Biology subjects

Kojima, R.

Publications and source records attributed to Kojima, R..

16 recordsLinked to original sources

bioRxiv2026

Biosensor-enabled single-molecule enzyme activity profiling for function-based molecular counting

Single-molecule enzyme activity analysis enables proteoform-resolved quantification of catalytically active enzyme molecules but remains limited by the need for enzyme-specific fluorogenic substrates, particularly for enzymes involved in central metabolism. Here we establish a biosensor-based framework that integrates genetically encoded fluorescent metabolite biosensors with single-molecule enzyme activity profiling (SEAP), enabling native enzymatic reactions to be monitored without enzyme-specific fluorogenic substrates. By employing different biosensors, the framework was applied to distinct metabolic enzymes, including lactate dehydrogenase and pyruvate kinase. Biosensor-based SEAP enables absolute quantification of active enzyme molecules, transforming conventional activity measurements (U/mL) into molecular abundance (molecules/mL). Applying this approach to single-cell functional proteoform analysis revealed functional heterogeneity in lactate dehydrogenase activity during T-cell activation. In addition, analysis of circulating enzymes showed that much of the plasma lactate dehydrogenase pool could be quantitatively accounted for by physiological erythrocyte turnover. These results establish genetically encoded fluorescent protein-based metabolite biosensors as modular optical transducers for single-molecule functional enzyme analysis across cellular and circulating samples.

biochemistry↗

bioRxiv2026

Monju: Multi-criteria clustering in single-cell omics

Clustering is a fundamental step in single-cell omics analysis. Although single-cell omics data can, in principle, be partitioned according to multiple biologically meaningful criteria, existing methods typically cluster cells using a single criterion. To address this problem, we developed Monju, a multi-criteria clustering method based on a deep generative mixture model. Monju divides cells into biologically reasonable submodels, each of which is equipped with an interpretable latent space. Furthermore, although the partitioning of cells into submodels varies across random seeds, each solution remains biologically plausible, collectively yielding multi-criteria clustering. Moreover, by integrating these multiple clustering solutions to perform meta-clustering, Monju enables the assessment of cluster stability. We applied Monju to human peripheral blood CITE-seq data and demonstrated that it can achieve multi-criteria clustering. Monju therefore provides a powerful and practical framework for dissecting cellular heterogeneity from multiple biological perspectives.

bioinformatics↗

bioRxiv2026

Enzyme activity as an actionable axis for small-molecule precision oncology

Current precision oncology--molecular targeted therapies and immunotherapies--relies on genomic or expressed biomarkers, yet most cancer patients remain ineligible for these treatments. Here, we establish enzyme activity as an actionable and orthogonal axis for precision cancer medicine. Strategic activity-based screening of mouse organs and human clinical specimens with a panel of enzyme-reactive fluorescence probes identified {beta}-galactosidase 1 (GLB1) and {beta}-hexosaminidases (HEX) as broadly elevated tumor-selective biomarkers. Leveraging these activities, we developed 7-ethyl-10-hydroxycamptothecin (SN38)-based GLB1-and HEX-reactive prodrugs. These prodrugs exhibited dramatically reduced systemic toxicities and improved therapeutic windows, compared to a clinically used SN38-based prodrug, irinotecan (CPT-11). Both prodrugs demonstrated activity-dependent therapeutic efficacy, affording a dramatic reduction of tumor volumes across multiple in vivo models, including a subcutaneous patient-derived xenograft (PDX) of lung squamous cell carcinoma that lacked genetic alterations targeted by current precision medicine. Furthermore, this strategy is broadly applicable across various cytotoxic payloads, establishing a generalizable platform for small-molecule precision medicines. Our results define an enzyme-targeting paradigm for precision oncology, in which fluorescence probes serve as companion diagnostic tools to guide development and selection of appropriately targeted prodrugs, which are expected to provide safer and more efficacious treatment options for cancer patients with elevated enzyme activities.

cancer biology↗

bioRxiv2025

The glomerulus as a selective gate for tumor-derived extracellular vesicles in urine

Urinary small extracellular vesicles (sEVs), which can reflect systemic conditions, hold great promise for non-invasive cancer diagnostics, yet the mechanism by which tumor-derived sEVs reach urine remains unclear. Here, we demonstrate that the glomerulus actively transcytoses circulating tumor-derived sEVs into urine. Using CRISPR gRNA-tagged glioma sEVs and bioluminescent/fluorescent GeNL-tagged lung and pancreatic cancer sEVs, we tracked their journey from tumors to urine in multiple mouse models. In vivo and in vitro analyses revealed endocytic uptake and transcytotic release by glomerular cells, accompanied by changes in sEV size and surface composition. GeNL-tagged sEVs consistently showed higher signals in urine than plasma, indicating selective excretion. These findings redefine the glomerulus as a dynamic regulator of EV processing and establish a mechanistic foundation for urinary liquid biopsy.

cancer biology↗

bioRxiv2025

Development of a protocol utilizing single-cell analysis for the differentiation of human iPSCs into SOX6+ midbrain dopaminergic neurons.

Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons, leading to severe debilitating motor impairment. Recent single-cell analyses have revealed that specific mDA subpopulations, such as SOX6 AGTR1 neurons, are most vulnerable to degeneration. Current human pluripotent stem cell differentiation protocols fail to selectively generate these subtypes. Here, we describe a multiomic-guided strategy that enriches SOX6+ mDA neurons by combining enhancing Sonic Hedgehog agonism with prolonged Wnt activation. This approach accelerates floor plate specification, increases expression of mDA developmental markers, and substantially increases the yield of SOX6 mature mDA neurons compared to prior attempts. Following intrastriatal transplantation into hemiparkinsonian mice, these cells restored motor function in approximately 4 months and generated grafts containing SOX6 A9-like neurons. Our work thus establishes a reproducible differentiation platform for generating the PD-susceptible mDA subtype, providing a foundation for precision disease modelling and subtype-targeted cell replacement therapies.

cell biology↗

bioRxiv2025

Low-Background Cancer Imaging With a Bioorthogonal Fluorescence Probe and Engineered Reporter Enzyme Bearing a Targeting Moiety

Combinatorial use of an antibody-reporter enzyme conjugate and a fluorescence probe activated by the enzyme is a powerful strategy for fluorescence-guided cancer surgery. However, conventional probes for typical reporter enzymes are insufficiently bioorthogonal, resulting in high background signals in non-target tissues. We screened a library of HMRef (rhodol derivative)-based fluorescence probes bearing various sugar moieties, and discovered that HMRef-{beta}-D-fucose is bioorthogonal in mammalian systems, but is activated by a metagenomic glycosidase, Td2F2. Directed evolution generated a mutant with a kcat/Km value for HMRef-{beta}-D-fucose of 3.3 x 105/M/sec, 7.3 times higher than that of wild-type Td2F2 and comparable to that of {beta}-galactosidase (LacZ) with a corresponding probe. Theoretical calculation suggested that E296G mutation in Td2F2 causes structural changes that facilitate the probes access to the enzymes active site. In a proof-of-concept study, cancer cells were visualized with a minimal background in the mesentery of a mouse model of peritoneally disseminated human-ovarian-cancer-derived SKOV-3 cells, which endogenously express HER2, by using HMRef-{beta}-D-fucose together with engineered Td2F2 conjugated/fused to a HER2-binding antibody/nanobody.

bioengineering↗

bioRxiv2024

Structural insight into the nuclear transportation mechanism of PPARg by Transportin-1

The spatial and temporal control of protein is essential for normal cellular function. Proteins working in the nucleus have nuclear localization signal (NLS) sequences and are escorted into the nucleus by cognate nuclear transport receptors. A wealth of experimental data about NLS has been accumulated, and nuclear transportation mechanisms are established at the biochemical and structural levels. The peroxisome proliferator-activated receptors (PPARs) are ligand-dependent transcription factors that control various biological responses. We recently reported that the transportation of PPAR{gamma} is mediated by Transportin-1, but PPAR{gamma} lacks a typical NLS sequence recognized by Transportin-1. Moreover, the recognition mechanism remains largely unknown. In this study, we determined the Cryo-EM structure of PPAR{gamma} in complex with Transportin-1 and revealed that Transportin-1 gripped the folded DNA binding domain and the Hinge region of PPAR{gamma}, indicating that PPAR{gamma} recognizes a folded domain with an extended region as a nuclear localization signal, not a canonical unstructured signal sequence, confirmed by the mutation analyses in vitro and in cultured cells. Our study is the first snapshot structure working in nuclear transportation, not in transcription, of PPAR{gamma}.

molecular biology↗

bioRxiv2024

ChemGLaM: Chemical-Genomics Language Models for Compound-Protein Interaction Prediction

Deep learning-based compound-protein interaction (CPI) prediction models are promising in the field of molecular biology, particularly for facilitating the drug discovery process. In practical applications, CPI models should achieve a high generalization performance, quantify prediction confidence, and ensure explainability. Here, we propose ChemGLaM, a chemical genomics language model for reliable and explainable CPI predictions, by addressing these three crucial aspects. ChemGLaM integrates independently pre-trained chemical and protein language models through an interaction block with a cross-attention mechanism, achieving state-of-the-art performance in predicting novel CPIs. Incorporating uncertainty estimation and attention visualization enables ChemGLaM to enhance the success rate of virtual screening and to provide molecular insights into CPIs. Furthermore, we demonstrate its practical applicability by constructing a public database for large-scale CPI predictions and enabling drug/target exploration for candidate treatment of amyotrophic lateral sclerosis (ALS). ChemGLaM represents a significant step toward overcoming the challenges of AI-driven drug discovery and addressing unmet medical needs.

bioinformatics↗

bioRxiv2023

Barcoding of small extracellular vesicles with CRISPR-gRNA enables comprehensive, subpopulation-specific analysis of their biogenesis/release regulators

Small extracellular vesicles (sEVs) are important intercellular information transmitters in various biological contexts, but their release processes remain poorly understood. Herein, we describe a high-throughput assay platform, CRISPR-assisted individually barcoded sEV-based release regulator (CIBER) screening, for identifying key players in sEV release. CIBER screening employs sEVs barcoded with CRISPR-gRNA through the interaction of gRNA and dead Cas9 fused with an sEV marker. Barcode quantification enables the estimation of the sEV amount released from each cell in a massively parallel manner. Barcoding sEVs with different sEV markers in a CRISPR pooled-screening format allows genome-wide exploration of sEV release regulators in a subpopulation-specific manner, successfully identifying previously unknown sEV release regulators and uncovering the exosomal/ectosomal nature of CD63+/CD9+ sEVs, respectively, as well as the synchronization of CD9+ sEV release with the cell cycle. CIBER should be a valuable tool for detailed studies on the biogenesis, release, and heterogeneity of sEVs.

cell biology↗

bioRxiv2023

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

BackgroundIn cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. ResultsNet-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms. ConclusionsWe propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.

bioinformatics↗

bioRxiv2022

Super-resolution vibrational imaging based on photoswitchable Raman probe

Super-resolution vibrational microscopy is a promising tool to increase the degree of multiplexing of nanometer-scale biological imaging, because the spectral linewidth of molecular vibration is about 50 times narrower than that of fluorescence. However, current techniques of super-resolution vibrational microscopy still suffer from various limitations including the need for cell fixation, high power loading or complicated frequency-modulated detection schemes. Herein we utilize photoswitchable stimulated Raman scattering (SRS) to develop a method that we call reversible saturable optical Raman transitions (RESORT) microscopy, which overcomes these limitations. We first describe a new kind of photoswitchable Raman probe designated DAE620 and then we employ a standard SRS detection scheme to validate its signal activation and depletion characteristics when exposed to low-power (microwatt level) continuous-wave laser light. By harnessing the SRS signal depletion of DAE620 through a donut-shaped beam, we demonstrate super-resolution vibrational imaging of mammalian cells with excellent chemical specificity and spatial resolution beyond the optical diffraction limit. Our results indicate RESORT microscopy to be an effective tool with high potential for multiplexed super-resolution imaging of live cells.

bioengineering↗

bioRxiv2022

Droplet array-based platform for parallel optical analysis of dynamic extracellular vesicle secretion from single cells

Extracellular vesicles (EVs) are essential intercellular communication tools, but the regulatory mechanisms governing heterogeneous EV secretion are still unclear due to the lack of methods for precise analysis. Monitoring the dynamics of secretion from individually isolated cells is crucial because, in bulk analysis, secretion activity can be perturbed by cell-cell interactions, and a cell population rarely performs secretion in a magnitude- or duration-synchronized manner. Although various microfluidic techniques have been adopted to evaluate the abundance of single-cell-derived EVs, none can track their secretion dynamics continually for extended periods. Here, we have developed a droplet array-based method that allowed us to optically quantify the EV secretion dynamics of >300 single cells every 2 hours for 36 hours, which covers the cell doubling time of many cell types. The experimental results clearly show the highly heterogeneous nature of single-cell EV secretion and suggest that cell division facilitates EV secretion, showing the usefulness of this platform for discovering EV regulation machinery.

bioengineering↗

bioRxiv2021

Network-based pathogenicity prediction for variants of uncertain significance

While the number of genome sequences continues to increase, the functions of many detected gene variants remain to be identified. These variants of uncertain significance constitute a major barrier to precision medicine 1-3. Although many computational methods have been developed to predict the function of these variants, they all rely on individual gene features and do not consider complex molecular relationships. Here we develop PathoGN, a molecular network-based approach for predicting variant pathogenicity. PathoGN significantly outperforms existing methods using benchmark datasets. Moreover, PathoGN successfully predicts the pathogenicity of 3,994 variants of uncertain significance in the real-world database ClinVar and designates potential pathogenicity. This is the first computational method for the clinical interpretation of variants using biomolecular networks, and we anticipate our method to be broadly useful for the clinical interpretation of variants and for assigning biological function to unknown variants at the genomic scale.

genomics↗

bioRxiv2019

A non-canonical unfolded protein response pathway and mitochondrial dynamics control the number of ER-mitochondria contact sites

Mitochondria maintain their morphology and functions through the optimized balance between the mitochondrial fusion and division. Here we report a novel role of mitochondrial dynamics in controlling the number of ER-mitochondria encounter structure (ERMES) clusters in a yeast cell. Loss of mitochondrial fusion or division caused the increased or decreased number, respectively, of ERMES foci observed in cells. ERMES complexes, therefore, appear to cluster with each other and mitochondrial division may inhibit undesired ERMES hyper-clustering. Furthermore, our microscopic analyses suggest that ER stress induces dissociation of ERMES clusters, increasing the number of ERMES foci even in the absence of Ire1 and Hac1, which are essential factors for the UPR response. Interestingly, we found that ER stress leads to expansion of both the ER and mitochondrial membranes in an ERMES function-dependent manner. These findings imply that a cell is equipped with two independent regulatory mechanisms controlling the number of ER-mitochondria contact sites to meet the cellular as well as environmental demands.

cell biology↗

bioRxiv2019

The methyl cycle is a conserved regulator of biological clocks.

The methyl cycle is a universally conserved metabolic pathway operating in prokaryotes and eukaryotes. In this pathway, the amino acid methionine is used to synthesize S-adenosylmethionine, the methyl donor co-substrate in the methylation of nucleic acids, histone and non-histone proteins and many other molecules within the cell. The methylation of nucleic acids and proteins is the foundation of epigenetic and epitranscriptomic regulations of gene expression, but whether the methyl cycle centrally regulates gene expression and function by controlling the availability of methyl moieties is poorly understood.\n\nFrom cyanobacteria to humans, a circadian clock that involves an exquisitely regulated transcription-translation-feedback loop driving oscillations in gene expression and orchestrating physiology and behavior has been described. We reported previously that inhibition of the methyl cycle in mammalian cells caused the lengthening of the period of these oscillations, suggesting the methyl cycle may indeed act as a central regulator of gene expression, at least in mammals. Here, we investigated whether the methyl cycle, given its universal presence among living beings, regulates the circadian clock in species across the phylogenetic tree of life.\n\nWe reveal a remarkable evolutionary conservation of the link between the methyl cycle and the circadian clock. Moreover, we show that the methyl cycle also regulates the somite segmentation clock, another transcription-translation negative feedback loop-based timing mechanism that orchestrate embryonic development in vertebrates, highlighting the methyl cycle as a master regulator of biological clocks.\n\nSIGNIFICANCE STATEMENTHere we reveal that the methyl cycle, a universal metabolic pathway leading to the synthesis of S-adenosylmethionine, the methyl donor co-substrate in virtually all transmethylation reactions within the cell, is a conserved regulator of biological clocks. These discoveries highlight the methyl cycle as a metabolic hub that regulates gene expression via the availability of methyl moieties for the methylation of nucleic acids, proteins and many other molecules with the cell.

evolutionary biology↗

bioRxiv2018

Visualizing multiple inter-organelle contact sites using the organelle-targeted split-GFP system

Functional integrity of eukaryotic organelles relies on direct physical contacts between distinct organelles. However, the entity of organelle-tethering factors is not well understood due to lack of means to analyze inter-organelle interactions in living cells. Here we evaluate the split-GFP system for visualizing organelle contact sites in vivo and show its advantages and disadvantages. We observed punctate GFP signals from the split-GFP fragments targeted to any pairs of organelles among the ER, mitochondria, peroxisomes, vacuole and lipid droplets in yeast cells, which suggests that these organelles form contact sites with multiple organelles simultaneously although it is difficult to rule out the possibilities that these organelle contacts sites are artificially formed by the irreversible associations of the split-GFP probes. Importantly, split-GFP signals in the overlapped regions of the ER and mitochondria were mainly co-localized with ERMES, an authentic ER-mitochondria tethering structure, suggesting that split-GFP assembly depends on the preexisting inter-organelle contact sites. We also confirmed that the split-GFP system can be applied to detection of the ER-mitochondria contact sites in HeLa cells. We thus propose that the split-GFP system is a potential tool to observe and analyze inter-organelle contact sites in living yeast and mammalian cells.

cell biology↗