Multi-parameter micro-nano sensing chip for detecting neural network

Abstract

The invention discloses a multi-parameter micro-nano sensing chip for detecting a neural network. The multi-parameter micro-nano sensing chip comprises an ECIS interdigital electrode, an MEA multi-channel planar electrode, an electrochemical SWNT film array and a microfluidic structure. Three-dimensional heterogeneous integration is achieved through vertical stacking and partition cooperation, an ECIS interdigital electrode and an MEA chip share a plane base, an electrode layer is isolated through a nanoscale insulating layer, electrical signal crosstalk is avoided, meanwhile, the space of the chip is utilized to the maximum extent, meanwhile, a micro-fluidic cavity is divided into three corresponding detection areas, and the detection accuracy of the micro-fluidic cavity is improved. Therefore, signal detection of the in-vitro neural network in different states can be completed. According to the invention, multi-mode signal detection can be carried out on the neural network cultured by the same iPSCs neuron, and the ECIS interdigital electrode, the MEA multi-channel planar electrode and the electrochemical SWNT thin film array are combined together by using the micro-fluidic chip, so that the problems of single function, data splitting, insufficient sensitivity, difficulty in long-term stability and the like of the traditional neural chip are solved.

Description

Technical Field

[0001] The invention relates to the field of biotechnology, and in particular to a multi-parameter micro-nano sensor chip for detecting neuron networks. Background Art

[0002] Neurons are the core functional units of the brain and nervous system, and are mainly responsible for the reception, integration and transmission of information. The pathological characteristics of neurodegenerative diseases are often the dysfunction or death of neurons. Severe neurological diseases include Alzheimer's disease, Parkinson's disease, Alzheimer's disease and depression, and there are no specific drugs that can significantly cure or reverse the disease, so it is necessary to design new neural models that can perform high-throughput drug screening. In recent years, the relevant theories and technologies of iPSCs-derived neurons have developed rapidly, providing new ideas and approaches for modeling nervous system diseases, studying molecular mechanisms, cell therapy and drug screening systems. iPSCs-derived neurons from AD patients have the characteristics and advantages of personalized characterization and prediction of changes in patient brain proteomics, neuropathology and cognitive levels, and can be used as a new method for establishing in vitro neuronal models. However, this strategy is limited in two aspects: 1) The specific differentiation of iPSCs into neurons involves complex operating steps and multiple reagents, which require strict quality control. Minor differences in the culture process and microenvironment will accumulate over time, resulting in huge differences in differentiation success rate and survival rate, making the detection parameters obtained from different batches of iPSCs-derived neurons less reproducible and consistent; 2) The current detection methods are relatively independent for the various physiological changes of iPSCs-derived neurons from AD patients from specific differentiation to maturity, and can only obtain certain aspects of the pathological characteristics of iPSCs-derived neurons.

[0003] First, the detection of cell proliferation and morphological changes during the specific differentiation of iPSCs-derived neurons: the use of a microscope cannot observe the cell growth process for a long time, and the combination of a living cell workstation cannot quantitatively characterize cell proliferation and morphological changes. Immunohistochemistry detection is cumbersome and can only reflect the static state of cell growth at a certain moment. Secondly, the detection of electrophysiological activities and network characteristics of iPSCs-derived neurons after maturation: researchers mostly use patch clamp technology to detect and analyze voltage-gated sodium channels and potassium channels on the cell membrane of iPSCs-derived neurons. However, patch clamp can only detect the characteristics of ion channels on single cell membranes, with high experimental requirements and poor stability, and it is impossible to observe the clustering and synchronization characteristics of neural networks throughout the process and dynamically. Finally, the detection of disease-related proteins in the metabolic microenvironment of iPSCs-derived neurons: the content of secretory proteins in the cell culture microenvironment is extremely low. The current detection methods are limited to spectral analysis, liquid-mass spectrometry chromatography, enzyme-linked immunosorbent assay, etc., which are cumbersome and expensive, and cannot reflect the metabolic characteristics of iPSCs-derived neurons in a long-term and dynamic manner. Summary of the invention

[0004] Object of the Invention: The present invention provides a multi-parameter micro-nano sensing chip for detecting neural networks, which can integrate a cell impedance sensor, a microelectrode array, an electrochemical biosensor and a microfluidic chip into one body, achieve refined and specific differentiation and long-term culture of iPSCs-derived neurons, and can specifically and comprehensively characterize the pathological state of neurons in the brains of AD patients. By analyzing the variation laws of the above detection data sets, the differences from the normal group and the correlations with the disease severity can be obtained, pathological features can be extracted, and an AD personalized comprehensive in vitro neural network model can be established.

[0005] Technical Solution: A multi-parameter micro-nano sensing chip for detecting neural networks according to the present invention includes: an ECIS interdigitated electrode, an MEA multi-channel planar electrode, an electrochemical SWNT thin film array and a microfluidic structure; the microfluidic structure uses lithography technology to construct microchannels and culture chambers, the ECIS interdigitated electrode is embedded at the bottom of the chamber, and the attachment, proliferation and morphological changes of cells are monitored in real time by applying a low-frequency alternating current signal; the MEA multi-channel planar electrode is arranged at the bottom of the same chamber with a high-density platinum electrode layout for synchronously recording the electrophysiological activities of neural networks; the electrochemical SWNT thin film array is distributed in an independent detection area.

[0006] Further, the ECIS interdigitated electrode is composed of bead-shaped electrode pairs connected by a common end, forming a two-by-two cross electrode array structure. The total length of the strip electrodes is 5 mm, and the diameter of each bead is about 90 μm. The reference electrodes of the ECIS interdigitated electrode are located at the upper and lower ends.

[0007] Further, the MEA multi-channel planar electrode includes 16 working electrodes arranged in a 4×4 square layout and a trapezoidal grounding electrode with an area of 2 mm2. The diameter of a single planar electrode point is 30 μm, and the center distance between electrode points is 200 μm.

[0008] Further, gold electrodes are deposited on both sides of the electrochemical SWNT thin film array as source and drain electrodes to form a SWNT channel. The channel is 200 μm long and 1.6 mm wide, and an insulating additional layer is deposited between channels.

[0009] Further, the electrochemical SWNT thin film array is modified by nanomaterials, and multiple disease-related proteins are detected by amperometry or impedance spectroscopy.

[0010] Further, the microfluidic structure has an inlet and an outlet, which are respectively connected to a micro-injection pump and a waste liquid cylinder.

[0011] Further, the microfluidic structure serves as a core carrier for directionally controlling the culture, differentiation and microenvironment regulation of iPSCs-derived neurons.

[0012] Furthermore, in the spatial layout of the multi-parameter micro-nano sensing chip, physical separation of different sensors is achieved through vertical stacking and isolation by an insulating layer. At the same time, a microfluidic valve is used to partition and control the cell migration path to avoid signal interference and cross-contamination.

[0013] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: The present invention realizes three-dimensional heterogeneous integration through vertical stacking and partitioning in coordination. The ECIS interdigitated electrode and the MEA chip share a planar base, but the electrode layers are isolated by a nanoscale insulating layer to avoid electrical signal crosstalk. At the same time, the chip space is maximally utilized. At the same time, the microfluidic chamber is divided into corresponding regions for three types of detections to complete signal detection of the in vitro neural network in different states; the present invention can perform multi-modal signal detection on the neural network cultured from the same iPSCs neurons. By using a microfluidic chip to combine the ECIS interdigitated electrode, the MEA multi-channel planar electrode, and the electrochemical SWNT thin film array, the problems of single function, data fragmentation, insufficient sensitivity, and difficulty in long-term stability of traditional neural chips are solved. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the chip structure of the present invention. Detailed Embodiments

[0015] As Figure 1 shown, a multi-parameter micro-nano sensing chip for detecting a neural network includes: an ECIS interdigitated electrode, an MEA multi-channel planar electrode, an electrochemical SWNT thin film array, and a microfluidic structure. The microfluidic structure is bonded above the substrate through PDMS soft lithography technology to form a fluid network: the main culture chamber covers the ECIS and MEA regions to provide a long-term culture environment for neurons; the channel generates a neurotrophic factor gradient through hydrodynamic effects to simulate the in vivo microenvironment to guide axonal directional growth; the latter half of the SWNT detection chamber is connected to the waste liquid outlet, and the laminar flow effect is used to directionally enrich the pathological proteins secreted by cells to the sensing interface, significantly improving the detection sensitivity. The linkage between the SWNT array and the microfluidics is reflected in the directional transport and in-situ detection of metabolites: the microfluidic channel transports the secreted molecules to the surface of the SWNT thin film through fluid shear force for the determination of the Aβ protein concentration.

[0016] The multi-parameter micro-nano sensing chip includes two groups of interdigitated electrodes and two reference electrodes. The interdigitated electrodes are composed of round bead-shaped electrode pairs connected by a common end, forming an electrode array structure with pairwise intersections. The total length of the strip electrodes is 5 mm, and the diameter of each round bead is about 90 μm. The reference electrodes are located at the upper and lower ends. When the interdigitated electrodes are covered with cells and a small stimulating current or voltage is applied, the impedance of the cells to the current can be detected. When the number of cells on the electrode surface increases or the cell morphology area increases, the detected impedance value will also increase.

[0017] The MEA multi-channel planar electrode array consists of 16 working electrodes arranged in a 4×4 square pattern and a trapezoidal grounding electrode with an area of 2 mm 2 . The diameter of a single planar electrode point is 30 μm, and the center-to-center spacing of the electrode points is 200 μm. The equivalent circuit model for the MEA planar electrode to detect extracellular neuron potentials can be composed of the classical Hodgkin-Huxley model of cell action potentials and the double-layer model of the electrode-electrolyte interface. When the seal resistance formed between the neuron and the electrode point surface is high enough, the leakage current can be neglected, and the spike potential waveform corresponding to the cell action potential can be recorded.

[0018] The electrochemical SWNT thin film array is prepared by the Langmuir-Blodgett (LB) transfer method to fabricate a densely arranged SWNT thin film. SWNTs and poly[(m-phenylene ethynylene)-co-(2,5-dioctyloxy-p-phenylene ethynylene)] (PmPV) are suspended in dichloroethane (DCE) solution and ultrasonicated, and then repeatedly compressed at the water-air interface on the surface of the LB deposition tank filled with deionized water. The substrate is slowly pulled up and transferred to the micro-nano sensing chip, and annealed to remove the residual PmPV. Gold electrodes are deposited on both sides of the SWNT thin film as the source and drain electrodes to form an SWNT channel (200 μm long and 1.6 mm wide), and an insulating additional layer is deposited between the channels.

[0019] A circular cell culture chamber (15 mm in diameter and 2 mm in height) is constructed above the ECIS interdigitated electrodes and the MEA multi-channel planar electrodes, simultaneously forming a cell impedance and electrophysiological activity detection unit. Above the electrochemical SWNT thin film array, a metabolic microenvironment multi-disease related protein detection unit (15 mm long, 10 mm wide, and 2 mm high) is formed. The two detection units are connected by three microchannels (10 mm long, 1 mm wide, and 0.5 mm high). The microfluidic cavity contains an injection port and an outlet, which are connected to a micro-injection pump and a waste liquid tank respectively.

[0020] The present invention is respectively used to finely complete the specific differentiation and long-term culture of iPSCs-derived neurons, detect cell proliferation and morphological changes during differentiation, electrophysiological activities and network characteristics after neuron maturation, and the content of multiple disease-related proteins in the neuron metabolic microenvironment, and is the core component for establishing an AD detection platform. Through high-precision alignment technology, the interdigitated electrodes and the planar electrodes of the MEA chip share the same set of electrode points, and different reference electrodes are used to ensure the accuracy of their respective signals, and at the same time, the signals of the same neural network can be detected. The interdigitated electrodes, MEA planar electrodes and SWNT arrays are integrated onto a chip, and conductive glue or wire bonding technology is used to connect the electrodes to the external interface to achieve signal transmission. In order to improve the durability and stability of the chip, the non-working area is coated with polyimide as a protective layer. Finally, the chip is embedded in a customized housing and integrated with microfluidic pipelines and electrical interfaces.

[0021] The microfluidic channel design can precisely position neurons or other cells in a specific area, enabling them to grow on designated electrodes (such as ECIS interdigitated electrodes, MEA electrodes or SWNT arrays), ensuring reliable signal recording. At the same time, microfluidic technology can precisely control the composition, flow rate and flow direction of the culture medium, providing a stable and controllable microenvironment for the growth of neural networks, helping to simulate in-vivo conditions and improve experimental repeatability. Moreover, the microfluidic channels can precisely introduce specific drugs or neurotransmitters into the target area and act on selected cell populations. By designing branch networks or cross channels, different drug concentration stimuli can be applied to multiple areas simultaneously, so as to study the local or global effects of drugs.

[0022] 1) Detection of the impedance characteristics of the electrode array: The electrochemical impedance spectroscopy is used to verify the impedance characteristics of the electrode array. The interdigitated electrodes and the multi-channel planar electrodes are respectively used as the test working electrodes, and the external platinum black is used as the reference electrode. Phosphate buffered saline (PBS) is added to the detection chamber, and a sinusoidal excitation with an amplitude of 5 mV is applied externally to obtain the impedance spectrum change curves of each electrode point, analyze the influence of electroplated platinum black on the impedance of the MEA multi-channel planar electrodes, and obtain the baseline impedance of the interdigitated electrodes. After neuron cells grow, adhere and proliferate into a neural network on the multi-parameter chip, the cells can be coupled with the electrode surface through the electrolyte solution, which is equivalent to hindering the ionic current, and thus the impedance value increases. By applying an alternating current or voltage externally to the electrodes of the multi-parameter micro-nano sensing chip, the impedance of the cells to the connected current can be detected without damage, and finally the result is presented as the change in the impedance value.

[0023] 2) Detection of electrophysiological activities and network characteristics: Through the microfluidic channel, neurons are cultured and matured on the MEA surface, and the same neural network can be detected by the interdigitated electrodes. When neurons form a network on the MEA surface and generate action potentials, the ion current released by the neurons will produce local electric field changes at the electrodes. The MEA can record these electric field changes through each electrode, thereby obtaining the timing and spatial distribution of action potential firing.

[0024] 3) Detection of the content of multiple disease-related proteins in the microenvironment: When iPSCs-derived neurons produce metabolic secretions in the culture environment, the electrochemiluminescent biosensing technology can be used to detect various proteins and small molecule compounds without labeling, with ultra-high precision (down to the femtomolar level), and small and convenient. Through the designed and fabricated densely arranged single-walled carbon nanotube (SWNT) thin film array, combined with the antigen-antibody specific immune reaction, the content of multiple disease-related proteins (Aβ 40 、Aβ 42 、p-tau, t-tau) in the neuronal metabolic microenvironment can be detected. Connect the electrochemical SWNT thin film array of the micro-nano sensing chip to a multi-functional parameter analyzer. After the neural network releases neurotransmitters, these molecules will undergo redox reactions on the surface of the SWNTs, generating current signals. By analyzing the amplitude and time of the current, the amount and kinetic characteristics of neurotransmitter release can be reflected. By dynamically monitoring the concentration and ratio of Aβ and tau proteins through impedance changes, the pathological process and state of the neural network can be accurately reflected.

Claims

1. A multi-parameter micro-nano sensor chip for detecting neural networks, characterized in that: include: ECIS interdigital electrodes, MEA multi-channel planar electrodes, electrochemical SWNT film arrays and microfluidic structures; the microfluidic structure uses photolithography technology to construct microchannels and culture chambers, and the ECIS interdigital electrodes are embedded in the bottom of the chamber to monitor cell attachment, proliferation and morphological changes in real time by applying low-frequency AC signals; the MEA multi-channel planar electrodes use high-density platinum electrodes arranged at the bottom of the same chamber to synchronously record the electrophysiological activities of the neural network; the electrochemical SWNT film arrays are distributed in independent detection areas.

2. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: The ECIS interdigitated electrode consists of a pair of bead-shaped bar electrodes connected at the common end, forming an electrode array structure that crosses each other. The total length of the bar electrode is 5 mm, and the diameter of each bead is about 90 μm. The reference electrodes of the ECIS interdigitated electrode are located at the upper and lower ends.

3. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: The MEA multi-channel planar electrode contains 16 working electrodes arranged in a 4×4 square and a trapezoidal grounding electrode with an area of ​​2mm2. The diameter of a single planar electrode point is 30μm, and the center spacing of the electrode points is 200μm.

4. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: Gold electrodes were deposited on both sides of the electrochemical SWNT film array as source and drain electrodes to form SWNT channels. The channels were 200 μm long and 1.6 mm wide, and an insulating additional layer was deposited between the channels.

5. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: Electrochemical SWNT film arrays are modified with nanomaterials to detect multiple disease-related proteins using amperometry or impedance spectroscopy.

6. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: The microfluidic structure comprises an inlet and an outlet, which are respectively connected to a micro-injection pump and a waste liquid tank.

7. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: Microfluidic structures serve as core carriers for the directional control of the culture, differentiation and microenvironment regulation of iPSCs-derived neurons.

8. The multi-parameter micro-nano sensor chip for detecting neural networks according to claim 1, characterized in that: In the spatial layout of the multi-parameter micro-nano sensor chip, the physical separation of different sensors is achieved through vertical stacking and insulation layer isolation. At the same time, microfluidic valves are used to control the cell migration path in a partitioned manner to avoid signal interference and cross-contamination.

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