Biology subjects

Nishi, Y.

Publications and source records attributed to Nishi, Y..

7 recordsLinked to original sources

bioRxiv2026

Information coding strategies in biological reservoir computing for multiclass time-series classification

Physical reservoir computing (RC) using biological neural networks (BNNs) offers a framework for probing how high-dimensional dynamics arising from multicellular systems support complex information processing in the brain. However, how electrical inputs and extracellular readouts should be designed to suit different task demands remains unclear. Here, using primary rat cortical neurons cultured on a high-density microelectrode array, we investigated how input encoding and spike detection methods influence neural representations and the resulting RC performance in BNNs. Comparison of pulse amplitude modulation and latency coding revealed that the latter exhibited superior performance in demanding multiclass conditions, including a 10% improvement in a 10-class spoken digit classification task. Spike sorting further improved classification performance by enhancing class separability, while largely preserving the low-dimensional structure of population activity. These findings highlight the importance of designing information coding strategies according to task difficulty.

neuroscience↗

bioRxiv2026

Decoy and antisense RNA oligonucleotides targeting the EGFR exon 3 splice site suppress proliferation of A172 glioblastoma cells

Short RNA oligonucleotides directed at the 3' splice site region of EGFR exon 3 reduced the proliferation of A172 glioblastoma cells, while the same oligonucleotides had no detectable effect on A549 lung adenocarcinoma cells. Glioblastoma carries EGFR amplification in roughly a third of IDH-wildtype cases, and the A172 line harbours a tandem duplication in the EGFR tyrosine kinase domain. Because exon definition in human genes depends on RNA-binding proteins that engage exonic sequence near splice sites, we used the SpliceAid database to identify candidate RNA-binding proteins at EGFR exons 2 and 3. Y-box binding protein 1 (YB-1, encoded by YBX1), reported to drive glioblastoma growth through its RNA-binding function, scored highly immediately adjacent to the exon 3 3' splice site. We designed four 2'-modified RNA oligonucleotides carrying a nuclear localisation signal: two antisense oligonucleotides against the exon 3 region (asRNA1, spanning the splice site; asRNA2, downstream of it), a decoy complementary to the asRNA2 target (dRNA2), and a decoy carrying the YB-1 consensus binding sequence (dYB-1). In A172 cells, all four reduced EGFR protein in a single Western blot experiment, and asRNA1, asRNA2 and dRNA2 reduced proliferation relative to scrambled RNA at days 1 to 3; dYB-1 produced a comparable reduction at day 3. None of these effects was seen in A549 cells. YB-1 protein level and subcellular distribution were unchanged by the oligonucleotides, arguing against an indirect route through altered YB-1 abundance. These observations are preliminary. Every experiment reported here was performed once: the proliferation data come from single plates with three wells per group, analysed by Dunnett's test against the scrambled-RNA control, and the Western blot, RT-PCR and immunocytochemistry panels are single runs presented as representative observations. Direct binding of YB-1 to the targeted EGFR sequence has not been demonstrated. We post this work as a preprint so that the design strategy and the initial observations are available while these questions remain open.

molecular biology↗

bioRxiv2026

Handwritten Digit classification with neural cultures is influenced by neural architecture, network dynamics, and decoding methods

As silicon-based computing approaches fundamental physical limits, neurocomputing offers an energy-efficient alternative by leveraging the intrinsic non-linear dynamics of biological systems. To harness these dynamics, it is vital to understand the structure-function relationship governing how neural cultures process complex spatio-temporal information and how to appropriately decode the resulting neural electrophysiological activity. We investigated this utilizing a closed-loop electrophysiology platform, the CL1, to implement reservoir computing in human iPSC-derived neuronal networks. To systematically evaluate the variables driving neurocomputational capacity, we explored how cellular composition (cortical vs. hippocampal lineages), and the physical architecture (unstructured monolayers, 3D neural organoids, and modular networks confined by microfluidic devices) influenced electrophysiological properties and interacted with different decoding methodologies. Using a spatio-temporal version of a handwritten digit pattern recognition task (MNIST), we analyzed how these biological and analytical factors influenced classification accuracy. To ensure robust interpretation this required us to first demonstrated that reservoir computing decoding methods require strict artifact control and trial-based cross-validation to distinguish network computation from artifactual signal separability or temporal data leakage. Applying this validated frequency-domain pipeline, we suggest a clear functional hierarchy where structural modularity acts as a vital functional regularizer. Modular cortical cultures significantly outperformed unconstrained monolayers and organoids on MNIST. Furthermore, decoding frequency information from raw signals proved superior to typical time-bin decoding implementations. These findings establish that maximizing the computational potential of Synthetic Biological Intelligence, while avoiding false positives, requires a synergistic optimization of cellular identity, structural governance, and rigorous decoding logic. In doing so, this work provides a critical base establishing the criteria under which to evaluate neurocomputing implementations.

neuroscience↗

bioRxiv2026

Profiling and Targeting of Regulatory RNAs to Upregulate Gene Expression

Transcription of long noncoding RNAs (lncRNAs), including enhancer RNAs (eRNAs) and promoter-associated RNAs (paRNAs), collectively termed regulatory RNAs (regRNAs), is a hallmark of active gene expression, yet it remains unknown whether regRNAs can be targeted to selectively enhance transcription in cis. We developed regRNA Capture-seq, a high-throughput method to profile regRNAs, and applied it to primary human hepatocytes, annotating thousands of regRNAs at [~]2,000 enhancers and promoters. Using this approach, we interrogated a genetically validated enhancer of the ornithine transcarbamylase (OTC) gene, mutations of which cause OTC deficiency (OTCD), the most common urea cycle disorder. Antisense oligonucleotides (ASOs) targeting enhancer-derived regRNAs led to dose-dependent upregulation of OTC in hepatocytes. Mechanistically, ASOs altered regRNA structure, elevated regRNA levels, displaced transcriptional repressors, and increased H3K27 acetylation at the targeted enhancer. This work establishes a potential therapeutic strategy for addressing haploinsufficiency and highlights regRNAs as actionable targets for ASO-mediated upregulation of gene expression.

genomics↗

bioRxiv2025

Excellent agreement between automated deep learning-based and manual DWI infarct volume measurement in hyperacute stroke

BackgroundDiffusion-weighted imaging (DWI) lesion volume and infarct growth are important imaging markers in acute ischemic stroke, but manual volume measurement is time-consuming and resource-intensive. Deep learning (DL)-based automated segmentation may facilitate rapid assessment; however, its performance on hyperacute DWI has not been sufficiently assessed. The aim was to evaluate the agreement between DL-based automated and manual infarct volume measurements and to compare their ability to predict clinical outcomes in patients treated with mechanical thrombectomy (MT). MethodConsecutive MT-treated patients (September 2014-December 2019) who underwent DWI at admission and at approximately 24 hours were retrospectively analyzed. Manual infarct volume was measured by stroke neurologists. Automated measurements were obtained using DL-based software. Agreement was assessed using Pearsons correlation, Bland-Altman analysis, and intraclass correlation coefficients (ICC 2,1). Inter- and intra-rater reliabilities were evaluated in a randomly selected subgroup of 150 patients. Predictive ability for a good outcome at 3 months (modified Rankin Scale score 0-2 or stable/improved from premorbid status) was compared using C-statistics and DeLongs test. ResultsA total of 371 patients (677 DWI scans) were included. Manual and automated measurements showed very strong correlation (r = 0.96) with minimal bias (-1.77 mL). The ICC for manual-automated agreement was 0.959 (95% CI, 0.952-0.965), comparable to inter- and intra-rater ICCs. Agreement remained high across onset-to-imaging times and lesion sizes. Predictive abilities for a good outcome were similar for manual and automated admission DWI volume (C-statistics 0.867 vs. 0.861) and infarct growth (0.859 vs. 0.853). Manual follow-up DWI volume showed slightly better predictive ability than automated measurement (0.880 vs. 0.866). ConclusionDL-based automated infarct volume measurement shows excellent agreement with experienced clinicians, with predictive performance comparable to manual assessment. Automated DWI-based quantification is reliable and feasible for use in hyperacute stroke management.

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↗

bioRxiv2021

Post-transcriptionally regulated genes are essential for pluripotent stem cell survival

The effects of transcription factors on the maintenance and differentiation of pluripotent stem cells (PSCs) have been well studied. However, the importance of post-transcriptional regulatory mechanisms, which cause the quantitative dissociation of mRNA and protein expression, has not been explored in detail. Here, by combining transcriptome and proteome profiling, we identified 228 post-transcriptionally regulated genes with strict upregulation of the protein level in PSCs. Among them, we found that 84 genes were vital for the survival of PSCs and HDFs, including 20 genes that were specifically necessary for the survival of PSCs. These 20 proteins were upregulated only in PSCs and not in differentiated cells derived from the three germ layers. Subcellular fractionation of the mRNA showed that the expression of most of the 20 proteins was regulated at the mRNA localization stage from the nucleus to the cytoplasm, but their translation efficiency was constant. Together, these results revealed that post-transcriptionally regulated genes have a crucial role in PSC survival.

molecular biology↗