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In this report, we describe the development of a modified adeno‐associated virus (AAV) capsid and promoter for transduction of retinal ON‐bipolar cells. The bipolar cells, which are post‐synaptic to the photoreceptors, are important retinal targets for both basic and preclinical research. In particular, a therapeutic strategy under investigation for advanced forms of blindness involves using optogenetic molecules to render ON‐bipolar cells light‐sensitive. Currently, delivery of adequate levels of gene expression is a limiting step for this approach. The synthetic AAV capsid and promoter described here achieves high level of optogenetic transgene expression in ON‐bipolar cells. This evokes high‐frequency (~100 Hz) spiking responses in ganglion cells of previously blind, rd1 , mice. Our vector is a promising vehicle for further development toward potential clinical use. Synopsis An engineered genetically modified adeno‐associated virus is shown here to efficiently and specifically drive the optogenetic molecule channelrhodopsin‐2 in ON‐bipolar cells, rendering them light sensitive and restoring retinal function in blind rd1 mice. A synthetic AAV capsid and modified bipolar‐cell specific promoter were developed to enhance transgene expression in retinal bipolar cells. The new virus transduced at least 59% of ON‐bipolar cells in mouse retina. In the blind rd1 mouse the virus was used to drive expression of optogenetic channels at levels high enough to elicit strong and robust spiking responses from the ganglion cells. This new virus‐promoter combination is thus presented as a candidate vector for clinical intervention in advanced forms of retinal degeneration. Graphical Abstract An engineered genetically modified adeno‐associated virus is shown here to efficiently and specifically drive the optogenetic molecule channelrhodopsin‐2 in ON‐bipolar cells, rendering them light sensitive and restoring retinal function in blind rd1 mice.

Brain organoids, or cerebral organoids, have become widely used to study the human brain in vitro . As pluripotent stem cell-derived structures capable of self-organization and recapitulation of physiological cell types and architecture, brain organoids bridge the gap between relatively simple two-dimensional human cell cultures and non-human animal models. This allows for high complexity and physiological relevance in a controlled in vitro setting, opening the door for a variety of applications including development and disease modeling and high-throughput screening. While technologies such as single cell sequencing have led to significant advances in brain organoid characterization and understanding, improved functional analysis (especially electrophysiology) is needed to realize the full potential of brain organoids. In this review, we highlight key technologies for brain organoid development and characterization, then discuss current electrophysiological methods for brain organoid analysis. While electrophysiological approaches have improved rapidly for two-dimensional cultures, only in the past several years have advances been made to overcome limitations posed by the three-dimensionality of brain organoids. Here, we review major advances in electrophysiological technologies and analytical methods with a focus on advances with applicability for brain organoid analysis.

[This corrects the article DOI: 10.3389/fncel.2023.1287089.].

The development of in vitro networks composed of distinct but interacting neuronal sub-populations (clusters) has advanced the study of emergent behaviors in neural networks as individual functional units. In a previous work, we developed an in vitro model of a network formed by four mutually interconnected clusters of rat embryonic cortical neurons cultured on multi-electrode arrays (MEA), where we observed recurring, spatially and temporally structured activation sequences. In the present study, we examined the effects of NMDAR blockade (MK-801) to modulate such temporal patterns. We found that MK-801 reduced the overall excitability of the network and disrupted the diversity of repeated activation patterns, while paradoxically increasing their temporal persistence. This led the network to transition from a dynamic regime characterized by frequent and flexible repetitions to one dominated by fewer, more stable and enduring activation motifs. Functional connectivity analysis further revealed a selective weakening of inter-cluster links alongside a strengthening of intra-cluster connections. This reorganization likely explains the observed reduction in activity propagation between clusters and the simultaneous emergence of more persistent activation sequences among clusters. Data suggest that clustered neural networks serve as semi-autonomous modules, capable of sustaining internal dynamics even under diminished excitatory drive. The stable repetition of activation patterns may reflect a functional “closure” within clusters, forming self-sustained loops that enable the reactivation of previously formed motifs. From a neuroengineering perspective, this model provides a versatile platform to explore how spatiotemporal neural dynamics underpin inter-network communication, information encoding, and complex cortical functions.

Neural lamination is a common feature of the CNS, with several subcellular structures, such as adherens junctions (AJs), playing a role in this process. The retina is also heavily laminated, but it remains unclear how laminar formation impacts retinal cell morphology, synapse integrity, and overall retinal function. In this study, we demonstrate that the loss of afadin, a key component of AJs, in mice leads to significant pathological changes. These include the disruption of outer retinal lamination and a notable decrease as well as mislocalization of photoreceptors, their outer segments, and photoreceptor synapses. Interestingly, despite these severe impairments, we recorded small local field potentials, including the a- and b-waves. We also classified retinal ganglion cells (RGCs) into ON, ON-OFF, and OFF types based on their firing patterns in response to light stimuli. Additionally, we successfully characterized the receptive fields of certain RGCs. Overall, these findings provide evidence that retinal circuit function can be partially preserved even when there are significant disruptions in both retinal lamination and photoreceptor synapses. Our results indicate that retinas with severely altered morphology still retain some capacity to process light stimuli.

At least 20 distinct retinal ganglion cell (RGC) types have been identified morphologically in the primate retina, but our understanding of the distinctive visual messages they send to various targets in the brain remains limited, particularly for naturalistic stimuli. Here, we use large-scale multi-electrode recordings to examine how multiple functionally distinct RGC types in the macaque retina respond to flashed natural images. Responses to white noise visual stimulation were used to functionally identify 936 RGCs of 12 types in three recordings. Each cell type was confirmed by the mosaic organization of receptive fields, and seven cell types were cross-identified between recordings. Responses to thousands of natural images were used to examine the average firing rate kinetics in each RGC type as well as the repertoire of distinct firing patterns that each type produced. The average response across images was highly stereotyped for cells of each type and distinct for cells of different types. The responses to natural images more clearly distinguished certain cell types than did the response to white noise stimulation. Moreover, the full repertoires of firing patterns produced by different cell types, assessed by their latency and duration, were largely distinct in most cases and in some cases non-overlapping. Together these data provide an overview of the diversity of RGC signals transmitted from the primate retina to the brain in natural viewing conditions.

Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity. At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. Available software offers semi-automated analysis for a fixed set of parameters that allow for the definition of spikes, bursts and network bursts. However, this analysis remains time-consuming, user-biased, and limited by pre-defined parameters. Here, we present autoMEA, software for machine learning-based automated burst detection in MEA datasets. We exemplify autoMEA efficacy on neuronal network activity of primary hippocampal neurons from wild-type mice monitored using 24-well multi-well MEA plates. To validate and benchmark the software, we showcase its application using wild-type neuronal networks and two different neuronal networks modeling neurodevelopmental disorders to assess network phenotype detection. Detection of network characteristics typically reported in literature, such as synchronicity and rhythmicity, could be accurately detected compared to manual analysis using the autoMEA software. Additionally, autoMEA could detect reverberations, a more complex burst dynamic present in hippocampal cultures. Furthermore, autoMEA burst detection was sufficiently sensitive to detect changes in the synchronicity and rhythmicity of networks modeling neurodevelopmental disorders as well as detecting changes in their network burst dynamics. Thus, we show that autoMEA reliably analyses neural networks measured with the multi-well MEA setup with the precision and accuracy compared to that of a human expert.

Spreading depolarization is a wave of neuronal and glial depolarization that propagates through brain tissue, triggering neuropeptide release and altered blood flow. It has been observed in ischemic stroke, traumatic brain injury, subarachnoid haemorrhage, epilepsy, and migraine aura. Spreading depolarization imposes a high energetic demand, and recovery impaired under metabolic substrate deficiency. Despite its clinical relevance, metabolic responses remain poorly understood, limiting therapeutic progress. We investigated metabolic effects of spreading depolarisation using an ex vivo brain slice model, aiming to characterise changes in intracellular calcium signalling, mitochondrial function, and central carbon metabolism, and to assess the impact of glucose deprivation. We further tested whether coenzyme Q10 could improve recovery under metabolically compromised conditions. Spreading depolarization increased mitochondrial activity and shifted metabolism toward anaerobic respiration and glycolysis. Glucose deprivation impaired recovery, inducing mitochondrial dysfunction and accumulation intermediates indicative of tricarboxylic acid cycle stalling and disrupted central carbon metabolism. Supplementing glucose-deprived brain slices with coenzyme Q10 shortened spreading depolarization repolarization duration, indicating enhanced metabolic recovery. These findings demonstrate that spreading depolarization imposes a significant metabolic burden, particularly under glucose limitation, and that mitochondrial-targeted interventions such as coenzyme Q10 may enhance tissue resilience in neurological disorders.

Reprogramming of human-induced pluripotent stem cells (iPSCs) and their differentiation into specific cell types, such as induced sensory-like neurons (iSNs), are critical for disease modeling and drug testing. However, the variability of cell populations challenges reliability and reproducibility. While various protocols for iSN differentiation exist, the development of non-iSN cells in these cultures remains an issue. Therefore, standardization of protocols is essential. This study aimed to improve iSN culture conditions by reducing the number of non-iSN cells while preserving the survival and quality of iSNs. iSNs were differentiated from a healthy control iPSC line using an established protocol. Interventions for protocol optimization included floxuridine (FdU) or 1-β-D-arabinofuranosyl-cytosine hydrochloride (AraC) treatment, magnetic-activated cell sorting (MACS), early cell passaging, and replating. Cell viability and iSN-to-total-cell-count ratio were assessed using a luminescent assay and immunocytochemistry, respectively. Passaging of cells during differentiation did not increase the iSN-to-total-cell-count ratio, and MACS of immature iSNs led to neuronal blebbing and reduced the iSN-to-total-cell-count ratio. Treatment with high concentrations and prolonged incubation of FdU or AraC resulted in excessive cell death. However, treatment with 10 μM FdU for 24 h post-differentiation showed the most selective targeting of non-iSN cells, leading to an increase in the iSN-to-total-cell count ratio without compromising the viability or functionality of the iSN population. Replating of iSNs shortly after seeding also helped to reduce non-iSN cells. In direct comparison with other methods, treatment with 10 μM FdU for 24 h after differentiation shows promise for improving iSN culture purity, which could benefit downstream applications in disease modeling and drug discovery. However, further investigations involving multiple iPSC lines and optimization of protocol parameters are warranted to fully exploit the potential of this method and enhance its reproducibility and applicability. Overall, this study provides valuable insights into optimizing culture conditions for iSN differentiation and highlights the importance of standardized protocols in iPSC-based research.

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo , functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a “bottom-up” 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.