A method for constructing a chronic unpredictable negative stress model and a chronic unpredictable negative stress model and its use in mental illness research

Abstract

The present invention provides a method for constructing a chronic unpredictable negative stress model and a chronic unpredictable negative stress model and its use in the study of mental illness, belonging to the field of in vitro brain organoid model construction. Through dual verification of microelectrode arrays and single-cell sequencing, the present invention successfully constructed an in vitro brain organoid model that can simulate the core characteristics of chronic unpredictable negative stress, and revealed its potential neural network functional reconstruction mechanism and cellular molecular basis. This comprehensive research paradigm breaks through the limitations of traditional single technology platforms and provides an accurate, efficient, and highly human-relevant innovative research platform for the study of the pathogenesis of mental illness, especially diseases related to chronic stress such as depression, anxiety, etc., and the development of new treatment strategies.

Description

Technical Field

[0001] The present invention belongs to the field of constructing in vitro models of brain organoids, and specifically relates to a chronic unpredictable negative stress model, a construction method thereof, and its use in the study of mental illnesses. Background Art

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

[0003] Mental illnesses such as depression, schizophrenia, bipolar disorder, and anxiety disorders are closely associated with chronic stress, especially chronic, unpredictable negative stress. While traditional animal experimental models offer certain advantages for studying the pathology of chronic stress-related diseases and screening potential drugs, differences between animal models and humans often make it difficult to accurately simulate the pathophysiology of human diseases. Furthermore, animal experiments are subject to limitations such as ethical concerns, long timelines, and high costs.

[0004] With the development of stem cell and organoid technologies, brain organoids, as models that simulate human brain development and some functional structures in vitro, are gaining increasing attention in the study of human nervous system development and disease. However, accurately simulating the state of chronic, unpredictable negative stress in in vitro brain organoid models remains a technical challenge.

[0005] Recent studies have attempted to use chemical stimuli (such as inflammatory factors, bacteria, viruses, and their metabolites) to simulate stressful environments or events in vitro. However, most methods have not fully considered the multidimensional nature of emotional stimuli. As a key vehicle for human emotional communication, speech stimulation can simulate a variety of emotional states and stressful events encountered in real life through audio signals with specific emotions (such as happiness, sadness, and neutrality).

[0006] If emotional sound information can be converted into electrical input that can be sensed by in vitro brain organoids in an in vitro environment, and if different patterns of chronic stimulation of the brain organoids can be performed through microelectrode arrays to establish an in vitro model of chronic unpredictable negative stress, it will provide a novel research paradigm and tools for studying mental illnesses such as depression, schizophrenia, bipolar disorder, and anxiety. Summary of the Invention

[0007] This paper provides a comprehensive approach combining emotional speech signal encoding and electrical stimulation, brain organoid culture, microelectrode array (MEA) recording, and single-cell sequencing analysis to construct and validate a highly realistic in vitro model of chronic, unpredictable negative stress. This approach overcomes the limitations of traditional animal models, including species diversity and ethical restrictions, as well as the inability of single in vitro techniques to fully dissect the complexity of stress response neural networks.

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

[0009] The purpose of the present invention is to provide a chronic unpredictable negative stress model and a construction method thereof and use thereof in the study of mental illness.

[0010] The present invention provides a method for constructing a chronic unpredictable negative stress model, the method comprising the following steps:

[0011] (1) Extract features of negative emotional speech signals and convert emotional speech signals into negative emotional electrical stimulation signals;

[0012] (2) Transferring brain organoids to a microelectrode array system on a chip for culture;

[0013] (3) The negative emotional electrical stimulation signal of step (1) is introduced into the microelectrode array system to continuously input negative emotional electrical stimulation to the brain organoids to obtain a chronic unpredictable negative stress model.

[0014] Furthermore, the feature extraction in step (1) is to remove the environmental noise from the speech containing negative emotional features through bandpass filtering, and then use the Python-based Librosa audio processing library to extract the Mel-frequency cepstral coefficients, each Mel-frequency cepstral coefficient corresponds to the energy distribution of a specific frequency interval, and extract the 64-dimensional Mel-frequency cepstral coefficient feature vector. After normalization, a binary coding sequence is generated through threshold judgment to obtain a negative emotional electrical stimulation signal.

[0015] Furthermore, the speech containing negative emotional features in step (1) is speech containing "sad" emotional features.

[0016] Furthermore, the brain organoids in step (2) are obtained by inducing neural differentiation of stem cells and culturing them in three-dimensional suspension until they mature, and the induction culture medium is a 3D brain organoid induction culture medium.

[0017] Furthermore, the stem cells are induced pluripotent stem cells or embryonic stem cells, and the culture medium is STEMdiff™ Cerebral Organoid Kit .

[0018] Furthermore, the culture conditions in step (2) are 1-10% matrix gel, 20-50° C., and 1-10% CO 2 .

[0019] Furthermore, the culture conditions in step (2) are 5% matrix gel, 37° C., and 5% CO 2 .

[0020] Furthermore, the negative emotion electrical stimulation input in step (3) is a negative emotion electrical stimulation that varies randomly within the following parameter range: stimulation voltage 100-1000 mV, pulse width 100-500 μs, stimulation frequency 10-300 Hz, stimulation interval 20-100 s, and each Stimulation frequency per day: 3~50 times .

[0021] Furthermore, the negative emotion electrical stimulation input in step (3) is a negative emotion electrical stimulation that changes randomly within the following parameter range: stimulation voltage 300-800 mV, pulse width 400 μs, stimulation frequency 50-200 Hz, stimulation interval 60 s, and each Stimulation frequency per day: 10 to 30 times .

[0022] Furthermore, the negative emotional electrical stimulation continuously input in step (3) is randomly extracted from the negative emotional electrical stimulation signal obtained in step (1) and applied to the brain organoid in a random order; the continuous input is continuous input for more than 7 consecutive days.

[0023] Furthermore, the continuous input is continuous input for more than 14 consecutive days.

[0024] The present invention also provides a chronic unpredictable negative stress model, which is constructed according to the above method.

[0025] The present invention also provides use of the above method in constructing an in vitro chronic unpredictable negative stress model.

[0026] The present invention also provides uses of the above method and chronic unpredictable negative stress model in the study of mental illness.

[0027] Furthermore, the mental illness is depression, schizophrenia, bipolar disorder or anxiety disorder.

[0028] In the present invention, "more than 14 days" means greater than or equal to 14 days, and so on.

[0029] In the present invention, "D14" means the 14th day, and so on.

[0030] In the present invention, "D0" means day 0, that is, the day when the experiment started.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. More realistic emotion simulation: By extracting and encoding the features of multiple emotional speech signals (such as "sad," "happy," and "neutral"), we can create an in vitro stress environment that is closer to real-life human emotional changes, surpassing single chemical or physical stimulation.

[0033] 2. Precise controllability and real-time monitoring: The MEA system enables precise electrical stimulation output and real-time, high-throughput recording and analysis of neural activity in brain organoids, ensuring experimental controllability and data reliability.

[0034] 3. Reduce animal use: While ensuring the validity of the model, it helps reduce animal experiments, comply with ethical requirements, save costs and shorten research cycles;

[0035] 4. High Human Relevance and In-Depth Mechanistic Insights: Brain organoids are derived from human stem cells and can more directly simulate human brain development and pathological processes. Combined with single-cell sequencing technology, they can reveal the cell-type-specific molecular mechanisms of stress responses at single-cell resolution, significantly enhancing the clinical translational potential of these findings.

[0036] 5. Comprehensive Verification and Model Confirmation: This approach integrates network functional analysis using MEA and molecular mechanism analysis using single-cell sequencing, verifying the model's validity and biological basis from both the macroscopic network and microscopic molecular levels, providing a more comprehensive chain of evidence for model confirmation.

[0037] The present invention can induce neural network reconstruction related to chronic negative stress in brain organoids, providing new ideas for studying related diseases, and providing new in vitro models for the study of various mental illnesses and drug screening, reducing the use of animal experiments, shortening the research cycle, and improving the correlation with human pathophysiological processes, thereby providing an important reference for drug screening and disease mechanism research.

[0038] Obviously, based on the above contents of the present invention, according to common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, other various forms of modifications, replacements or changes can be made.

[0039] The following is a further detailed description of the present invention through specific embodiments in the form of examples. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-mentioned content of the present invention fall within the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the device and experimental flow of the present invention.

[0041] Figure 2 Figure 3 is a trend chart showing the change of the significant edge weight mean with the experimental days (D0-D14) and the difference statistics results of D14.

[0042] Figure 3 Figure 3 is a trend chart showing the network modularity score changing with the number of experimental days (D0-D14) and the statistical results of the difference on D14.

[0043] Figure 4 The trend graph of the activity proportion of the top 10% principal components (Top 10% PC) changing with the experimental days (D0~D14) and the difference statistical results of D14.

[0044] Figure 5 The trend graph of the non-negative matrix decomposition components (Num NMF components) changing with the experimental days (D0~D14) and the difference statistics results of D14.

[0045] Figure 6 The GO functional enrichment analysis results of the differentially expressed genes identified by single-cell sequencing on the 14th day of the experiment compared with the blank control group showed the top 15 significantly enriched biological process entries.

[0046] Figure 7 This is the result of gene set enrichment analysis (GSEA) related to ribosome function in the single-cell sequencing data of the chronic negative stress group compared with the blank control group on the 14th day of the experiment.

[0047] Figure 8 GSEA results related to ribosome function in single-cell sequencing data on day 14 of the experiment compared with the mixed stimulation control group and the blank control group.

[0048] Figure 9GSEA results related to mitochondrial function in single-cell sequencing data on the 14th day of the experiment compared with the chronic negative stress group and the blank control group.

[0049] Figure 10 GSEA results related to mitochondrial function in single-cell sequencing data on day 14 compared with the mixed stimulation control group and the blank control group.

[0050] Figure 11 GSEA results related to synaptic function in single-cell sequencing data on the 14th day in the chronic negative stress group compared with the blank control group.

[0051] Figure 12 GSEA results related to synaptic function in single-cell sequencing data on day 14 compared with the mixed stimulation control group and the blank control group.

[0052] Figure 13 Key neurotrophic factors detected by single-cell sequencing in the chronic negative stress group, mixed stimulation control group and blank control group on the 14th day of the experiment BDNF The relative expression level comparison results were shown. DETAILED DESCRIPTION

[0053] The raw materials and equipment used in the present invention are all known products and are obtained by purchasing commercially available products.

[0054] Example 1: Method for constructing a chronic unpredictable negative stress model in vitro based on negative emotion encoding electrical stimulation

[0055] The device and experimental process of the present invention are as follows Figure 1 shown.

[0056] 1. Emotional Speech Signal Coding and Electrical Stimulation

[0057] The 200 speech clips containing emotional characteristics of "happy," "neutral," and "sad" used in the experiment were taken from a standardized emotional speech database to ensure standardized emotion classification and strong data reproducibility, thereby improving the reliability and universality of the experimental data. Feature extraction and electrical encoding were performed on the 200 speech clips containing emotional characteristics of "happy," "neutral," and "sad." After removing ambient noise through bandpass filtering, Mel-Frequency Cepstral Coefficients (MFCCs) were extracted using the Python-based Librosa audio processing library. Each MFCC coefficient corresponds to the energy distribution of a specific frequency bin. A 64-dimensional MFCC feature vector (selected based on the MEA chip used) was extracted and normalized, then thresholded (threshold = 15) to generate a binary encoding sequence. Segments with feature values ​​above a preset threshold were converted to biphasic square wave electrical stimulation, while segments below the threshold corresponded to silence. 100 speech clips with the emotional characteristic of "sad" were encoded, and 50 each for the emotional characteristics of "happy" and "neutral." By using a rich set of emotional stimulus signals, a multidimensional emotional electrical stimulation input set was constructed.

[0058] 2. Configuration and Unpredictable Settings of the Microelectrode Array Stimulation System

[0059] 1. Microelectrode Array (MEA) Selection

[0060] The MEA system (Axion biosystem) suitable for in vitro brain organoid culture was used, which has adjustable electrode distribution and number to ensure multi-point stimulation and recording of brain organoids.

[0061] 2. Stimulation System Configuration

[0062] (1) Use a computer or dedicated controller to import the emotional electrical stimulation input set into the MEA system.

[0063] (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 number of stimulations per day (10-30 times).

[0064] In actual implementation, 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.

[0065] 3. Unpredictable settings

[0066] (1) Electrical stimulation encoded with the emotion “sadness” was continuously input into the brain organoids for 14 days or longer to simulate chronic negative stress;

[0067] (2) For 14 consecutive days or longer, random stimulation order, random stimulation voltage intensity, random stimulation frequency, and random stimulation number are used to make the brain organoids unpredictable in the stimulation pattern.

[0068] 3. Cultivation and Stimulation of Brain Organoids

[0069] 1. Brain Organoid Culture

[0070] Based on human pluripotent stem cells (induced pluripotent stem cells, iPSCs or embryonic stem cells, ESCs), using ( E8, Gibco ) culture medium to maintain stem cell culture, and when the cell state is appropriate, neural differentiation and three-dimensional suspension culture are performed, using standardized 3D brain organoid induction medium ( STEMdiff™ Cerebral Organoid Kit, STEMCELL Technologies), Long-term culture was performed according to the instructions of the culture medium until the organoids reached D120, and initially mature brain organoids were obtained.

[0071] 2. Integration of Brain Organoids and MEAs

[0072] The above-mentioned cultured and initially mature brain organoids were transferred to the MEA system ( Maestro Pro, Axion BioSystems ) on a matching chip. MEA's high-throughput microelectrodes can simultaneously collect electrical signals and deliver electrical stimulation to brain organoids. To ensure full contact between brain organoids and electrodes and obtain a stable signal coupling relationship, the surface of the MEA culture plate is coated with 5% Matrigel ( Matrigel, Corning ) solution for coating. Organoids were then cultured in MEA plates at 37°C and 5% CO2 to allow them to gradually adapt and stabilize in their new environment. After the organoids had adapted to the MEA plate for 14 days and their spontaneous discharges had established tight connections with the microelectrode chip, subsequent negative emotional stimulation experiments were conducted.

[0073] 3. Stimulation and Observation Cycle

[0074] Start a 14-day (can be extended if needed) emotional stimulation experiment:

[0075] Experimental Group (Chronic Negative Stress Group): During the continuous stimulation cycle, the voltage intensity, frequency, pulse width, and number of stimulations applied daily, encompassing only the emotion encoding sadness, were randomly varied within a predetermined range to reflect the unpredictable nature of negative stress. The "random number of stimulations" refers to the number of randomly selected electrical stimulation events per day. This means the number of brain organoid stimulations per day also varied randomly, fully ensuring the randomness and unpredictability of the stimulation pattern.

[0076] Control group 1 (mixed stimulus control group): randomly given unpredictable stress including happy / neutral emotions every day, and the range of variation of this unpredictable stress was consistent with that of the experimental group;

[0077] Control group 2 (blank control group): No emotion encoding stimulation was applied, and only brain organoids were integrated with MEA plates and cultured with conventional nutrition.

[0078] 4. Data Collection and Analysis

[0079] Using MATLAB 2024b, we analyzed the electrode discharge signals recorded by MEA, focusing on the following network graph theory and decomposition metrics: significant edge weight mean, modularity score, top 10% principal components (PC), and number of non-negative matrix factorization components (Num NMFcomponents). We also evaluated 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:

[0080] (1) Event detection and functional connectivity calculation: First, use threshold detection or template matching methods to identify and record the time point and amplitude of spike discharges; further perform spike sorting as needed to distinguish the discharges of different neurons under the same electrode; calculate the correlation coefficient, mutual information or phase synchronization of the discharge rate time series between any two electrodes (or neurons) to obtain a matrix reflecting the strength of functional connectivity.

[0081] (2) Network construction and graph theory indicators: The obtained functional connectivity matrix is ​​thresholded, and the edges that are statistically significant or have high strength are retained to form a weighted or binary network adjacency matrix; the significant edge weight mean is calculated to measure the overall level of high-strength edges in the network; the commonly used community discovery algorithm (Louvain) is used to segment the network to obtain the modularity score, which reflects the degree of network partitioning and subgroup integration.

[0082] (3) Multi-dimensional degradation analysis: Use principal component analysis (PCA) to reduce the dimensionality of the data after the high-dimensional time series or connection matrix is ​​expanded, calculate the proportion of the first few principal components, and extract the top 10% principal components (Top 10% PC) as an important indicator to measure the dominant mode; iteratively solve the non-negative matrix factorization (NMF) to determine the number of non-negative matrix factorization components (Num NMF components) that can significantly describe the network activity after decomposition, so as to quantify the diversity of network activities.

[0083] (4) Single-cell sequencing analysis: Single-cell nuclear suspensions were prepared from brain organoid samples collected on the 14th day of the experiment. First, tissue processing was performed to extract cell nuclei and obtain single-cell nuclear suspensions. The quality of the nuclei was then checked. Next, gel beads containing barcode information were combined with a mixture of cell nuclei suspension and enzymes. Then, gel beads-in-emulsion (GEMs) containing single cell nuclei were formed in the oil phase through a microfluidic system. PCR amplification was performed using cDNA as a template to construct a standard sequencing library. Finally, PCR amplification was performed to obtain the final DNA library, and the constructed library was subjected to high-throughput sequencing using the Illumina sequencing platform.

[0084] (5) Bioinformatics analysis process: First, the original sequencing data were processed using 10x Genomics official analysis software Cell Ranger, including data filtering, sequence alignment (alignment to the reference genome), gene transcript quantification, and cell identification, and finally the gene expression matrix of each cell (nucleus) was obtained. Furthermore, the gene expression matrix output by CellRanger was further analyzed using the R software package Seurat. The main steps include: cell filtering, standardization and normalization of gene expression data, cell subpopulation clustering analysis, analysis of differentially expressed genes in each subpopulation, and marker gene screening 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) were performed based on differentially expressed genes or specific gene sets to deeply analyze the changing characteristics of brain organoids in cell composition, gene expression profiles, and biological functions under different experimental conditions.

[0085] 5. Experimental Results

[0086] (1) Results of MEA neural network analysis of brain organoids:

[0087] 1) The average weight of the significant connection edge increases: the results are as follows Figure 2 As shown in the figure, on the 14th day, the average weight of the "significant edges" in the functional connectivity map of the chronic negative stress group was higher than that of the blank control group, indicating that the synchronization between neurons and synaptic plasticity were enhanced.

[0088] 2) Network modularity increases: The results are as follows Figure 3 As shown, the chronic negative stress group caused the brain organoid network to become more fragmented and showed a higher modularity score, indicating that the cohesion between local neural clusters was enhanced, while the connection between different modules was relatively reduced, which is consistent with the common phenomenon of "abnormal differentiation of functional networks" under chronic negative stress.

[0089] 3) The proportion of the first 10% principal components increases: the results are as follows Figure 4 As shown in Figure 3, the PCA results showed that the proportion of the overall variance in the main components of the chronic negative stress group increased, which means that the network activity is more concentrated in several dominant patterns, reflecting the "coreization" or "stereotyping" trend of network dynamics under stress.

[0090] 4) Non-negative matrix decomposition increases the number of components: the result is as follows Figure 5 As shown in the figure, 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 synchronization was strengthened, local neural clusters showed diversified or differentiated characteristics.

[0091] (2) Single-cell transcriptome sequencing results of brain organoids:

[0092] To further explore the molecular mechanism behind the network functional reconstruction observed by the above-mentioned MEA and verify the biological effects of the model, we performed single-cell transcriptome sequencing analysis on each group of brain organoids on the 14th day of the experiment.

[0093] 1) GO functional enrichment analysis of differentially expressed genes: Differentially expressed genes were analyzed between the experimental group and the blank control group. The GO functional enrichment results showed that ( Figure 6 ), differentially expressed genes were significantly enriched in yellow-labeled entries related to "synapse GO:0045202, glutamatergic synapse GO:0098978 and dendrite GO:0030425," and blue-labeled entries related to protein synthesis, such as "ribosome GO:0005840, translation GO:0006412." This preliminarily suggests that chronic negative stress primarily affects protein translation, synthesis-related biological processes, and neuronal synaptic morphology and function in brain organoids.

[0094] 2) Changes in core protein synthesis and respiratory function biological pathways: GSEA analysis further revealed systematic changes at the pathway level. Figure 7 As shown, in the experimental group (chronic negative stress group), pathways related to ribosomes (e.g., "ribosomes," "cytoplasmic ribosomes," "cytoplasmic large ribosomal subunits," etc.) and pathways related to protein translation (e.g., "cytoplasmic translation," "translation

[0095] ” etc.) showed significant downregulation, suggesting that the protein synthesis capacity related to ribosomes and translation may be inhibited. In the mixed stimulation control group, the same protein translation-related pathway showed significant upregulation ( Figure 8 Similarly, pathways related to respiratory function (e.g., "cellular respiration," "oxidative phosphorylation," "mitochondrial proton transport 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, while these pathways were upregulated in the mixed stimulation control group ( Figure 10 These results suggest that chronic negative stress may specifically impair basal cellular biosynthesis and energy metabolism, consistent with findings in animal studies, while mixed "neutral" and "happy" emotional stimulation may produce different or even opposite biological effects.

[0096] 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 the chronic negative stress group ( Figure 11 ) and mixed stimulation control group ( Figure 12 ) Compared with the blank control group, there was a significant upregulation enrichment of pathways related to synaptic function. However, this upregulation showed obvious neuron type specificity: in the chronic negative stress group, the most significantly upregulated pathways were mainly related to the synaptic function of GABAergic neurons and glutamatergic neurons; while in the mixed stimulation control group, the most significantly upregulated pathways were more related to the synaptic function of dopaminergic neurons. This finding not only reveals that stimuli of different emotional natures may affect synaptic plasticity by regulating different neurotransmitter systems, but also highlights the unique advantages of single-cell sequencing in analyzing cellular heterogeneous responses. These changes in synaptic gene expression, especially the upregulation of synaptic function of inhibitory and excitatory neurons in the experimental group, contributed to the enhanced functional connectivity detected by MEA ( Figure 2 ) and network modularity ( Figure 3 ) provides direct molecular mechanistic support.

[0097] 4) Detection of the expression of key neurotrophic factors for synaptic plasticity: This invention further detected brain-derived neurotrophic factor (BDNF) which is closely related to synaptic plasticity and neural network remodeling. BDNF ) expression level. Figure 13 As shown in Figure 2, compared with the blank control group, the brain organoids in the chronic negative stress group had BDNF The relative expression level showed an increase, while the mixed stimulation control group BDNF The expression level of β-catenin was further increased in the chronic negative stress group. BDNF It plays an important role in neural plasticity, learning and memory, and stress response regulation. The changes in its expression levels further demonstrate that this model successfully induces molecular phenotypes associated with chronic stress.

[0098] (3) Comprehensive discussion and model validation: This study combined MEA network function analysis and single-cell transcriptome sequencing to double-validate the chronic unpredictable negative stress brain organoid model constructed based on emotional speech coding electrical stimulation. MEA results showed that chronic negative stimulation significantly changed the macroscopic neural network activity pattern of brain organoids, manifested as enhanced functional connectivity, intensified network modularity, and solidified neural network activity patterns. Single-cell sequencing results revealed the biological basis of these network changes at the microscopic molecular level: chronic negative stimulation specifically inhibited pathways related to ribosome and mitochondrial function, while upregulating the expression of synaptic function-related genes in specific types of neurons (GABAergic and glutamatergic), and affecting key neurotrophic factors. BDNF These molecular findings, particularly the alterations in synaptic function, provide a direct mechanistic explanation for the network functional remodeling observed in MEA.

[0099] Compared with existing findings from animal models (such as abnormal functional connectivity and altered network modularity) and neuroimaging studies of patients with depression, the in vitro model constructed in this invention demonstrates good consistency and correlation at the level of network function and molecular mechanisms (references below). The introduction of single-cell sequencing has greatly enhanced our understanding of the model's internal cellular heterogeneity and complex molecular regulatory networks.

[0100] In summary, this study, through dual validation using MEA and single-cell sequencing, not only successfully constructed an in vitro brain organoid model capable of simulating the core features of chronic, unpredictable negative stress, but also deeply revealed the underlying mechanisms of neural network functional reconstruction and the cellular and molecular basis. This comprehensive research paradigm transcends the limitations of traditional single technology platforms and provides a precise, efficient, and highly relevant innovative research platform for studying the pathogenesis of psychiatric disorders (particularly those related to chronic stress, such as depression and anxiety) and developing novel treatment strategies.

[0101] This study, using both MEA and single-cell sequencing, confirmed the effectiveness and accuracy of the chronic unpredictable negative stress model in brain organoids. This comprehensive research paradigm transcends the limitations of traditional single technology platforms, significantly improving the accuracy, reproducibility, and clinical relevance of model construction. It is expected to become an important new in vitro research platform for psychiatric disorders.

Claims

1. A method for constructing a chronic unpredictable negative stress model, characterized by: The method comprises the following steps: (1) Extract features of negative emotional speech signals and convert emotional speech signals into negative emotional electrical stimulation signals; (2) Transferring brain organoids to a microelectrode array system on a chip for culture; (3) introducing the negative emotion electrical stimulation signal of step (1) into the microelectrode array system to continuously input negative emotion electrical stimulation to the brain organoid to obtain a chronic unpredictable negative stress model; The feature extraction in step (1) is to remove the environmental noise from the speech containing negative emotion features through bandpass filtering, and then use the Python-based Librosa audio processing library to extract the Mel-frequency cepstral coefficients, each Mel-frequency cepstral coefficient corresponds to the energy distribution of a specific frequency interval, and extract the 64-dimensional Mel-frequency cepstral coefficient feature vector. After normalization, a binary coding sequence is generated through threshold judgment to obtain a negative emotion electrical stimulation signal; The brain organoids in step (2) are obtained by inducing neural differentiation of stem cells and culturing them in three-dimensional suspension until they mature, and the induction medium is a 3D brain organoid induction medium; The negative emotional electrical stimulation input in step (3) is a negative emotional electrical stimulation that varies randomly within the following parameter range: stimulation voltage 100-1000 mV, pulse width 100-500 μs, stimulation frequency 10-300 Hz, stimulation interval 20-100 s, and stimulation frequency 3-50 times per day; The negative emotional electrical stimulation continuously input in step (3) is randomly extracted from the negative emotional electrical stimulation signal obtained in step (1) and applied to the brain organoid in a random order; the continuous input is continuous input for more than 7 consecutive days.

2. The method according to claim 1, wherein: The speech containing negative emotional features in step (1) is a speech containing "sad" emotional features.

3. The method according to claim 1, wherein: The culture conditions in step (2) are 1-10% matrix gel, 20-50° C., and 1-10% CO 2 .

4. A chronic unpredictable negative stress model, characterized by: The chronic unpredictable negative stress model is constructed according to the method according to any one of claims 1 to 3.

5. Use of the method according to any one of claims 1 to 3 in constructing an in vitro chronic unpredictable negative stress model.

6. Use of the method according to any one of claims 1 to 3 and the chronic unpredictable negative stress model according to claim 4 in the study of depression and anxiety.

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