CryoVesNet: A dedicated framework for synaptic vesicle segmentation in cryo-electron tomograms.

Cryo-electron tomography (cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is highly complex, requiring image segmentation for visualization and quantification of subcellular structures. Due to noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Cryo-ET study of the spatial organization of SVs and their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Accurate SV segmentation is a prerequisite to obtaining a faithful connectivity representation. Hundreds of SVs are present in a synapse, and their manual segmentation is a bottleneck. We addressed this by designing a workflow consisting of a convolutional network followed by post-processing steps. Alongside, we provide an interactive tool for accurately segmenting spherical vesicles. Our pipeline can in principle segment spherical vesicles in any cell type as well as extracellular and in vitro spherical vesicles.

Synaptic interactions between stellate cells and parvalbumin interneurons in layer 2 of the medial entorhinal cortex are organized at the scale of grid cell clusters.

Interactions between excitatory and inhibitory neurons are critical to computations in cortical circuits but their organization is difficult to assess with standard electrophysiological approaches. Within the medial entorhinal cortex, representation of location by grid and other spatial cells involves circuits in layer 2 in which excitatory stellate cells interact with each other via inhibitory parvalbumin expressing interneurons. Whether this connectivity is structured to support local circuit computations is unclear. Here, we introduce strategies to address the functional organization of excitatory-inhibitory interactions using crossed Cre- and Flp-driver mouse lines to direct targeted presynaptic optogenetic activation and postsynaptic cell identification. We then use simultaneous patch-clamp recordings from postsynaptic neurons to assess their shared input from optically activated presynaptic populations. We find that extensive axonal projections support spatially organized connectivity between stellate cells and parvalbumin interneurons, such that direct connections are often, but not always, shared by nearby neurons, whereas multisynaptic interactions coordinate inputs to neurons with greater spatial separation. We suggest that direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters, with local modules defined by excitatory-inhibitory connectivity, while indirect interactions may coordinate activity at the scale of grid cell modules.

Structural Basis for Histaminergic Regulation of Neural Circuits in the Mouse Olfactory Bulb.

Odor information is modulated by centrifugal inputs from other brain regions to the olfactory bulb (OB). Neurons containing monoamines, such as serotonin, acetylcholine, and noradrenaline, are well known as centrifugal inputs; however, the role of histamine, which is also present in the OB, is not well understood. In this study, we examined the histaminergic neurons projecting from the hypothalamus to the OB. We used an antibody against histidine decarboxylase (HDC), a synthesizing enzyme of histamine, to identify histaminergic neurons and assess their localization within the OB and the ultrastructure of their fibers and synapses using multiple immunostaining laser microscopy, ultra-high voltage electron microscopy (EM), and EM to confirm their relationships with other neurons. To further identify the origin nucleus of the histaminergic neurons projecting to the OB, we injected the retrograde tracer FluoroGold and analyzed the pathway to the OB anterogradely. HDC-immunoreactive (-ir) fibers were abundant in the olfactory nerve (ON) layer compared to other monoamines. HDC-ir neurons received asymmetrical synapses from ONs and formed synapses containing pleomorphic vesicles with variable postsynaptic densities to non-ON elements, thus forming serial synapses. We also confirmed that histaminergic neurons project from the rostral ventral tuberomammillary nucleus to the granule cell layer of the OB and, for the first time, successfully visualized their axons from the hypothalamus to the OB. These findings indicate that histamine may regulate odor discrimination in the OB, suggesting a regulatory relationship between hypothalamic function and olfaction. We thus elucidate morphological mechanisms with tuberomammillary nucleus-derived histaminergic neurons involved in olfactory information.

Neuronal BAG3 attenuates tau hyperphosphorylation, synaptic dysfunction, and cognitive deficits induced by traumatic brain injury via the regulation of autophagy-lysosome pathway.

Growing evidence supports that early- or middle-life traumatic brain injury (TBI) is a risk factor for developing Alzheimer's disease (AD) and AD-related dementia (ADRD). Nevertheless, the molecular mechanisms underlying TBI-induced AD-like pathology and cognitive deficits remain unclear. In this study, we found that a single TBI (induced by controlled cortical impact) reduced the expression of BCL2-associated athanogene 3 (BAG3) in neurons and oligodendrocytes, which is associated with decreased proteins related to the autophagy-lysosome pathway (ALP) and increased hyperphosphorylated tau (ptau) accumulation in excitatory neurons and oligodendrocytes, gliosis, synaptic dysfunction, and cognitive deficits in wild-type (WT) and human tau knock-in (hTKI) mice. These pathological changes were also found in human cases with a TBI history and exaggerated in human AD cases with TBI. The knockdown of BAG3 significantly inhibited autophagic flux, while overexpression of BAG3 significantly increased it in vitro. Specific overexpression of neuronal BAG3 in the hippocampus attenuated AD-like pathology and cognitive deficits induced by TBI in hTKI mice, which is associated with increased ALP-related proteins. Our data suggest that targeting neuronal BAG3 may be a therapeutic strategy for preventing or reducing AD-like pathology and cognitive deficits induced by TBI.

Geometric Scaling Law in Real Neuronal Networks.

We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling law in neuronal connection probability relative to spatial distance. This power-law behavior significantly differs from the exponential distance rule previously observed in coarse-grained brain networks. We demonstrate that the geometric scaling law carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Perturbing either the empirical probability model's parameters or its type results in the loss of these advantageous properties. Furthermore, we derive an explicit quantitative predictor for neuronal connectivity, incorporating only interneuronal distance and neurons' in and out degrees. Our findings establish a direct link between brain geometry and topology, shedding lights on the understanding of how the brain operates optimally within its confined space.

Discovery of a novel piperazine derivative, cmp2: a selective TRPC6 activator suitable for treatment of synaptic deficiency in Alzheimer's disease hippocampal neurons.

Alzheimer disease (AD) is characterized by progressive loss of memory. Synaptic loss is now the best correlate of cognitive dysfunction in patients with Alzheimer's disease. Thus, restoration or limitation of synapse loss is a promising strategy for pharmacotherapy of AD. N-N substituted piperazines are widely used chemical compounds for drug interventions to treat different illnesses including CNS diseases such as drug abuse, mental and anxiety disorders. Piperazine derivatives are small molecules that are usually well tolerated and cross blood brain barrier (BBB). Thus, disubstituted piperazines are good tools for searching and developing novel disease-modifying drugs. Previously, we have determined the piperazine derivative, 51164, as an activator of TRPC6 in dendritic spines. We have demonstrated synaptoprotective properties of 51164 in AD mouse models. However, 51164 was not able to cross BBB. Within the current study, we identified a novel piperazine derivative, cmp2, that is structurally similar to 51164 but is able to cross BBB. Cmp2 binds central part of monomeric TRPC6 in similar way as hypeforin does. Cmp2 selectively activates TRPC6 but not structurally related TRPC3 and TRPC7. Novel piperazine derivative exhibits synaptoprotective properties in culture and slices and penetrates the BBB. In vivo study indicated cmp2 (10 mg/kg I.P.) reversed deficits in synaptic plasticity in the 5xFAD mice. Thus, we suggest that cmp2 is a novel lead compound for drug development. The mechanism of cmp2 action is based on selective TRPC6 stimulation and it is expected to treat synaptic deficiency in hippocampal neurons.

A Drosophila computational brain model reveals sensorimotor processing.

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease.

BACKGROUND: Blood-based biomarkers are gaining grounds for the detection of Alzheimer's disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aß)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel. METHODS: The NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aß40, and Aß42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aß pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers. RESULTS: NULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aß pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aß-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aß PET-dependent yearly increases in Aß-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype. CONCLUSIONS: Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

LRRTM2 controls presynapse nano-organization and AMPA receptor sub-positioning through Neurexin-binding interface.

Synapses are organized into nanocolumns that control synaptic transmission efficacy through precise alignment of postsynaptic neurotransmitter receptors and presynaptic release sites. Recent evidence show that Leucine-Rich Repeat Transmembrane protein LRRTM2, highly enriched and confined at synapses, interacts with Neurexins through its C-terminal cap, but the role of this binding interface has not been explored in synapse formation and function. Here, we develop a conditional knock-out mouse model (cKO) to address the molecular mechanisms of LRRTM2 regulation, and its role in synapse organization and function. We show that LRRTM2 cKO specifically impairs excitatory synapse formation and function in mice. Surface expression, synaptic clustering, and membrane dynamics of LRRTM2 are tightly controlled by selective motifs in the C-terminal domain. Conversely, the N-terminal domain controls presynapse nano-organization and postsynapse AMPAR sub-positioning and stabilization through the recently identified Neurexin-binding interface. Thus, we identify LRRTM2 as a central organizer of pre- and post- excitatory synapse nanostructure through interaction with presynaptic Neurexins.

Activity patterns in ring networks of quadratic integrate-and-fire neurons with synaptic and gap junction coupling.

We consider a ring network of quadratic integrate-and-fire neurons with nonlocal synaptic and gap junction coupling. The corresponding neural field model supports solutions such as standing and traveling waves, and also lurching waves. We show that many of these solutions satisfy self-consistency equations which can be used to follow them as parameters are varied. We perform numerical bifurcation analysis of the neural field model, concentrating on the effects of varying gap junction coupling strength. Our methods are generally applicable to a wide variety of networks of quadratic integrate-and-fire neurons.

Searching for long timescales without fine tuning.

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs.

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75-90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

THE EFFECTS OF HYDROCORTISONE ON SYNAPTIC PROCESSES IN PARKINSON'S DISEASE UNDERLYING THE POTENTIAL THERAPEUTIC STRATEGIES.

The study was carried out electrophysiological effects of hydrocortisone for protection on the prelimbic cortex (PrL) neurons in rats, particularly in response to high-frequency stimulation (HFS) of the Caudate-Putamen nuclear complex (CPu) on the models of Parkinson's disease (PD). The study involved 19 rats of the Albino line, each weighing 250 gr. The rats were divided into three experimental groups: intact, rotenone model of Parkinson's disease (PD), and rats with PD but treated with hydrocortisone for protection. Extracellular recording was conducted to measure the impulse activity of single neurons in the prelimbic cortex (PrL) particularly in response to high-frequency stimulation (HFS) of the Caudate-Putamen nuclear complex (CPu) on the models of PD and PD treated with hydrocortisone for protection. In rats with the PD model, there was a decrease in post-stimulus synaptic depressor tetanic effects compared to the norm. This means that the ability of synapses to depress their activity after stimulation was reduced in PD. Conversely, excitatory effects increased in PD rats compared to the norm. This indicates an increase in the excitatory response of neurons in the PD model. When hydrocortisone was applied in PD rats, the frequency of impulse activity dropped sharply, even falling below the levels observed in the normal condition. This indicates that hydrocortisone treatment mitigated the heightened neural activity induced by PD, possibly returning it to a more normal state. Overall, these findings suggest that PD alters synaptic responses and neural activity in the PrL, and hydrocortisone treatment seems to reverse some of these effects.

Developing forebrain synapses are uniquely vulnerable to sleep loss.

Sleep is an essential behavior that supports lifelong brain health and cognition. Neuronal synapses are a major target for restorative sleep function and a locus of dysfunction in response to sleep deprivation (SD). Synapse density is highly dynamic during development, becoming stabilized with maturation to adulthood, suggesting sleep exerts distinct synaptic functions between development and adulthood. Importantly, problems with sleep are common in neurodevelopmental disorders including autism spectrum disorder (ASD). Moreover, early life sleep disruption in animal models causes long-lasting changes in adult behavior. Divergent plasticity engaged during sleep necessarily implies that developing and adult synapses will show differential vulnerability to SD. To investigate distinct sleep functions and mechanisms of vulnerability to SD across development, we systematically examined the behavioral and molecular responses to acute SD between juvenile (P21 to P28), adolescent (P42 to P49), and adult (P70 to P100) mice of both sexes. Compared to adults, juveniles lack robust adaptations to SD, precipitating cognitive deficits in the novel object recognition task. Subcellular fractionation, combined with proteome and phosphoproteome analysis revealed the developing synapse is profoundly vulnerable to SD, whereas adults exhibit comparative resilience. SD in juveniles, and not older mice, aberrantly drives induction of synapse potentiation, synaptogenesis, and expression of perineuronal nets. Our analysis further reveals the developing synapse as a putative node of convergence between vulnerability to SD and ASD genetic risk. Together, our systematic analysis supports a distinct developmental function of sleep and reveals how sleep disruption impacts key aspects of brain development, providing insights for ASD susceptibility.

Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.

Brain-inspired machine intelligence research seeks to develop computational models that emulate the information processing and adaptability that distinguishes biological systems of neurons. This has led to the development of spiking neural networks, a class of models that promisingly addresses the biological implausibility and the lack of energy efficiency inherent to modern-day deep neural networks. In this work, we address the challenge of designing neurobiologically motivated schemes for adjusting the synapses of spiking networks and propose contrastive signal-dependent plasticity, a process which generalizes ideas behind self-supervised learning to facilitate local adaptation in architectures of event-based neuronal layers that operate in parallel. Our experimental simulations demonstrate a consistent advantage over other biologically plausible approaches when training recurrent spiking networks, crucially side-stepping the need for extra structure such as feedback synapses.

Ultrastructural characterization of hippocampal inhibitory synapses under resting and stimulated conditions.

The present study uses electron microscopy to document ultrastructural characteristics of hippocampal GABAergic inhibitory synapses under resting and stimulated conditions in three experimental systems. Synaptic profiles were sampled from stratum pyramidale and radiatum of the CA1 region from (1) perfusion fixed mouse brains, (2) immersion fixed rat organotypic slice cultures, and from (3) rat dissociated hippocampal cultures of mixed cell types. Synapses were stimulated in the brain by a 5 min delay in perfusion fixation to trigger an ischemia-like excitatory condition, and by treating the two culture systems with 90 mM high K+ for 2-3 min to depolarize the neurons. Upon such stimulation conditions, the presynaptic terminals of the inhibitory synapses exhibited similar structural changes to those seen in glutamatergic excitatory synapses, with depletion of synaptic vesicles, increase of clathrin-coated vesicles and appearance of synaptic spinules. However, in contrast to excitatory synapses, no structural differences were detected in the postsynaptic compartment of the inhibitory synapses upon stimulation. There were no changes in the appearance of material associated with the postsynaptic membrane or the length and curvature of the membrane. Also no change was detected in the labeling density of gephyrin, a GABAergic synaptic marker, lining the postsynaptic membrane. Furthermore, virtually all inhibitory synaptic clefts remained rigidly apposed, unlike in the case of excitatory synapses where ~ 20-30% of cleft edges were open upon stimulation, presumably to facilitate the clearance of neurotransmitters from the cleft. The fact that no open clefts were induced in inhibitory synapses upon stimulation suggests that inhibitory input may not need to be toned down under these conditions. On the other hand, similar to excitatory synapse, EGTA (a calcium chelator) induced open clefts in ~ 18% of inhibitory synaptic cleft edges, presumably disrupting similar calcium-dependent trans-synaptic bridges in both types of synapses.

Impaired axon initial segment structure and function in a model of ARHGEF9 developmental and epileptic encephalopathy.

Developmental and epileptic encephalopathies (DEE) are rare but devastating and largely intractable childhood epilepsies. Genetic variants in ARHGEF9, encoding a scaffolding protein important for the organization of the postsynaptic density of inhibitory synapses, are associated with DEE accompanied by complex neurological phenotypes. In a mouse model carrying a patient-derived ARHGEF9 variant associated with severe disease, we observed aggregation of postsynaptic proteins and loss of functional inhibitory synapses at the axon initial segment (AIS), altered axo-axonic synaptic inhibition, disrupted action potential generation, and complex seizure phenotypes consistent with clinical observations. These results illustrate diverse roles of ARHGEF9 that converge on regulation of the structure and function of the AIS, thus revealing a pathological mechanism for ARHGEF9-associated DEE. This unique example of a neuropathological condition associated with multiple AIS dysfunctions may inform strategies for treating neurodevelopmental diseases.

Tissue-specific knockout in the Drosophila neuromuscular system reveals ESCRT's role in formation of synapse-derived extracellular vesicles.

Tissue-specific gene knockout by CRISPR/Cas9 is a powerful approach for characterizing gene functions during development. However, this approach has not been successfully applied to most Drosophila tissues, including the Drosophila neuromuscular junction (NMJ). To expand tissue-specific CRISPR to this powerful model system, here we present a CRISPR-mediated tissue-restricted mutagenesis (CRISPR-TRiM) toolkit for knocking out genes in motoneurons, muscles, and glial cells. We validated the efficacy of CRISPR-TRiM by knocking out multiple genes in each tissue, demonstrated its orthogonal use with the Gal4/UAS binary expression system, and showed simultaneous knockout of multiple redundant genes. We used CRISPR-TRiM to discover an essential role for SNARE components in NMJ maintenance. Furthermore, we demonstrate that the canonical ESCRT pathway suppresses NMJ bouton growth by downregulating retrograde Gbb signaling. Lastly, we found that axon termini of motoneurons rely on ESCRT-mediated intra-axonal membrane trafficking to release extracellular vesicles at the NMJ. Thus, we have successfully developed an NMJ CRISPR mutagenesis approach which we used to reveal genes important for NMJ structural plasticity.

Dexamethasone as an emerging environmental pollutant: Disruption of cholesterol-dependent synaptogenesis in the hippocampus and subsequent neurobehavioral impacts in offspring.

When fetuses are exposed to abnormally high levels of glucocorticoids in utero, irreversible damage to neuronal synaptogenesis occurs, leading to long-term cognitive and emotional behavioral abnormalities after birth. In this study, we investigated how maternal exposure to a novel environmental pollutant-synthetic glucocorticoid dexamethasone-affects offspring cognitive and emotional behaviors enduringly. We noted that offspring subjected to maternal dexamethasone exposure (MDE) displayed cognitive and emotional neurobehavioral deficits beginning in infancy, and these impairments persisted into adulthood. The principal mechanism involves MDE-induced damage to hippocampal neuronal synapse formation in the offspring, primarily due to a cholesterol deficiency which destabilizes neuronal membranes, thereby affecting normal synapse formation and ultimately leading to cognitive and emotional deficiencies. Specifically, we demonstrated abnormal activation of glucocorticoid receptors in hippocampal astroglial cells of MDE offspring, which triggers changes in the miR-450a-3p/HAT1/ABCG1 signaling axis, causing impaired cholesterol efflux in astroglial cells and insufficient cholesterol supply to neurons, further impairing synaptogenesis. This research not only underscores the significant impact of prenatal environmental pollutants on long-term health outcomes in offspring but also broadens our understanding of how prenatal exposure to glucocorticoids affects brain development in the progeny, providing new insights for interventions in neurodevelopmental and psychiatric disorders of fetal origin.