Biology subjects

Shimamura, T.

Publications and source records attributed to Shimamura, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Embryonic macrophages chronologically change their brain-entry routes and competence for neural progenitor-induced microglial differentiation

Microglia arise from macrophage precursors that enter the developing brain and acquire microglial identity, but how these processes are coordinated remains unclear. We show that despite prominent entry of macrophages from the ventricles into the mouse telencephalon at embryonic day (E) 12.5, they enter from the meninges at E11.5, preferentially in the rostral pallium. Consistent with this regional pattern, whole-mount en face imaging revealed that focal holes in the pial basement membrane were most frequently observed in the rostral pallium. We found that neural progenitors, but not endothelial cells, promote acquisition of microglial properties through a process that requires close cellular proximity and partly depends on TGF-{beta} receptor signaling. This responsiveness is substantially stronger in E13.5 than in E17.5 meningeal macrophages, indicating stage-dependent competence to acquire microglial properties. These findings suggest that microglial establishment is developmentally regulated by macrophage access to the telencephalon and responsiveness to local neural cues.

developmental biology↗

bioRxiv2026

Microfluidic Platform for Drug Response Profiling in NSCLC Patient-Derived Organoids

Tumor models that recapitulate 3D architecture are essential for understanding how cellular organization and microenvironmental interactions govern therapeutic response in human cancers. Here, we developed a microfluidic microphysiological system that enables controlled and scalable culture and drug testing of non-small cell lung cancer spheroids and patient-derived organoids. The platform integrated U-shaped microwells with dual-channel loading to support de novo spheroid formation, efficient trapping of pre-formed spheroids, and loading of intact organoids with reduced size heterogeneity. Tumor spheroids and organoids maintained high viability and structural integrity during long-term on-chip culture, and constrained microscale confinement produced ellipsoidal geometries that deviate from idealized spherical assumptions. Baseline genotype-dependent responses to KRAS G12C and EGFR inhibitors were preserved across agarose and microfluidic formats, establishing a validated reference state. Building on this baseline, fibroblast- and endothelial-derived cues consistently attenuated responses to targeted therapies across conditioned media, mixed co-culture, and spatially organized configurations. Resistance phenotypes converged on a dominant role for paracrine signaling, while increasing architectural complexity primarily enhanced morphological fidelity rather than altering therapeutic response. These findings establish a microphysiological framework that decouples tumor-intrinsic drug sensitivity from microenvironment-mediated modulation, enabling the systematic evaluation of paracrine resistance mechanisms in NSCLC.

bioengineering↗

bioRxiv2026

Probabilistic coupling of cellular and microenvironmental heterogeneity by masked self-supervised learning

AO_SCPLOWBSTRACTC_SCPLOWSpatial omics technologies have advanced to single-cell resolution, enabling systematic analysis of tissue microenvironments alongside cellular-state heterogeneity. However, computationally defining microenvironmental states at single-cell resolution and identifying representations most informative for biological discovery remain major challenges. Here we present Mievformer, a Transformer-based masked self-supervised framework that learns microenvironmental embeddings by encoding neighboring cellular states and relative spatial configurations to parameterize the conditional distribution of continuous cell states at central spatial positions. Through InfoNCE optimization, Mievformer learns representations that capture the relative enrichment of cell states across microenvironments, formalized as a conditional density ratio, thereby enabling probabilistic inference of the coupling between microenvironmental and cellular heterogeneity. Mievformer outperformed existing methods in niche clustering on simulated spatial transcriptomics data and achieved the highest average performance across five real datasets spanning three spatial transcriptomics platforms when evaluated using DREC, a ground-truth-free metric that most strongly correlated with ground-truth performance in simulations. Beyond conventional clustering, Mievformer enables identification of cellular subpopulations based on their microenvironmental distribution and detection of gene-expression signatures associated with colocalization of specific cell populations. Together, these results establish Mievformer as a quantitatively robust and biologically informative framework for learning microenvironment representations in spatial omics.

bioinformatics↗

bioRxiv2026

Closed-Loop Multi-Objective Optimization for Receptor-Selective Cell-Penetrating Peptide Design

Cell-penetrating peptides (CPPs) can deliver diverse cargos into cells. However, designing CPPs with receptor-selective interaction profiles remains difficult because interactions with individual cell-surface components cannot be tuned independently. Here, we developed a closed-loop in silico framework for receptor-selective CPP design, in which receptor interactions are formulated as explicit objectives in a multi-objective optimization problem. We first constructed a CPP-like candidate library using a sequence generative model fine-tuned on known CPPs. The framework then evaluated candidate peptides by receptor-wise docking, molecular dynamics simulations, and MM/GBSA to compute receptor-wise binding scores. These scores were used iteratively to propose subsequent candidates by multi-objective Bayesian optimization. Applied to a CXCR4/NRP1 design setting, the framework identified candidates with more favorable predicted interaction profiles, characterized by higher CXCR4 binding scores and lower NRP1 binding scores. We selected 10 peptides from the computationally identified candidates for cell-based imaging and found that 4 showed higher enrichment in CXCR4-positive regions than in NRP1-positive regions under the tested conditions. These results show that the proposed framework provides a practical in silico approach for designing CPPs with receptor-selective interaction profiles.

bioinformatics↗

bioRxiv2026

Skip-Zeros Variational Inference in the Million-Cell Era of Single-Cell Transcriptomics

Combinatorial indexing-based single-cell RNA sequencing methods such as sci-RNA-seq and sci-RNA-seq3 now enable the profiling of millions of cells, producing expression matrices that are both extremely sparse and high-dimensional. Conventional nonnegative matrix factorization (NMF) provides an interpretable framework for uncovering latent biological structures but is computationally prohibitive at this scale, as it requires explicit access to the vast number of zero entries. We introduce UNISON (Unified Sparse-Optimized Nonnegative factorization), a scalable framework for matrix and tensor factorization based on skip-zeros variational inference. By reformulating stochastic variational Bayes updates in terms of sufficient statistics, UNISON performs inference using only nonzero elements, while implicitly accounting for zeros through geometric sampling. This strategy enables efficient parameter estimation without matrix expansion and naturally accommodates multiple experimental contexts. Simulation studies show that UNISON is robust to diverse learning-rate schedules and mini-batch sizes, providing practical guidelines for optimization. Application to the Mouse Organogenesis Cell Atlas demonstrates scalability to over one million cells, yielding latent factors that capture developmental trajectories and lineage-specific signatures with improved interpretability compared to existing methods. Cross-species analysis of aqueous humor outflow pathways across five vertebrate species further highlights UNISONs ability to disentangle conserved from species-specific transcriptional programs and to recover biologically meaningful gene-gene and gene-phenotype relationships relevant to glaucoma. By efficiently exploiting sparsity while preserving interpretability, UNISON establishes a principled and practical solution for integrative, large-scale single-cell transcriptomics. Significance StatementSingle-cell technologies now generate datasets spanning millions of cells, creating massive, sparse matrices that are computationally difficult to analyze. Conventional methods often sacrifice statistical rigor for speed, either by discarding data or employing inappropriate models. We present UNISON, a framework that leverages a mathematical trick to perform exact inference using only nonzero elements. By combining this efficient computation with a probability model tailored for count data, UNISON scales to millions of cells while preserving information on rare cell types. This approach resolves the trade-off between scalability and accuracy, enabling precise integrative analyses of development and evolution across species without requiring massive computational resources.

bioinformatics↗

bioRxiv2026

scTREND: An annotation-free single-cell time-resolved and condition-dependent hazard model

Prognosis in cancer and other complex diseases is shaped by heterogeneous cell states and clinical or genetic contexts whose effects change over time. Yet most survival analyses assume predefined cell types and time-invariant covariates, and cohorts pairing single-cell transcriptomes with outcomes are limited, obscuring within-type heterogeneity, time-varying risk, and condition-specific effects. We developed scTREND, an annotation-free, time-resolved, condition-dependent hazard model that learns variational single-cell embeddings, deconvolves bulk or spatial samples without labels, and fits a conditional piecewise-constant hazard model to estimate cell-level hazard coefficients across discrete time bins and conditions. scTREND enables cell-level risk attribution without prior cell-type annotations, dynamic risk modeling over time, and mutation- or treatment-specific risk assessment. In simulations, scTREND recovered ground-truth temporal and condition-specific coefficients with strong concordance and improved prediction over an existing method. In melanoma, scTREND identified subpopulations with risk amplified in BRAF-mutant tumors; in COVID-19, cytotoxic T-cell subpopulations with early-to-late risk reversal and pathways associated with prognosis under remdesivir; and in spatial transcriptomics of clear cell renal carcinoma, spatial regions whose prognostic relevance shifts over time. Across diseases and modalities (bulk RNA-seq and spatial transcriptomics), scTREND provides a time-resolved, condition-aware link between single-cell states and clinical outcomes and is implemented in Python (GitHub:https://github.com/R301Carbine/scTREND).

bioinformatics↗

bioRxiv2025

STODE: A Deep Generative Framework for Continuous Spatiotemporal Dynamics in Spatial Transcriptomics

MotivationSpatial transcriptomics offers detailed molecular insights but struggles to capture continuous tissue dynamics due to experimental constraints and destructive sampling. Traditional optimal transport (OT) methods attempt to link time points but often rely on unimodal data and oversimplify complex developmental processes. We propose a spatiotemporal deep generative model that captures the continuous evolution of tissue, integrating both spatial and gene-expression dynamics. We first embed spatial transcriptomic particles into a latent space using a VAE and then learn time-dependent dynamics with a potential-guided neural ODE. This framework reveals diverse tissue behaviors, including growth patterns, spatial reorganization, and temporal gene-expression changes. A neural decoder estimates key behaviors like growth, disappearance, movement, and clustering, while a differentiable growth module models region appearance and disappearance. ResultsSTODE is a deep generative model that reconstructs continuous spatiotemporal dynamics from discrete spatial transcriptomics measurements collected at multiple time points. On a complex synthetic dataset simulating gene-driven tissue morphogenesis and mouse midbrain development dataset, STODE significantly outperformed both linear interpolation and Entropic Optimal Transport models in predicting gene expression and spatial coordinates for a excluded timepoint. When applied to a mouse organogenesis dataset, the models backward simulations from late-stage embryos to early progenitors accurately reconstructed known developmental processes. The simulations correctly identified early pharyngeal arch derivatives as the populations occupying the largest area and captured the progressive differentiation of diverse cell types from multipotent neural crest and mesenchymal progenitors, demonstrating the models capability to reconstruct continuous and meaningful biological trajectories from discrete-timepoint spatial transcriptomes. In addition, the inferred factors reflected coordinated activation of morphogenetic programs, including cytoskeletal remodeling and cell-matrix interactions, and revealed transcriptional signatures associated with epithelial-mesenchymal transition and directed tissue migration, suggesting that the model effectively disentangled regulatory modules underlying the spatial organization and morphogenesis of craniofacial structures. The proposed model, STODE, will be broadly applicable to studies on embryogenesis, tissue regeneration, and disease progression, where reconstructing continuous spatiotemporal trajectories is essential for understanding biological mechanisms. Availability and ImplementationThe STODE model was implemented in Python using the PyTorch deep learning library. This code is available on GitHub at https://github.com/LzrRacer/STODE/. Contactk.majima1214@gmail.com, yakojim@ncc.go.jp, shimamura.csb@tmd.ac.jp

bioinformatics↗

bioRxiv2025

Anti-amyloid beta therapy resolves stroke recovery impairment caused by Alzheimer's disease

Stroke and dementia are common comorbidities and a growing concern causing disability in aging societies worldwide. Although anti-amyloid beta (anti-A{beta}) antibodies have recently been anticipated to relieve preclinical Alzheimers disease pathology, we discovered that post-stroke administration of anti-A{beta} antibodies restored neural repair for stroke recovery impeded by cerebral A{beta} accumulation. Neuronal recovery-associated gene expression for stroke recovery was considerably impaired even by slight A{beta} accumulation in murine and human brain. Slight A{beta} accumulation had less impact on neurons without stroke but caused a unique myeloid immunity after an ischemic stroke that enhanced the inflammatory cascades impeding neural repair for stroke recovery. Aducanumab administration after ischemic stroke prevented formation of this malignant myeloid immunity, resolving the impairment of post-stroke neural repair caused by cerebral A{beta} accumulation. Thus, our study has revealed the ability of anti-A{beta} therapies to restore functional recovery after a stroke with cerebral A{beta} accumulation.

neuroscience↗

bioRxiv2024

mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities

Single-cell multiomics provides unique insight into the regulatory relationships across different biological layers such as the transcriptome and regulome. However, single-cell multiomics is limited by its ability to capture only static snapshots at the time of observation, restricting the reflection of dynamic state changes orchestrated across modalities. RNA velocity analysis of single cells allows for the prediction of temporal changes in the transcriptome; however, the inferred dynamics cannot be applied across all biological layers, specifically in the regulome. Therefore, to address this limitation, we developed multimodal velocity of single cells (mmVelo), a deep generative model designed to estimate cell state-dependent dynamics across multiple modalities. mmVelo estimates cell state dynamics based on spliced and unspliced mRNA expression, and uses multimodal representation learning to project these dynamics onto chromatin accessibility, inferring chromatin velocity at a single-peak resolution. We applied mmVelo to single-cell multiomics data from a developing mouse brain and validated the accuracy of the estimated chromatin accessibility dynamics. Furthermore, using the estimated dynamics, we identified the transcription factors that are crucial for chromatin accessibility regulation in mouse skin. Finally, using multiomics data as a bridge, we demonstrated that during human brain development, the dynamics of missing modalities can be inferred from single-modal data via cross-modal generation. Overall, mmVelo enhances our understanding of the dynamic interactions between modalities, offering insights into the regulatory relationships across molecular layers.

bioinformatics↗

bioRxiv2024

scSurv: a deep generative model for single-cell survival analysis

Single-cell omics analysis has unveiled the heterogeneity of various cell types within tumors. However, no methodology currently reveals how this heterogeneity influences cancer patient survival at single-cell resolution. Here, we introduce scSurv, combining a Cox proportional hazards model with a deep generative model of single-cell transcriptome, to estimate individual cellular contributions to clinical outcomes. The accuracy of scSurv was validated using both simulated and real datasets. This method identifies cells associated with favorable or adverse prognoses and extracts genes correlated with their contribution levels. In melanoma, scSurv reproduces known prognostic macrophage classifications and facilitates hazard mapping through spatial transcriptomics in renal cell carcinoma. We also identified genes consistently associated with prognosis across multiple cancers and demonstrated the applicability of this method to infectious diseases. scSurv is a novel framework for quantifying the heterogeneity of individual cellular effects on clinical outcomes.

bioinformatics↗

bioRxiv2024

Quantitative in toto live imaging analysis of apical nuclear migration in the mouse telencephalic neuroepithelium

In the embryonic neuroepithelium (NE), neural progenitor cells undergo cell cycle-dependent interkinetic nuclear migration (IKNM) along the apicobasal axis. Extensive IKNM supports increasing cell production rates per unit apical surface, as typically observed in the mammalian telencephalic NE. Apical nucleokinesis during the G2 phase is an essential premitotic event, but its occurrence has not yet been quantitatively analyzed at a large 3D scale with sufficient spatiotemporal resolution. Here, we comprehensively analyzed apically migrating nuclei/somata in reference to their surroundings from embryonic day (E)11 to E13 in the mouse telencephalon. The velocity of apical nucleokinesis decreased, with more frequent nuclear pausing occurring at E12 and E13, whereas the nuclear density in the middle NE zone (20-40 m deep) increased. This result, together with the results of Shh-mediated overproliferation experiments in which the nuclear density was increased in vivo at E11, suggests that apical nucleokinesis is physically influenced by the surrounding nuclei. Mean square displacement analysis for nuclei being passed by the apically migrating nuclei via horizontal sectioning in toto-recorded movies revealed that the "tissue fluidity" or physical permissiveness of the NE to apical nucleokinesis gradually decreased (E11 > E12 > E13). To further investigate the spatial relationship between preexisting mitoses and subsequent premitotic apical nucleokinesis, the horizontal distribution of mitoses was cumulatively ([~]3 hours) analyzed under in toto monitoring. The four-dimensional cumulative apical mitoses presented a "random", not "clustered" or "regular", distribution pattern throughout the period examined. These methodologies provide a basis for future comparative studies of interspecies differences.

developmental biology↗

bioRxiv2024

Unsupervised deep learning enables blur-free super-resolution in two-photon microscopy

We developed an unsupervised deep learning method to simultaneously perform deblurring, super-resolution, and segmentation of two-photon microscopy images. Two-photon microscopy is an excellent technique for non-invasively observing deep biological tissues, but blurring during deep imaging has been a challenge. Conventional deblurring methods have limited performance and are not suitable for deblurring two-photon microscopy images. Moreover, methods that simultaneously perform segmentation, which is usually required in downstream analysis, have not been developed. Therefore, in this method (TENET), we precisely modeled the blur of two-photon microscopy and simultaneously achieved deblurring, super-resolution, and segmentation through unsupervised deep learning. In simulation and experimental data, we achieved deblurring, resolution improvement, and segmentation accuracy surpassing conventional methods. Furthermore, we applied the method to live imaging of microglia, enabling quantitative 3D morphological analysis that was previously difficult. This method allows non-invasive visualization of detailed structures in deep biological tissues, and is expected to lead to a more high-definition understanding of biological phenomena. Future applications to time-series morphological analysis of microglia are anticipated.

bioinformatics↗

bioRxiv2024

A deep generative model integrating single-cell time-frequency characteristics transformed from electrophysiological data with transcriptomic features

Patch-seq yields multi-modal data (e.g., transcriptomic and electrophysiological data) from single cells. However, currently used analytical methods are based on a few global electrophysiological features predefined from chronological potential changes and overlook the importance of time-frequency domain. In this study, we present LincSpectr, a deep neural network model that integrates transcriptomic and electrophysiological features with the latent variables of various variational autoencoders. This model combines the two modalities according to the connection between the latent variables of different modalities calculated by attention-like mechanisms and achieves cross-modal predictions and an inverse analysis. We discovered that the predicted electrophysiological features changed continuously along with their transcriptional profiles and that the neighborhood relationships between the latent states of the transcriptional profiles were consistent with those of the electrophysiological features. Inverse analysis of our model enabled the extraction of gene sets affecting specific time-frequency domains; some genes were likely to be involved in neural activity. Our approach represents a potential avenue to facilitate the discovery of molecular mechanisms underlying time-frequency activities in various cell types, enhancing our understanding of their roles in neural function.

bioinformatics↗

bioRxiv2024

Inferring extrinsic factor-dependent single-cell transcriptome dynamics using a deep generative model

1RNA velocity estimation helps elucidate temporal changes in the single-cell transcriptome. However, current methodologies for inferring single-cell transcriptome dynamics ignore extrinsic factors, such as experimental conditions and neighboring cell. Here, we propose ExDyn--a deep generative model integrated with splicing kinetics for estimating cell state dynamics dependent on extrinsic factors. ExDyn enables the counterfactual inference of cell state dynamics under different conditions. Among the extrinsic factors, ExDyn can extract key features which have large effects on cell state dynamics. ExDyn correctly estimated the difference in dynamics between two conditions and showed better accuracy over existing RNA velocity methods. ExDyn were utilized for unveiling the effect of PERK-knockout on neurosphere differentiation, hematopoietic stem cell differentiation driven by chromatin activity and the dynamics of squamous cell carcinoma cells dependent on colocalized neighboring cells. These results demonstrated that ExDyn is useful for analyzing key features in the dynamic generation of heterogeneous cell populations.

bioinformatics↗

bioRxiv2024

LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. This approach transforms scRNA-seq observations with an identical lineage barcode into sequential trajectories toward a common progenitor in a latent cell state space. Using hematopoiesis and reprogrammed fibroblast datasets, we demonstrate the capability of LineageVAE to reconstruct unobservable cell state transitions, historical transcriptome, and regulatory dynamics toward progenitor cell states at single-cell resolution.

bioinformatics↗

bioRxiv2024

Dysregulated expanded endocannabinoid system as therapeutic targets of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the selective loss of upper and lower motor neurons. ALS patients often manifest systemic metabolic abnormalities such as glucose intolerance. Herein, to elucidate the systemic metabolic changes related to ALS progression, we performed metabolomics analysis on the serum of ALS patients and identified several metabolites associated with the disease progression, including metabolites involved in the expanded endocannabinoid system (ECS). In particular, the levels of N-acyl taurines (NAT) were correlated with the longitudinal change in the revised ALS functional rating scale (ALSFRS-R) rating. In vitro experiments with ALS cell models and in vivo studies with SOD1G93A transgenic mice revealed that PF-04457845, a fatty amide acid hydrolase (FAAH) inhibitor, up-regulated the expanded ECS, particularly the levels of NATs and N-acyl ethanolamine and ameliorates motor neuron degeneration through the regulation of microglial polarization, synapse plasticity, and neuronal development. Our study indicates that dysregulation of the expanded ECS is associated with ALS progression and a target for novel disease-modifying therapies.

neuroscience↗

bioRxiv2023

A deep generative model for estimating single-cell RNA splicing and degradation rates

AO_SCPLOWBSTRACTC_SCPLOWMessenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. We introduce DeepKINET, a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperformed existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identified RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzed the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

bioinformatics↗

bioRxiv2023

Survivability and Life Support in Sealed Mini-Ecosystems with Simulated Planetary Soils

Establishing a sustainable life-support system for space exploration is challenging due to the vast distances, costs, and differing environments from Earth. Using insights from the Biosphere 2 experiment, we introduced the "Ecosphere" and "Biosealed" systems in custom containers to replicate Earths ecosystems, suggesting feasible space migration through transplanting Earth-like biomes. Over four years, we gained deeper insights into these enclosed ecosystems. Moisture deficiency was a major obstacle to plant growth, which we addressed by incorporating a groundwater layer in the containers. We underscored the critical role of microorganisms in building and sustaining these ecosystems. However, temperature spikes from sunlight threatened stability. Our experiments confirmed fruit flies survival on plant-produced oxygen and photosynthetic bacteria. Interactions between plants, microbes, and simulated space soils were examined. Detailed analysis unveiled diverse microbes shaping both confined and simulated space environments. Major findings include the symbiotic relationship of plants with cyanobacteria, the potential of LED lighting in sun-limited missions, and challenges with ethylene gas and moisture. Microbial integration in rough soils holds promise for seed germination, but understanding their role in space soils is crucial. Our research offers a comprehensive foundation for future space life-support systems and underlines potential concerns about microbes affecting human health.

ecology↗