Method for constructing chronic unpredictable negative stress model, chronic unpredictable negative stress model and application of chronic unpredictable negative stress model in mental disease research

Abstract

The invention provides a method for constructing a chronic unpredictable negative stress model, the chronic unpredictable negative stress model and application of the chronic unpredictable negative stress model in mental disease research, and belongs to the field of brain organ in-vitro model construction. Through dual verification of a microelectrode array and single cell sequencing, an in-vitro brain organ model capable of simulating chronic unpredictable negative stress core characteristics is successfully constructed, and a potential neural network function reconstruction mechanism and a cell molecule basis of the in-vitro brain organ model are disclosed. The comprehensive research normal form breaks through the limitation of a traditional single technology platform, and an accurate and efficient innovative research platform highly related to human is provided for pathogenesis research of mental diseases, especially chronic stress related diseases such as depression and anxiety and development of novel treatment strategies.

Description

Technical Field

[0001] The present invention belongs to the field of construction of in vitro models of brain organoids, and particularly relates to a chronic unpredictable negative stress model, a construction method thereof, and uses in the research of mental diseases. Background Art

[0002] The chronic unpredictable negative stress model is an animal model of depression developed more than 20 years ago. The basis of this model is that after long-term exposure to a series of mild but unpredictable stressors, animals will develop a state of impaired reward salience, similar to the anhedonia observed in major depressive disorder.

[0003] Mental diseases such as depression, schizophrenia, bipolar disorder, and anxiety disorder are closely related to chronic stress, especially chronic unpredictable negative stress. Although traditional animal experimental models have certain advantages in studying the pathology of chronic stress-related diseases and screening potential drugs, due to the differences between animal models and humans, it is often difficult to accurately simulate the pathophysiological process of human diseases. At the same time, animal experiments also have limitations such as ethical controversies, long cycles, and high costs.

[0004] With the development of stem cell and organoid technologies, brain organoids, as a model for simulating human brain development and partial functional structures in vitro, have been increasingly valued in the research of human nervous system development and diseases. However, how to accurately simulate the state of chronic unpredictable negative stress on in vitro brain organoid models still faces technical challenges.

[0005] In recent years, most studies have tried to use chemical stimuli (such as inflammatory factors, bacteria, viruses, and related metabolites) to simulate stress environments or stress events in vitro, but most methods have not fully considered the multidimensional properties of emotional stimuli. As an important carrier of human emotional communication, voice stimuli can simulate various emotional states and stress events faced in real life through audio signals with specific emotions (happy, sad, neutral, etc.).

[0006] If it is possible to convert emotional sound information into electrical inputs that can be perceived by ex vivo brain organoids in an in vitro environment, and perform different modes of chronic stimulation on brain organoids through microelectrode arrays to establish an in vitro model of chronic unpredictable negative stress, it will provide a novel research paradigm and tool for the research of mental diseases such as depression, schizophrenia, bipolar disorder, and anxiety disorder. Summary of the Invention

[0007] The present invention provides a comprehensive method combining emotional voice signal encoding electrical stimulation, brain organoid culture, microelectrode array (MEA) recording, and single-cell sequencing analysis for constructing and validating a highly simulated in vitro chronic unpredictable negative stress model. This method overcomes the species differences and ethical limitations of traditional animal models, as well as the limitation that a single ex vivo technique cannot comprehensively analyze the complexity of the stress response neural network.

[0008] The present invention is achieved through the following steps: (1) encoding a standardized emotional voice signal (especially negative emotions such as "sadness") into an accurately controllable electrical stimulation sequence through a specific algorithm (such as extracting Mel-frequency cepstral coefficients); (2) inducing and differentiating human stem cells into mature brain organoids; (3) using a microelectrode array system to apply the encoded emotional electrical stimulation to the brain organoids in a chronic and unpredictable manner (random stimulation parameters, order, and number of times); (4) real-time monitoring and analyzing the changes in the neural network function of the brain organoids through MEA; (5) combining single-cell sequencing technology to deeply analyze the biological mechanisms induced by negative stress at the cell type and molecular levels. The present invention combines electrophysiological stimulation encoded with emotional information with brain organoids, MEA, and single-cell sequencing for the first time, providing a more accurate, efficient, and highly relevant in vitro platform for the pathological mechanism research and drug screening of mental diseases (such as depression, anxiety, etc.).

[0009] The purpose of the present invention is to provide a chronic unpredictable negative stress model, a method for constructing the same, and its use in the research of mental diseases.

[0010] The present invention provides a method for constructing a chronic unpredictable negative stress model, and the method includes the following steps: (1) Extracting features from the negative emotional voice signal to convert the emotional voice signal into a negative emotional electrical stimulation signal; (2) Transferring the brain organoids to a supporting chip of the microelectrode array system for culture; (3) Importing the negative emotional electrical stimulation signal in step (1) into the microelectrode array system to continuously input negative emotional electrical stimulation to the brain organoids to obtain a chronic unpredictable negative stress model.

[0011] Further, the feature extraction in step (1) is to remove environmental noise from the voice containing negative emotional features through band-pass filtering, and then use the Librosa audio processing library based on Python to extract Mel-frequency cepstral coefficients. Each Mel-frequency cepstral coefficient corresponds to the energy distribution in a specific frequency interval, and a 64-dimensional Mel-frequency cepstral coefficient feature vector is extracted. After normalization processing, a binary coding sequence is generated through threshold determination to obtain a negative emotional electrical stimulation signal.

[0012] Further, the speech with negative emotion features described in step (1) is the speech with "sad" emotion features.

[0013] Further, the brain organoids described in step (2) are obtained by inducing the differentiation of stem cells in the neural direction and culturing them in three-dimensional suspension until they are mature. The induction medium is a 3D brain organoid induction medium.

[0014] Further, the stem cells are induced pluripotent stem cells or embryonic stem cells, and the medium is STEMdiff™ Cerebral Organoid Kit 。

[0015] Further, the culture conditions described in step (2) are 1-10% Matrigel, 20-50 °C, and 1-10% CO2.

[0016] Further, the culture conditions described in step (2) are 5% Matrigel, 37 °C, and 5% CO2.

[0017] Further, the negative emotion electrical stimulation input in step (3) is a negative emotion electrical stimulation that randomly varies within the following parameter ranges: stimulation voltage 100-1000 mV, pulse width 100-500 μs, stimulation frequency 10-300 Hz, stimulation interval 20-100 s, every Number of stimulations per day: 3 - 50 times 。

[0018] Further, the negative emotion electrical stimulation input in step (3) is a negative emotion electrical stimulation that randomly varies within the following parameter ranges: stimulation voltage 300-800 mV, pulse width 400 μs, stimulation frequency 50-200 Hz, stimulation interval 60 s, every Number of stimulations per day: 10 - 30 times 。

[0019] Further, the continuously input negative emotion electrical stimulation in step (3) is randomly selected from the negative emotion electrical stimulation signals obtained in step (1) and applied to the brain organoids in a random order; the continuous input is a continuous input for more than 7 days.

[0020] Further, the continuous input is a continuous input for more than 14 days.

[0021] The present invention also provides a chronic unpredictable negative stress model, and the chronic unpredictable negative stress model is constructed by the above method.

[0022] The present invention also provides the use of the above method in constructing an in vitro chronic unpredictable negative stress model.

[0023] The present invention also provides the use of the above method and the chronic unpredictable negative stress model in the research of mental diseases.

[0024] Further, the mental illness is depression, schizophrenia, bipolar disorder or anxiety disorder.

[0025] In the present invention, "more than 14 days" means greater than or equal to 14 days, and so on.

[0026] In the present invention, "D14" means the 14th day, and so on.

[0027] In the present invention, "D0" means the 0th day, that is, the day when the experiment starts.

[0028] Compared with the prior art, the present invention has the following beneficial effects: 1. More realistic emotion simulation: By extracting and encoding the features of a variety of emotional voice signals (such as emotions like "sad", "happy", "neutral", etc.), an in vitro stress environment closer to the emotional changes in real human life can be constructed, which is superior to single chemical or physical stimuli.

[0029] 2. Precise controllability and real-time monitoring: The MEA system is used to achieve precise electrical stimulation output of brain organoids and real-time, high-throughput recording and analysis of neural activities, ensuring the controllability of the experiment and the reliability of the data; 3. Reduction of animal use: While ensuring the effectiveness of the model, it helps to reduce animal experiments, meets ethical requirements, saves costs and shortens the research cycle; 4. High human relevance and in-depth mechanism analysis: Brain organoids are derived from human stem cells and can more directly simulate the development and pathological processes of the human brain. Combined with single-cell sequencing technology, the cell type-specific molecular mechanism of the stress response can be revealed at the single-cell resolution, significantly improving the clinical translation potential of the research results.

[0030] 5. Comprehensive verification and model confirmation: Integrating the network function analysis of MEA and the molecular mechanism analysis of single-cell sequencing, the effectiveness and biological basis of the model are double-verified from the macroscopic network to the microscopic molecular level, providing a more comprehensive evidence chain for the confirmation of the model.

[0031] The present invention can induce neural network reconstruction related to chronic negative stress in brain organoids, provide new ideas for the research of related diseases, provide a new in vitro model for the research of various mental diseases and drug screening, reduce the use of animal tests, shorten the research cycle, and improve the relevance to the human pathophysiological process, thereby providing important references for drug screening and disease mechanism research.

[0032] Obviously, based on the above content of the present invention, according to the common general knowledge and conventional means in the art, without departing from the above basic technical idea of the present invention, various other forms of modification, replacement or change can also be made.

[0033] The following is a further detailed description of the above content of the present invention in the form of specific embodiments. However, this should not be construed as limiting the scope of the above subject matter of the present invention to the following embodiments. Any technology implemented based on the above content of the present invention falls within the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the device and experimental process of the present invention.

[0035] Figure 2 It is a trend graph of the change of the significant edge weight mean with the experimental days (D0 - D14) and the statistical results of the differences on D14.

[0036] Figure 3 It is a trend graph of the change of the modularity score with the experimental days (D0 - D14) and the statistical results of the differences on D14.

[0037] Figure 4 It is a trend graph of the change of the proportion of the activity of the top 10% principal components (Top 10% PC) with the experimental days (D0 - D14) and the statistical results of the differences on D14.

[0038] Figure 5 It is a trend graph of the change of the number of non - negative matrix factorization components (Num NMF components) with the experimental days (D0 - D14) and the statistical results of the differences on D14.

[0039] Figure 6 It is the result of the GO functional enrichment analysis of the differentially expressed genes identified by single - cell sequencing on the 14th day of the experiment in the chronic negative stress group compared with the blank control group, showing the top 15 biological process entries with significant enrichment.

[0040] Figure 7 It is the result of the gene set enrichment analysis (GSEA) related to ribosome function in the single - cell sequencing data on the 14th day of the experiment in the chronic negative stress group compared with the blank control group.

[0041] Figure 8 It is the GSEA result related to ribosome function in the single - cell sequencing data on the 14th day of the experiment in the mixed - stimulation control group compared with the blank control group.

[0042] Figure 9 It is the GSEA result related to mitochondrial function in the single - cell sequencing data on the 14th day of the experiment in the chronic negative stress group compared with the blank control group.

[0043] Figure 10GSEA results related to mitochondrial function in single-cell sequencing data on day 14 in the mixed stimulation control group compared with the blank control group.

[0044] Figure 11 GSEA results related to synaptic function in single-cell sequencing data on day 14 in the chronic negative stress group compared with the blank control group.

[0045] Figure 12 GSEA results related to synaptic function in single-cell sequencing data on day 14 in the mixed stimulation control group compared with the blank control group.

[0046] Figure 13 Comparison results of the relative expression levels of key neurotrophic factors detected by single-cell sequencing in the chronic negative stress group, the mixed stimulation control group, and the blank control group on day 14 of the experiment BDNF of. Specific implementation mode

[0047] The raw materials and equipment used in the present invention are all known products and are obtained by purchasing commercially available products.

[0048] Example 1: Method for constructing a chronic unpredictable negative stress model based on negative emotion-encoded electrical stimulation on an in vitro model The device and experimental process of the present invention are as Figure 1 shown.

[0049] I. Emotional voice signal-encoded electrical stimulation A total of 200 voice segments with "happy", "neutral", and "sad" emotional characteristics used in the experiment were all taken from a standardized emotional voice database to ensure the standardization of emotion classification and strong data repeatability, thereby improving the reliability and universality of experimental data. Feature extraction and electrical coding conversion were performed on 200 voices with "happy", "neutral", and "sad" emotional characteristics. After removing environmental noise through band-pass filtering, Mel-frequency cepstral coefficients (MFCCs) were extracted using the Librosa audio processing library based on Python. Each MFCC coefficient corresponds to the energy distribution in a specific frequency interval, and a 64-dimensional MFCC feature vector (selected according to the actual MEA chip used) was extracted. After normalization, a binary coding sequence was generated by judging through a threshold (threshold = 15). Among them, the time period with a feature value higher than the preset threshold was converted into a biphasic square wave electrical stimulation, and the time period lower than the threshold corresponded to a silent state. The "sad" emotional type voice segment corresponded to 100 encodings, and the "happy" and "neutral" emotional types each corresponded to 50 encodings. Through rich emotional type stimulation signals, a multi-dimensional emotional electrical stimulation input set was constructed.

[0050] II. Configuration and unpredictability setting of the microelectrode array stimulation system 1. Selection of Microelectrode Array (MEA) An MEA system (Axion biosystem) applicable to in vitro brain organoid culture is used, which has adjustable electrode distribution and the number of electrodes to ensure multi-point stimulation and recording of brain organoids.

[0051] 2. Stimulation System Configuration (1) Import the emotional electrical stimulation input set into the MEA system using a computer or a dedicated controller.

[0052] (2) Set the range of electrical stimulation parameters: stimulation voltage (300 mV - 800 mV, adjustable), pulse width (400 μs), stimulation frequency (50 - 200 Hz, adjustable), stimulation interval (60 s), and the number of stimulations per day (10 - 30 times).

[0053] In the actual implementation process, specific parameters can be randomly varied within the above range according to experimental needs to ensure that the model has the characteristic of "unpredictability" and more realistically simulates the chronic negative stress state.

[0054] 3. Unpredictability Setting (1) Continuously input the electrical stimulation encoded with the "sad" emotion to the brain organoids for 14 days or longer to simulate chronic negative stress; (2) Within 14 days or longer, use a random stimulation sequence, random stimulation voltage intensity, random stimulation frequency, and random number of stimulations to make the brain organoids unable to predict the stimulation pattern.

[0055] III. Culture and Stimulation of Brain Organoids 1. Brain Organoid Culture Based on human pluripotent stem cells (induced pluripotent stem cells, iPSCs or embryonic stem cells, ESCs), use ( E8, Gibco ) culture medium to maintain stem cell culture. When the cell state is appropriate, perform neural differentiation induction and three-dimensional suspension culture, and use a standardized 3D brain organoid induction culture medium ( STEMdiff™ Cerebral Organoid Kit, STEMCELL Technologies), Cultivate for a long time according to the method in the culture medium supporting instructions until it matures to D120, and then obtain preliminarily mature brain organoids.

[0056] 2. Integration of Brain Organoids and MEA Transfer the above preliminarily mature brain organoids to the MEA system ( Maestro Pro, Axion BioSystems)on the supporting chip. The high-throughput microelectrodes of the MEA can simultaneously collect electrical signals and deliver electrical stimuli to the brain organoids. To ensure sufficient contact between the brain organoids and the electrodes and obtain a stable signal coupling relationship, before transferring the brain organoids, the surface of the MEA culture plate was coated with a solution containing 5% Matrigel ( Matrigel, Corning ). The brain organoids placed in the MEA culture plate continued to be maintained in an environment of 37 °C and 5% CO2 to gradually adapt and stabilize in the new environment. After the brain organoids had adapted to the MEA culture plate environment for 14 days, and after monitoring the spontaneous electrical discharges of the brain organoids indicated that the microelectrode chips had established a tight connection, the subsequent negative emotion stimulation experiment was carried out.

[0057] 3. Stimulation and Observation Period Start a 14-day (extendable according to requirements) emotional stimulation experiment: Experimental group (chronic negative stress group): During the continuous stimulation period, the stimulation voltage intensity, stimulation frequency, pulse width, stimulation interval, and the number of stimulations per day, which only included sad emotion encoding, all randomly varied within a predetermined range to reflect the characteristics of unpredictable negative stress. Among them, the "random stimulation number" refers to the number of events of randomly selecting and applying electrical stimulation per day, that is, the number of times of stimulating the brain organoids per day also randomly varied, so as to comprehensively achieve the randomness and unpredictability of the stimulation pattern.

[0058] Control group 1 (mixed stimulation control group): Randomly give unpredictable stress including two emotions, happiness / neutrality, every day, and the variation range of this unpredictable stress is the same as that of the experimental group; Control group 2 (blank control group): Do not apply any emotion-encoding stimulation, and only perform the integration of brain organoids and MEA plates and conventional nutrient culture.

[0059] 4. Data Collection and Analysis Use MATLAB 2024b to analyze the electrode discharge signals recorded by the MEA, and focus on the following network graph theory and decomposition indexes: average value of significant edge weight (Significant edge weight mean), modularity score (Modularity score), top 10% principal components (Top 10% PC), number of non-negative matrix factorization components (Num NMFcomponents), etc.; evaluate the impact of chronic unpredictable negative stress on the overall neural network activity and functional partitioning of brain organoids. The specific analysis process is as follows: (1) Event detection and functional connectivity calculation: First, threshold detection or template matching methods are used to identify and record the time points and amplitudes of spike discharges; spike sorting is further performed as needed to distinguish the discharges of different neurons under the same electrode; the correlation coefficient, mutual information, or phase synchronization degree of the discharge rate time series between any two electrodes (or neurons) is calculated respectively to obtain a matrix reflecting the functional connectivity strength.

[0060] (2) Network construction and graph theory metrics: The obtained functional connectivity matrix is thresholded to retain the edges that reach statistical significance or have a higher strength, forming a weighted or binary network adjacency matrix; the average significant edge weight is calculated to measure the overall level of high-strength edges in the network; the Louvain algorithm, a commonly used community detection algorithm, is used to partition the network to obtain the modularity score, which reflects the network partition and subgroup integration degree.

[0061] (3) Multidimensional degradation analysis: Principal component analysis (PCA) is used to reduce the dimension of the high-dimensional time series or the data after unfolding the connectivity matrix, and the proportion of the first several principal components is statistically analyzed. The top 10% principal components (Top 10% PC) are extracted as important indicators to measure the dominant mode; the non-negative matrix factorization (NMF) is iteratively solved to determine the number of non-negative matrix factorization components that can significantly describe the network activity (Num NMF components) to quantify the diversity of network activity.

[0062] (4) Single-cell sequencing analysis: Single-nucleus suspensions are prepared from the brain organoid samples collected on the 14th day of the experiment. First, tissue processing is performed to extract the cell nuclei to obtain single-nucleus suspensions. Subsequently, the quality of the cell nuclei is inspected. Then, the gel beads containing barcode information are combined with the mixture of the single-nucleus suspension and enzymes, and then gel bead-in-emulsion (GEMs) containing single cell nuclei are formed in the oil phase through a microfluidic system. PCR amplification is performed using cDNA as a template to construct a standard sequencing library. Finally, PCR amplification is performed to obtain the final DNA library, and the constructed library is sequenced by high-throughput sequencing using the Illumina sequencing platform.

[0063] (5)Bioinformatics analysis process: First, use the official 10x Genomics analysis software Cell Ranger to process the raw sequencing data, including data filtering, sequence alignment (alignment to the reference genome), gene transcript quantification, and cell identification, and finally obtain the gene expression matrix of each cell (nucleus). Further, use the R software package Seurat to further analyze the gene expression matrix output by Cell Ranger. The main steps include: cell filtering, standardization and normalization of gene expression data, cell subpopulation clustering analysis, differential expression gene analysis of each subpopulation, and screening of Marker genes for cell type identification. In addition, gene ontology (GO) / Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and gene set enrichment analysis (GSEA) are also performed based on differential genes or specific gene sets to deeply analyze the changes in cell composition, gene expression profiles, and biological functions of brain organoids under different experimental conditions.

[0064] 5. Experimental results (1)Results of MEA neural network analysis of brain organoids: 1) Increase in the average value of the weight of significantly connected edges: As shown in Figure 2 , on the 14th day, the average weight of the "significant edges" in the functional connection map of the chronic negative stress group was higher than that of the blank control group, indicating enhanced neuronal synchrony and synaptic plasticity.

[0065] 2) Increase in network modularity: As shown in Figure 3 , the chronic negative stress group caused increased network partitioning in brain organoids, with a higher modularity score, indicating enhanced cohesion between local neural clusters, while the connections between different modules were relatively reduced, which is consistent with the common phenomenon of "abnormal differentiation of functional networks" under chronic negative stress. 3) Increase in the proportion of the top 10% principal components: As shown in Figure 4 , the PCA results showed that the proportion of the chronic negative stress group in the overall variance of the main components increased, meaning that network activities were more concentrated in several dominant patterns, reflecting the "centralization" or "stereotyping" trend of network dynamics under stress.

[0066] 4) Increase in the number of non-negative matrix factorization components: As shown in Figure 5 , compared with the blank control group, the chronic negative stress group decomposed more sub-components with independent activity characteristics in the network, suggesting that while macroscopic synchrony is enhanced, local neural clusters show diverse or differentiated characteristics.

[0067] (2)Results of single-cell transcriptome sequencing of brain organoids: To deeply explore the molecular mechanisms underlying the network function remodeling observed in the above MEA and to verify the biological effects of this model, we performed single-cell transcriptome sequencing analysis on the brain organoids of each group on the 14th day of the experiment.

[0068] 1) GO functional enrichment analysis of differentially expressed genes: Differentially expressed gene analysis was performed between the experimental group and the blank control group. The results of GO functional enrichment showed ( Figure 6 ), and the differentially expressed genes were significantly enriched in the yellow-highlighted entries in the figures such as "synapse GO:0045202, glutamatergic synapse GO:0098978, and dendrite GO:0030425" and the blue-highlighted entries in the figures related to protein synthesis biological processes such as "ribosome GO:0005840, translation GO:0006412". This preliminarily indicates that chronic negative stress mainly affects the biological processes related to protein translation and synthesis in brain organoids and the morphology and function of neuronal synapses.

[0069] 2) Changes in the biological pathways of core protein synthesis and respiratory function: GSEA analysis further revealed systematic changes at the pathway level. As Figure 7 shown, in the experimental group (chronic negative stress group), the pathways related to ribosomes (such as: "ribosome", "cytoplasmic ribosome", "cytoplasmic large ribosomal subunit", etc.) and the pathways related to protein translation (such as: "cytoplasmic translation", "translation ", etc.) were all significantly downregulated, suggesting that the protein synthesis ability related to ribosomes and translation may be inhibited. In the mixed stimulation control group, the same protein translation-related pathways were significantly upregulated ( Figure 8 ). Similarly, the pathways related to respiratory function (such as: "cellular respiration", "oxidative phosphorylation", "mitochondrial proton-transporting ATP synthase complex", etc.) also showed significant downregulation enrichment in the chronic negative stress group ( Figure 9 ), suggesting that oxidative stress and energy metabolism may be impaired under chronic stress conditions, while these pathways were upregulated in the mixed stimulation control group ( Figure 10 ). These results reveal that chronic negative stress may specifically damage the basic biosynthesis and energy metabolism functions of cells, which is consistent with the results observed in animal studies, while the mixed "neutral" and "happy" emotional stimuli may produce different or even opposite biological effects.

[0070] 3) GSEA results of synaptic function-related pathways and cell type specificity: GSEA analysis also examined the changes in synaptic function-related pathways. The results showed that in the chronic negative stress group ( Figure 11 ) and the mixed stimulation control group ( Figure 12Compared with the blank control group, significant upregulation and enrichment of pathways related to synaptic function were observed in all groups. However, this upregulation showed obvious neuron type specificity: in the chronic negative stress group, the most significantly upregulated pathways were mainly associated with the synaptic functions of GABAergic and glutamatergic neurons; while in the mixed stimulation control group, the most significantly upregulated pathways were more associated with the synaptic functions of dopaminergic neurons. This finding not only reveals that stimuli with different emotional natures may affect synaptic plasticity by regulating different neurotransmitter systems, but also highlights the unique advantage of single-cell sequencing in analyzing cell heterogeneous responses. These changes in synaptic gene expression levels, especially the upregulation of inhibitory and excitatory neuron synaptic functions in the experimental groups, provide direct molecular mechanism support for the enhanced functional connectivity detected by MEA ( Figure 2 ) and network modularity ( Figure 3 ).

[0071] 4) Detection of the expression of key neurotrophic factors for synaptic plasticity: The present invention further detected the expression levels of brain-derived neurotrophic factor ( BDNF ), which is closely related to synaptic plasticity and neural network remodeling. As Figure 13 shown, compared with the blank control group, the relative expression level of BDNF in the brain organoids of the chronic negative stress group showed an increase, while the expression level of BDNF in the mixed stimulation control group was further increased on the basis of the chronic negative stress group level. BDNF plays an important role in neural plasticity, learning and memory, and stress response regulation. The change in its expression level further corroborates that this model has successfully induced molecular phenotypes related to chronic stress.

[0072] (3) Comprehensive discussion and model verification: This study combined MEA network function analysis and single-cell transcriptome sequencing to perform double verification on the chronic unpredictable negative stress brain organoid model constructed based on emotional speech coding electrical stimulation. The MEA results showed that chronic negative stimulation significantly changed the macroscopic neural network activity pattern of the brain organoids, manifested as enhanced functional connectivity, increased network modularity, and solidification of the neural network activity pattern. The single-cell sequencing results revealed the biological basis of these network changes from the microscopic molecular level: chronic negative stimulation specifically inhibited the pathways related to ribosome and mitochondrial functions, while upregulating the expression of genes related to synaptic functions in specific types of neurons (GABAergic and glutamatergic), and affecting the level of key neurotrophic factor BDNF . These molecular-level findings, especially the changes in synaptic function, provide a direct mechanistic explanation for the network function reconstruction observed by MEA.

[0073] Compared with the findings of existing animal model studies (such as abnormal functional connectivity and altered network modularity) and neuroimaging findings in patients with depression, the in vitro model constructed in the present invention shows good consistency and correlation at both the network function and molecular mechanism levels (references are as follows). The introduction of single-cell sequencing has greatly enhanced the understanding of cell heterogeneity and complex molecular regulatory networks within the model.

[0074] In summary, through the dual verification of MEA and single-cell sequencing, the present invention has not only successfully constructed an in vitro brain organoid model that can mimic the core characteristics of chronic unpredictable stress, but also deeply revealed its potential neural network function reconstruction mechanism and cellular and molecular basis. This comprehensive research paradigm breaks through the limitations of traditional single-technical platforms and provides an accurate, efficient, and highly relevant innovative research platform for the study of the pathogenesis of mental diseases (especially chronic stress-related diseases such as depression and anxiety) and the development of new treatment strategies.

[0075] Through the dual verification of MEA and single-cell sequencing, the present invention has clarified the effectiveness and accuracy of the chronic unpredictable stress model at the brain organoid level. This comprehensive research paradigm breaks through the limitations of traditional single-technical platforms, greatly improving the precision, repeatability, and clinical relevance of model construction, and is expected to become an important new in vitro research platform in the field of mental disease research.

Claims

1. A method for constructing a chronic unpredictable negative stress model, characterized in that: The method comprises the following steps: (1) Extract features from negative emotion voice signals and convert the emotion voice signals into negative emotion electrical stimulation signals; (2) Transfer the brain organoids to a supporting chip of a microelectrode array system for cultivation; (3) Import the negative emotion electrical stimulation signals in step (1) into the microelectrode array system to continuously input negative emotion electrical stimulation to the brain organoids, and obtain a chronic unpredictable negative stress model.

2. The method according to claim 1, wherein: In step (1), the feature extraction is to remove environmental noise from the voice containing negative emotion features through band-pass filtering, and then use the Librosa audio processing library based on Python to extract Mel frequency cepstral coefficients. Each Mel frequency cepstral coefficient corresponds to the energy distribution in a specific frequency interval, and a 64-dimensional Mel frequency cepstral coefficient feature vector is extracted. After normalization processing, a binary coding sequence is generated through threshold determination to obtain a negative emotion electrical stimulation signal.

3. The method according to claim 2, wherein: The voice containing negative emotion features in step (1) is the voice containing the emotion feature of "sadness".

4. The method according to claim 1, wherein: The brain organoids in step (2) are obtained by inducing the differentiation of stem cells in the neural direction and performing three-dimensional suspension culture until maturity. The induction medium is a 3D brain organoid induction medium.

5. The method according to claim 1, wherein: The culture conditions in step (2) are 1-10% Matrigel, 20-50 °C, and 1-10% CO2.

6. The method according to claim 1, characterized in that: In step (3), the input negative emotion electrical stimulation is a negative emotion electrical stimulation that randomly varies within the following parameter ranges: stimulation voltage 100-1000 mV, pulse width 100-500 μs, stimulation frequency 10-300 Hz, stimulation interval 20-100 s, and the number of stimulation times per day is 3-50 times.

7. The method according to claim 1, wherein: The continuous input of negative emotion electrical stimulation in step (3) is randomly selected from the negative emotion electrical stimulation signals obtained in step (1) and applied to the brain organoids in a random order; the continuous input is continuous input for more than 7 days.

8. A chronic unpredictable negative stress model, characterized in that: The chronic unpredictable negative stress model is constructed by the method according to any one of claims 1-6.

9. Use of the method according to any one of claims 1-6 in constructing an in vitro chronic unpredictable negative stress model.

10. Use of the method according to any one of claims 1-6 and the chronic unpredictable negative stress model according to claim 7 in the research of mental diseases.

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