Biology subjects

Hirose, H.

Publications and source records attributed to Hirose, H..

8 recordsLinked to original sources

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↗

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

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↗

bioRxiv2022

Paradoxical tumor suppressive role of the musculoaponeurotic fibrosarcoma gene in colorectal cancer

Somatic cell reprogramming using the microRNAs miR200c, miR-302s, and miR-369s leads to increased expression of cyclin-dependent kinase inhibitors in human colorectal cancer (CRC) cells and suppressed tumor growth. Here, we investigated whether these microRNAs inhibit colorectal tumorigenesis in CPC;Apc mice, which are prone to colon and rectal polyps. Repeated administration of microRNAs inhibited polyp formation. Microarray analysis indicated that c-MAF, which reportedly shows oncogene-like behavior in multiple myeloma and T-cell lymphoma, decreased in tumor samples but increased in microRNA-treated normal mucosa. Immunohistochemistry identified downregulation of c-MAF as an early tumorigenesis event in CRC, with low c-MAF expression associated with poor prognosis. Of note, c-MAF expression and p53 protein levels were inversely correlated in CRC samples. c-MAF knockout led to enhanced tumor formation in azoxymethane/dextran sodium sulfate-treated mice, with activation of cancer-promoting genes. c-MAF may play a tumor-suppressive role in CRC development.

molecular biology↗

bioRxiv2022

Single-cell colocalization analysis using a deep generative model

1Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, cellular responses to external stimuli, and their biological functions in diseases and tissues. However, high-throughput methods for identifying spatial proximity at single-cell resolution are practically unavailable. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers inter-cellular colocalization networks with single cell resolution by the integration of single cell and spatial transcriptomes. It segregates cell populations defined by the colocalization relationships and predicts cell-cell interactions between colocalized single cells. DeepCOLOR could identify plausible cell-cell interaction candidates in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2 by reconstructing spatial colocalization maps at single-cell resolution. DeepCOLOR is typically applicable to studying cell-cell interactions in any spatial niche. Our newly developed computational framework could help uncover molecular pathways across single cells connected with colocalization networks.

bioinformatics↗

bioRxiv2021

Piezo1 activation using Yoda1 inhibits macropinocytosis and proliferation of cancer cells

Macropinocytosis is a type of endocytosis accompanied by actin rearrangement-driven membrane deformation, such as lamellipodia formation and membrane ruffling, followed by macropinosome formation. A certain number of mammalian mechanosensors are sensitive to membrane deformation and tension. However, it remains unclear whether macropinocytosis is regulated by mechanosensors. Focusing on the mechanosensitive ion channel Piezo1, we found that Yoda1, a Piezo1 agonist, potently inhibits macropinocytosis induced by epidermal growth factor (EGF). Although studies with Piezo1 knockout cells suggest that Piezo1 itself is not physiologically indispensable for macropinocytosis regulation, Yoda1 inhibited ruffle formation depending on the extracellular Ca2+ influx through Piezo1 and on the activation of the calcium-activated potassium channel KCa3.1. This suggests that Ca2+ ions can regulate EGF-stimulated macropinocytosis. Moreover, Yoda1 impaired cancer cell proliferation, suggesting the impact of macropinocytosis inhibition. We propose the potential for cancer therapy by macropinocytosis inhibition through the regulation of a mechanosensitive channel activity.

cell biology↗

bioRxiv2020

Phosphorylation and dephosphorylation of Ser852 and Ser889 control clustering, localization, and function of PAR-3

Cell polarity is essential for various asymmetric cellular events, where the partitioning defective (PAR) protein, PAR3, plays a unique role as a cellular landmark to establish polarity. In epithelial cells, PAR3 localizes at the subapical border such as the tight junction in vertebrates and functions as an apical determinant. Although there is much information about the regulators of PAR3 localization, the mechanism involved in PAR3 concentration and localization to the specific membrane domain remains an important question to be clarified. In this study, we demonstrate that ASPP2, a stimulator of PAR3 localization, can link PAR3 and protein phosphatase 1 (PP1). The ASPP2-PP1 complex dephosphorylates a novel phosphorylation site, Ser852, of PAR3. Furthermore, Ser852- or Ser889-unphosphorylatable PAR3 mutants form protein clusters and ectopically localize to the lateral membrane. Concomitance of clustering and ectopic localization suggests that PAR3 localization is a consequence of local clustering. We also demonstrate that unphosphorylatable forms of PAR3 are static in molecular turnover and fail to coordinate rapid reconstruction of the tight junction, supporting that both phosphorylated and dephosphorylated states are essential for the functional integrity of PAR3. Summary statementWe show that phosphorylation and dephosphorylation regulate clustering of PAR-3, a cell polarity-regulating factor, and how the clustering regulation affects localization of PAR-3 and cell-cell junction formation.

cell biology↗