Biology subjects

Sumi, T.

Publications and source records attributed to Sumi, T..

7 recordsLinked to original sources

bioRxiv2026

SCN1A haploinsufficiency disrupts neural progenitor networks and accelerates neurogenesis in human Dravet syndrome forebrain assembloids

Dravet syndrome is a severe childhood neurological disorder caused mostly due to mutations in the sodium channel gene SCN1A. Although the disorder is widely attributed to dysfunction of inhibitory neurons, growing evidence suggests that disease mechanisms may begin much earlier during brain development. To investigate the developmental consequences of SCN1A haploinsufficiency, we generated patient-derived dorsal-ventral forebrain assembloids that model key features of human corticogenesis. We found that SCN1A was expressed in ventricular zone neural progenitors and that its reduced expression was associated with impaired growth, disrupted ventricular zone organization, and progressive depletion of progenitor populations. We further demonstrated reduced progenitor proliferation and self-renewal, accelerated cell-cycle exit, and premature neuronal differentiation, leading to increased production of excitatory neurons and enhanced network excitability. Single cell transcriptomic analyses revealed reduced cycling radial glial cells, accelerated progenitor maturation, and dysregulation of gap junction and calcium signaling pathways. Following these findings, we examined progenitor calcium activity and found that SCN1A haploinsufficiency disrupted coordinated functional network organization, resulting in reduced recurrent connectivity and fragmentation of functional cellular assemblies. These abnormalities occurred despite preserved intrinsic electrophysiological properties of individual progenitors, indicating a defect in population-level coordination rather than single cell electrophysiology. Together, our findings identify disrupted progenitor communication as an early developmental consequence of SCN1A haploinsufficiency and suggest that abnormalities within neural progenitor populations contribute to altered corticogenesis, excitatory neurogenesis, and circuit hyperexcitability in Dravet syndrome. This work expands current disease models beyond interneuron dysfunction and highlights the developing ventricular zone as a potential therapeutic target.

neuroscience↗

bioRxiv2025

Online supervised learning of temporal patterns in biological neural networks under feedback control

In vitro biological neural networks (BNNs) provide a well-defined model system to constructively investigate how living cells interact with their environment to shape high-dimensional dynamics that could be used to generate a coherent temporal output, such as those required for motor control. Here, we developed a real-time closed-loop BNN system capable of generating periodic and chaotic temporal signals by integrating cultured cortical neurons with microfluidic devices and high-density microelectrode arrays. We show that training a simple linear decoder with fixed feedback weights enables the system to learn and autonomously generate diverse temporal patterns. When feedback was switched on, irregular activity in BNNs is transformed into low-dimensional, structured dynamics, producing coherent trajectories characterized by stable transitions between neural states. BNNs trained on different target frequencies--ranging from 4 to 30 s--could be trained to sustain oscillations at distinct frequencies, demonstrating their adaptability. Importantly, a top-down control of self-organized network formation with microfluidic devices is the key to suppress excessive synchronization and increase dynamical complexity in BNNs, facilitating the training and robust output generation. This work offers a biologically inspired platform for understanding the physical basis of cortical computation and for advancing energy-efficient neuromorphic computation. Significance StatementReservoir computing is a machine learning paradigm that exploits the transient dynamics of high-dimensional nonlinear systems. Although it was originally inspired by the mammalian brain and widely explored in physical systems, its implementations in biological neural networks (BNNs) have been limited due to their excessive connectivity and global synchrony in vitro. Here, we use microfluidic devices to construct modular, nonrandomly connected BNNs and integrate them with microelectrode arrays in a closed-loop reservoir computing environment. We show that the system can be trained to autonomously output various temporal signals, with the modular connectivity that is essential for learning. In vitro BNNs provide unique alternatives for physical reservoirs with dynamic adaptability.

neuroscience↗

bioRxiv2025

Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks

Inherent capacity to flexibly reorganize after injury is a hallmark of brain networks, and recent studies suggest that functional consequences of focal damage are strongly influenced by the networks non-random connectivity. Although many of these insights have been derived from animal models and computational simulations, experimental platforms that enable bottom-up investigations of the structure-function relationships underlying damage and recovery processes remain limited. In this study, we used polydimethylsiloxane microfluidic devices to construct hierarchically modular neuronal networks that mimic the architectural features of the mammalian cortex. Laser microdissection was employed to selectively sever intermodular connections, enabling controlled damage to either hub or peripheral connections. Damage to hub connections led to delayed recovery, requiring more than three days for correlations to re-emerge. In contrast, peripheral damage resulted in faster recovery. Experiments that induced repeated injury to neuronal networks further demonstrated that recovery primarily occurred through the formation of alternative pathways rather than restoration of the original connections. These findings highlight how topological features of neuronal networks shape their response to injury and subsequent reorganization, providing mechanistic insights into the intrinsic self-repair capacity of biological systems.

neuroscience↗

bioRxiv2025

Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling in vitro neurons

Impaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brains inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how the modular structure interacts with synaptic plasticity, most notably in the form of spike-timing-dependent plasticity (STDP), to define the damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal network in vitro bearing a modular structure. Consistent with the in vitro observations, the in silico numerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. We revealed that the modular structure confers robustness to injury, minimizes the decrease in neuronal activity, and promotes recovery via STDP. Finally, using the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of synchronous activity. Our work provides an experimental-numerical platform for predicting recovery in damaged neuronal networks and may help developing effective models for brain injury.

neuroscience↗

bioRxiv2024

A neural network model that generates salt concentration memory-dependent chemotaxis in Caenorhabditis elegans

A neuroanatomical minimal network model was revisited to elucidate the mechanism of salt concentration memory-dependent chemotaxis observed in Caenorhabditis elegans. C. elegans memorizes the salt concentration during cultivation, manifesting a pronounced taste preference for this concentration. The right-side head sensory neuron, designated ASER, exhibits a response to a decrease in salt concentration. The basal level of glutamate transmission from ASER has been demonstrated to transiently increase and decrease when the current environmental salt concentrations are lower and higher, respectively, than that during previous cultivation. Given the sensitivity of excitatory/inhibitory glutamate receptors expressed on the postsynaptic AIY interneurons, it can be anticipated that the ASER-AIY synaptic transmission will undergo a reversal due to alterations in the basal glutamate release. The neural network, derived from the hypothesis, reproduced the salt concentration memory-dependent preference behavior and revealed that the circuit downstream of ASE functions as a module that is responsible for salt klinotaxis.

neuroscience↗

bioRxiv2021

Implications of taxonomic and numerical resolution on DNA metabarcoding-based inference of benthic macroinvertebrate responses to river restoration

Exploring and clearly defining the level of taxonomic identification and quantification approaches for diversity and biomonitoring studies are essential, given its potential influence on the assessment and interpretation of ecological outcomes. In this study, we evaluated the response of benthic macroinvertebrate communities to the restoration or construction of gravel bars conducted in the dam-impacted Trinity River, with the non-dam influenced tributaries serving as the reference sites. We aim to evaluate the performance of different taxonomic and numerical (i.e., abundance vs. presence/absence data) resolutions of DNA metabarcoding with consequent comparison to morphology-based identification and how it affects assessment outcomes. DNA metabarcoding detected 93% of the morphologically identified individuals and provided finer taxonomic resolution. We also detected significant correlations between morphological sample abundance, biomass, and DNA metabarcoding read abundance. We observed a relatively high and significant congruence in macroinvertebrate community structure and composition between different taxonomic and numerical resolutions of both methods, indicating a satisfactory surrogacy between the two approaches and their varying identification levels and data transformation. Additionally, the community-environmental association were significant for all datasets but showed varying significant associations against the physicochemical parameters. Furthermore, both methods identified Simulium spp. as significant indicators of the dam-impacted gravel bars. Still, only DNA metabarcoding showed significant false discovery rate proving the methods robustness compared to morphology-based identification. Our observations imply that coarser taxonomic resolution could be highly advantageous to DNA metabarcoding-based applications in situations where the lack of taxonomic information, e.g., poor reference database, might severely affect the quality of biological assessments.

ecology↗

bioRxiv2020

Profiling the microbial community structure and functional diversity of a dam-regulated river undergoing gravel bar restoration

BackgroundRiver restoration efforts are expected to influence and change the diversity and functions of microbial communities following the recovery of habitat characteristics in the river ecosystem. The recreation or restoration of gravel bars in the Trinity River in California aims to rehabilitate the environmental heterogeneity downstream of the dam impounded channel. Here, we profiled the community composition, estimated diversity, and annotated putative metabolic functions of the sediment microbial communities to assess whether the construction and restoration of gravel bars in the Trinity River in California enhanced environmental heterogeneity, with the increase in the microbial beta diversity of these in-channel structures against the free-flowing reach of the main channel with comparison to its undisturbed tributaries. ResultsMicrobial community composition of the free-flowing (i.e., no gravel bars) communities were relatively closer regardless of dam influence, whereas the Trinity River gravel bar and tributaries gravel bar communities were highly dissimilar. Proteobacteria, Bacteroidetes, and Acidobacteria were the highly abundant sediment microbial phyla on most sites, specifically in the Trinity River gravel bar communities. Putative functional annotation of microbial taxa revealed that chemoheterotrophy and aerobic chemoheterotrophy were the most prevalent microbial processes, with the Trinity River gravel bars having relatively higher representations. The considerably large abundance of heterotrophic taxa implies that gravel bars provide suitable areas for heterotrophic microorganisms with metabolic functions contributing to the net respiration in the river. ConclusionsOur results provide supporting evidence on the positive impact of habitat restoration being conducted in the Trinity River with the non-dam influenced, undisturbed tributaries as the basis of comparison. Gravel bar recreation and restoration contributed to the increased microbial biodiversity through the restoration of environmental heterogeneity at the river scale. We provided valuable insights into the potential microbial processes in the sediment that might be contributing to the biogeochemical processes carried out by the microbial communities in the Trinity River. The significant positive correlation between the functional diversity of the identified microbial taxa and beta diversity suggests that differences in the detected metabolic functions were closely related to dissimilarities in community composition.

microbiology↗