Faculty Profiles - KATO, Ryuji

Papers - KATO, Ryuji

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Overcoming the tumor microenvironment in pancreatic cancer via barrier-specific nanoparticle drug delivery

Yang, J; Kato, R; Choi, J

NANOSCALE 2025.8

Label-free morphology-based phenotypic analysis of spinal and bulbar muscular atrophy muscle cell models Open Access

Sakakibara, K; Tanaka, K; Iida, M; Imai, Y; Okada, M; Sahashi, K; Hirunagi, T; Maeda, K; Kato, R; Katsuno, M

DISEASE MODELS & MECHANISMS Vol. 18 ( 6 ) 2025.6

Language：English Publisher：Dmm Disease Models and Mechanisms

Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disorder caused by CAG trinucleotide expansion in the androgen receptor (AR) gene. To improve the quality of in vitro cell-based assays for the evaluation of potential drug candidates for SBMA, we developed a morphology-based phenotypic analysis for a muscle cell model of SBMA that involves multiparametric morphological profiling to quantitatively assess the therapeutic effects of drugs on muscle cell phenotype. The analysis was validated using dihydrotestosterone and pioglitazone, which have been shown to exacerbate and ameliorate the pathophysiology of SBMA, respectively. Gene expression analysis revealed activation of the JNK pathway in the SBMA cells compared to the control cells. Phenotypic analysis revealed the effect of naratriptan, a JNK inhibitor, on the phenotypic changes of SBMA cells, and the results were confirmed by LDH assays. We then trained a predictive machine learning model to classify the drug responses, and it successfully discriminated between pioglitazone-type and naratriptan-type morphological profiles based on their morphological characteristics. Our morphology-based phenotypic analysis provides a noninvasive and efficient screening method to accelerate the development of therapeutics for SBMA.

DOI： 10.1242/dmm.052220

Determination and validation of design space for mesenchymal stem cell cultivation processes using prediction intervals Open Access

Hirono, K; Hayashi, Y; Udugama, IA; Gaddem, MR; Tanaka, K; Takemoto, Y; Kato, R; Kino-oka, M; Sugiyama, H

COMMUNICATIONS BIOLOGY Vol. 8 ( 1 ) page： 657 2025.5

Language：English Publisher：Communications Biology

In regenerative medicine, mesenchymal stem cells (MSCs) constitute a promising therapeutic route for many diseases. The current quality-by-design guidelines do not clearly define a framework for MSC production. Here, we suggest and experimentally validate a model-based method to determine design spaces (DSs) for MSC cultivation. A kinetic model used in previous work was employed; part of the experimental data was used to re-estimate the maximum specific growth rate in the kinetic model and then calculate the prediction intervals of this parameter. Subsequently, regions of seeding density and harvesting time where both the upper and lower limits of growth predictions met the acceptable number of cells and confluency with given risk levels were defined as DSs. Finally, the established DS was validated with the remaining data; it allowed better predictions of the cell numbers and confluency under specific cultivation conditions and improved the overall robustness of MSC cultivation processes. (Figure presented.)

DOI： 10.1038/s42003-025-08063-2

Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Shiina T, Kimura K, Takemoto Y, Tanaka K, Kato R

Journal of bioscience and bioengineering Vol. 139 ( 4 ) page： 329 - 339 2025.4

Language：English Publisher：Journal of Bioscience and Bioengineering

Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-invasive cell quality evaluation. Image-based evaluation provides an efficient, quantitative, and automated approach to cell analysis, but segmentation remains a critical and challenging step. In this study, we investigated how training dataset design influenced the robustness of U-Net models for cell segmentation, focusing on challenges posed by limited data availability in cell culture. Using 2592 image pairs from four cell types representing key morphological categories, we constructed 42 investigation patterns to evaluate the effects of dataset size, dataset content, and morphological diversity on model performance. Our results showed that robust segmentation models could be developed with approximately 10 raw images captured using a 4× objective lens, a much smaller dataset than typically assumed. The dataset content was found to be crucial: training dataset images that captured commonly observed cell patterns yielded more robust models compared to those capturing rare or irregular cell patterns, which often impaired model performance with large deviations. Additionally, including both spindle and round cell morphologies in the training datasets improved model robustness when tested across all four cell types, while datasets restricted to a single morphology type could not achieve robust models. These findings highlight the importance of curating datasets that capture representative yet diverse cell morphologies. By addressing critical questions about dataset design, this study provides actionable guidance for the effective use of deep learning-based cell segmentation models in manufacturing and research applications.

DOI： 10.1016/j.jbiosc.2025.01.004

Scopus

PubMed

細胞産業における AI技術の応用と課題 Open Access

田中健二郎, 加藤竜司

人工知能 Vol. 40 ( 2 ) page： 141 - 147 2025.3

Medicinal cannabis extracts are neuroprotective against Aβ1-42-mediated toxicity in vitro Open Access

Marsh, DT; Shibuta, M; Kato, R; Smid, SD

BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY Vol. 135 ( 5 ) page： 575 - 592 2024.11

Language：English Publisher：Basic and Clinical Pharmacology and Toxicology

Background: Phytocannabinoids inhibit the aggregation and neurotoxicity of the neurotoxic Alzheimer's disease protein β amyloid (Aβ). We characterised the capacity of five proprietary medical cannabis extracts, heated and non-heated, with varying ratios of cannabidiol and Δ9-tetrahydrocannabinol and their parent carboxylated compounds to protect against lipid peroxidation and Aβ-evoked neurotoxicity in PC12 cells. Methods: Neuroprotection against lipid peroxidation and Aβ1–42-induced cytotoxicity was assessed using the thiazolyl blue tetrazolium bromide (MTT) assay. Transmission electron microscopy was used to visualise phytocannabinoid effects on Aβ1–42 aggregation and fluorescence microscopy. Results: Tetrahydrocannabinol (THC)/tetrahydrocannabinolic acid (THCA)-predominant cannabis extracts demonstrated the most significant overall neuroprotection against Aβ1–42-induced loss of PC12 cell viability. These protective effects were still significant after heating of extracts, while none of the extracts provided significant neuroprotection to lipid peroxidation via tbhp exposure. Modest inhibition of Aβ1–42 aggregation was demonstrated only with the non-heated BC-401 cannabis extract, but overall, there was no clear correlation between effects on fibrils and conferral of neuroprotection. Conclusions: These findings highlight the variable neuroprotective activity of cannabis extracts containing major phytocannabinoids THC/THCA and cannabidiol (CBD)/cannabidiolic acid (CBDA) on Aβ-evoked neurotoxicity and inhibition of amyloid β aggregation. This may inform the future use of medicinal cannabis formulations in the treatment of Alzheimer's disease and dementia.

DOI： 10.1111/bcpt.14078

Editorial: Artificial intelligence in predicting, determining and controlling cell phenotype or tissue function in inflammatory diseases Open Access

Hart, ML; Kato, R; Rolauffs, B

FRONTIERS IN IMMUNOLOGY Vol. 15 page： 1443534 2024.6

Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in Aspergillus oryzae

Oka, H; Kojima, T; Kato, R; Ihara, K; Nakano, H

JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY Vol. 22 ( 03 ) page： 2450017 2024.6

Language：English Publisher：Journal of Bioinformatics and Computational Biology

DNA-binding transcription factors (TFs) play a central role in transcriptional regulation mechanisms, mainly through their specific binding to target sites on the genome and regulation of the expression of downstream genes. Therefore, a comprehensive analysis of the function of these TFs will lead to the understanding of various biological mechanisms. However, the functions of TFs in vivo are diverse and complicated, and the identified binding sites on the genome are not necessarily involved in the regulation of downstream gene expression. In this study, we investigated whether DNA structural information around the binding site of TFs can be used to predict the involvement of the binding site in the regulation of the expression of genes located downstream of the binding site. Specifically, we calculated the structural parameters based on the DNA shape around the DNA binding motif located upstream of the gene whose expression is directly regulated by one TF AoXlnR from Aspergillus oryzae, and showed that the presence or absence of expression regulation can be predicted from the sequence information with high accuracy (AUPRC=0.8-1.0) by machine learning incorporating these parameters.

DOI： 10.1142/S0219720024500173

Web of Science

Scopus

PubMed

Image-based hybrid model incorporating initial spatial distribution for mesenchymal stem cell cultivation process design Open Access

Hirono, K; Hayashi, Y; Udugama, IA; Takemoto, Y; Kato, R; Kino-oka, M; Sugiyama, H

AICHE JOURNAL 2024.4

Publisher：AIChE Journal

This work presents an image-based hybrid model incorporating the initial spatial distribution for mesenchymal stem cell (MSC) cultivation process design. First, three levels of seeding bias in static MSC cultivation were experimentally investigated, and the resulting initial distribution was quantified using phase contrast microscopy image analysis. Second, the observed spatial heterogeneity was defined as a new parameter by calculating the standard deviation of the seeded cell number fraction among numerical 8 × 8 tiles on two-dimensional plates. Third, the parameter was incorporated into kinetic models to consider spatial growth limitation. The model was then applied to simulate MSC cultivation processes with experimental data. Using dynamic and stochastic simulation outputs, feasible ranges of cell-harvesting time could be determined to satisfy a given requirement for the minimum cell number and acceptable confluency level. The developed hybrid model could serve as a basis for quantitative decision-making in the design of MSC cultivation processes.

DOI： 10.1002/aic.18452

Open Access

Web of Science

Scopus

Prediction of antibody production performance change in Chinese hamster ovary cells using morphological profiling. Reviewed

Hisada T, Imai Y, Takemoto Y, Kanie K, Kato R

Journal of bioscience and bioengineering 2024.3

Development of novel waxy bone haemostatic agents composed of biodegradable polymers with osteogenic-enhancing peptides in rabbit models Reviewed Open Access

Ohno, T; Suenaga, H; Yamawaki-Ogata, A; Kanie, K; Kato, R; Uto, K; Ebara, M; Ito, H; Narita, Y; Usui, A; Mutsuga, M

Interdisciplinary cardiovascular and thoracic surgery Vol. 37 ( 5 ) 2023.11

The structurally diverse phytocannabinoids cannabichromene, cannabigerol and cannabinol significantly inhibit amyloid β-evoked neurotoxicity and changes in cell morphology in PC12 cells. Reviewed International coauthorship Open Access

Marsh DT, Sugiyama A, Imai Y, Kato R, Smid SD

Basic & clinical pharmacology & toxicology 2023.9

Automated cell isolation from photodegradable hydrogel based on fluorescence image analysis. Reviewed

Shinji Sugiura, Shinya Yamahira, Masato Tamura, Kazumi Shin, Mayu Shibuta, Taku Satoh, Yui Matsuzawa, Gen Fujii, Fumiki Yanagawa, Michihiro Mutoh, Masumi Yanagisawa, Ryuji Kato, Hirofumi Matsui

BIOTECHNOLOGY AND BIOENGINEERING 2023.3

Novel functional peptide for next-generation vital pulp therapy Reviewed Open Access

Watanabe Masakatsu, Okamoto Motoki, Komichi Shun, Huang Hailing, Matsumoto Sayoko, Moriyama Kiichi, Ohshima Jun, Abe Shotaro, Morita Masayoshi, Ali Manahil,, Takebe Katsuki, Kozaki Ikko, Fujimoto Akiyo, Kanie Kei, Kato Ryuji, Uto Kenichiro, Ebara Mitsuhiro, Yamawaki-Ogata Aika, Narita Yuji, Takahashi Yusuke, Hayashi Mikako

JOURNAL OF DENTAL RESEARCH Vol. 102 ( 3 ) page： 322 - 330 2023.3

Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives Reviewed Open Access

Uesaka Kazuma, Oka Hiroya, Kato Ryuji, Kanie Kei, Kojima Takaaki, Tsugawa Hiroshi, Toda Yosuke, Horinouchi Takaaki

JOURNAL OF BIOSCIENCE AND BIOENGINEERING Vol. 134 ( 5 ) page： 363 - 373 2022.11

天野日出, 木村和恵, 今井佑太, 加藤竜司, 中澤温子, 城田千代栄, 滝本愛太朗, 住田亙, 牧田智, 岡本眞宗, 安井昭洋, 高田瞬也, 中川洋一, 加藤大幾, 檜顕成, 内田広夫

小児外科 Vol. 54 ( 10 ) page： 1007 - 1011 2022.10

Morphology-based noninvasive early prediction of serial-passage potency enhances the selection of clone-derived high-potency cell bank from mesenchymal stem cells Reviewed Open Access

Suyama Takashi, Takemoto Yuto, Miyauchi Hiromi, Kato Yuko, Matsuzaki Yumi, Kato Ryuji

INFLAMMATION AND REGENERATION Vol. 42 ( 1 ) page： 30 2022.10

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells Reviewed Open Access

Imai Yuta, Iida Madoka, Kanie Kei, Katsuno Masahisa, Kato Ryuji

SCIENTIFIC REPORTS Vol. 12 ( 1 ) page： 9296 2022.6

Bi-layered carboxymethyl cellulose-collagen vitrigel dual-surface adhesion-prevention membrane Reviewed Open Access

Wang Yue, Kanie Kei, Takezawa Toshiaki, Horikawa Miki, Kaneko Kyoshiro, Sugimoto Ayako, Yamawaki-Ogata Aika, Narita Yuji, Kato Ryuji

CARBOHYDRATE POLYMERS Vol. 285 page： 119223 2022.6

Myc supports self-renewal of basal cells in the esophageal epithelium. Reviewed International coauthorship Open Access

Tomoaki Hishida, Eric Vazquez-Ferrer, Yuriko Hishida-Nozaki, Yuto Takemoto, Fumiyuki Hatanaka, Kei Yoshida, Javier Prieto, Sanjeeb Kumar Sahu, Yuta Takahashi, Pradeep Reddy, David D O'Keefe, Concepcion Rodriguez Esteban, Paul S Knoepfler, Estrella Nuñez Delicado, Antoni Castells, Josep M Campistol, Ryuji Kato, Hiroshi Nakagawa, Juan Carlos Izpisua Belmonte

FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY Vol. 10 page： 786031 2022.3

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Papers - KATO, Ryuji