encountered. (3) Control the microtube electrode to perform three consecutive 1μm step-down movements, record the resistance change generated by each descent, and perform online parameter identification to calculate the unknown geometric parameters of the current microtube electrode; (4) Using the identified geometric parameters, the distance between the microtube electrode opening and the cell is calculated and estimated in real time, and the microtube electrode is started to perform lateral scanning to obtain the dynamic three-dimensional positioning of the cell. (5) After obtaining the cell position, the opposing grasping strategy is initiated to complete the precise contact between the microtube electrode port and the cell and form a high-resistance seal; (6) Identify targets and avoid obstacles by distinguishing the characteristics of resistance waveforms: if the resistance trajectory has small, rapid fluctuations (ripples) superimposed on large oscillations, it is determined that a blood vessel has been encountered; if the waveform is smooth and stable, it is determined that a neuron has been encountered. (7) After successful sealing, physiological indicators such as resting membrane potential, membrane resistance, membrane capacitance, discharge activity and leakage current baseline of neurons were measured and recorded in whole-cell recording mode and cell-attachment recording mode, respectively.

[0052] According to the method of this invention, a total of 80 experiments were conducted on 8 anesthetized mice. The mice were divided into two experimental groups: one for superficial brain regions (<500 μm) and the other for deeper brain regions (>500 μm). Each group was assigned 40 microtubule electrodes for high-resistance sealing, and the success rate was statistically analyzed. Ten microtubule electrodes experienced orifice blockage (i.e., excessively high initial resistance or encountering blood vessels). After ruling out electrode blockage, the method of this invention successfully detected cells in 94.3% (66 / 70) of the experiments. With successful cell detection, the robotic system of this invention achieved a high-resistance sealing success rate of 81.8% (54 / 66), which is 30.8 percentage points and 57.8 percentage points higher than the existing robotic blind patch-clamp operations (51% and 24%), respectively, representing relative improvements of 60.4% and 240.8%. In addition, the final measured average sealing resistance was 4.25±2.77 GΩ, the average sealing duration was 33±7 minutes, and all successfully formed high-resistance seals were maintained stably for more than 20 minutes.

[0053] The present invention has been described in detail above through embodiments, but the content described is only an exemplary embodiment of the present invention and should not be considered as limiting the scope of the present invention. The scope of protection of the present invention is defined by the claims. Any technical solutions designed by those skilled in the art using the technical solutions described in the present invention, or similar technical solutions designed by those skilled in the art under the inspiration of the technical solutions of the present invention, within the substance and scope of protection of the present invention, to achieve the above-mentioned technical effects, or equivalent changes and improvements made to the scope of the application, should still fall within the patent protection scope of the present invention. It should be noted that, for clarity, descriptions of some components and processes that are not directly and obviously related to the scope of protection of the present invention but are known to those skilled in the art have been omitted in the description of the present invention.

Claims

1. A method for in vivo cell patch-clamp manipulation based on electrode resistance guidance, characterized in that, Includes the following steps: S1: Establish a theoretical model of the microtube electrode resistance and the distance between the tube opening and the cell surface, and use the finite element method to calibrate and verify the key parameters of the model; S2: Based on the established theoretical model and combined with lateral scanning, the target cells are dynamically located in three dimensions; S3: Based on the target cell location information obtained from real-time positioning, the microtube electrode is controlled to use a dynamic cell-facing grasping strategy to contact the target cell, so as to prolong the dwell time after contact and promote the formation of high-resistance seal. S4: Based on the distance theory model, dynamic three-dimensional positioning, and dynamic cell-to-cell grasping strategy, execute the robotic in vivo neural cell patch-clamp operation process guided by electrode resistance.

2. The method according to claim 1, characterized in that, In the theoretical model of microtube electrode resistance constructed in step S1, the microtube electrode resistance is expressed as the sum of the series resistance inside the microtube electrode and the gap resistance formed between the microtube electrode opening and the cell to be approached.

3. The method according to claim 2, characterized in that, The series resistance model is expressed as: ； Where R s Indicates series resistance. r i It is the radius of the internal opening of the microtube electrode. θ It is the cone angle of the inner wall of the microtube electrode orifice. z The length of the microtube electrode cone. ρ It is the conductivity of the solution inside the microtube electrode; In the experimental setup, when z >> r i When the series resistance is such that: 。 4. The method according to claim 1, characterized in that, The gap resistance model is expressed as: ； in k 1 , k 2 and n R is a dimensionless correction parameter. a For gap resistance, ρ The conductivity of the solution inside the microtube electrode. r i and r o These are the inner and outer radii of the microtube electrode opening, respectively. d This is the axial distance from the microtubule electrode opening to the cell. f (·) is the distance correction function. E It is the cell's Young's modulus. P It is the pressure applied at the opening of the microtube electrode.

5. The method according to claim 4, characterized in that, The calculation process for the correction parameters and distance correction function in the gap resistance model is as follows: S1.1: Construct the geometric model of the microtube electrode nozzle in finite element software; S1.2: Use scanning electron microscope and optical microscope to physically image multiple real microtube electrodes, quantitatively measure the real geometric parameters of the microtube electrodes and substitute them into the finite element model; S1.3: Run finite element software to perform simulation, calculate and extract the total resistance change data of the microtube electrode orifice at different approximation distances above the cell; S1.4: Calculate the fixed series resistance using theoretical formulas and subtract it from the total resistance obtained from the simulation to obtain the gap resistance data that varies with distance; S1.5: Apply the least squares fitting algorithm to fit the gap resistance data into the gap resistance calculation formula, and use this to calculate the correction parameters and distance correction function.

6. The method according to claim 5, characterized in that, In finite element simulation, cells are simplified as elastic flattened ellipsoids with insulating surfaces, and the walls of microtube electrodes are set as insulating boundaries.

7. The method according to claim 1, characterized in that, The specific steps of step S2 are as follows: The microtube electrode first moves forward along the axis in the target area, and the change in the resistance at the tube opening is detected in real time. The real-time resistance value is converted into an estimated distance from the microtube electrode opening to the cell surface, thereby obtaining the axial position information of the cell relative to the microtube electrode. Once the distance estimate indicates that the microtubule electrode has entered the vicinity of the cell, the microtubule electrode is controlled to perform a scanning motion in the lateral direction, and resistance signals at each position are continuously acquired during the scanning process. Based on the resistance response at different lateral positions, the position coordinates of the target cell in the lateral plane are determined. Combining the lateral position coordinates with the axial distance estimate, the three-dimensional position of the target cell relative to the microtubule electrode is obtained.

8. The method according to claim 7, characterized in that, For cells in a dynamic state, resistance detection, distance estimation, and lateral scanning localization are repeatedly performed to continuously update the cell's three-dimensional coordinates and achieve dynamic tracking and localization.

9. The method according to claim 8, characterized in that, The opposing grasping strategy in step S3 is to determine the stage in quasi-periodic cell movement where the cell moves toward the microtube electrode and the acceleration is directed toward the microtube electrode as the sealing window period, and to synchronize the contact between the microtube electrode opening and the cell membrane to this window period.

10. The method according to claim 9, characterized in that, The optimal capture phase is selected during the sealing window period for cell capture. The optimal capture phase is the phase corresponding to the cell moving to the farthest position relative to the measuring microtube electrode opening during dynamic tracking and positioning.

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