Updated on 2023/09/29

写真a

 
TAKEMOTO Yuto
 
Organization
Graduate School of Medicine Designated assistant professor
Title
Designated assistant professor
External link

Degree 1

  1. 博士(創薬科学) ( 2023.3   名古屋大学 ) 

 

Papers 4

  1. Morphology-based noninvasive early prediction of serial-passage potency enhances the selection of clone-derived high-potency cell bank from mesenchymal stem cells. Reviewed International journal

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

    Inflammation and regeneration   Vol. 42 ( 1 ) page: 30 - 30   2022.10

     More details

    Language:English   Publishing type:Research paper (scientific journal)  

    BACKGROUND: Rapidly expanding clones (RECs) are one of the single-cell-derived mesenchymal stem cell clones sorted from human bone marrow mononuclear cells (BMMCs), which possess advantageous features. The RECs exhibit long-lasting proliferation potency that allows more than 10 repeated serial passages in vitro, considerably benefiting the manufacturing process of allogenic MSC-based therapeutic products. Although RECs aid the preparation of large-variation clone libraries for a greedy selection of better-quality clones, such a selection is only possible by establishing multiple-candidate cell banks for quality comparisons. Thus, there is a high demand for a novel method that can predict "low-risk and high-potency clones" early and in a feasible manner given the excessive cost and effort required to maintain such an establishment. METHODS: LNGFR and Thy-1 co-positive cells from BMMCs were single-cell-sorted into 96-well plates, and only fast-growing clones that reached confluency in 2 weeks were picked up and passaged as RECs. Fifteen RECs were prepared as passage 3 (P3) cryostock as the primary cell bank. From this cryostock, RECs were passaged until their proliferation limitation; their serial-passage limitation numbers were labeled as serial-passage potencies. At the P1 stage, phase-contrast microscopic images were obtained over 6-90 h to identify time-course changes of 24 morphological descriptors describing cell population information. Machine learning models were constructed using the morphological descriptors for predicting serial-passage potencies. The time window and field-of-view-number effects were evaluated to identify the most efficient image data usage condition for realizing high-performance serial-passage potency models. RESULTS: Serial-passage test results indicated variations of 7-13-repeated serial-passage potencies within RECs. Such potency values were predicted quantitatively with high performance (RMSE < 1.0) from P1 morphological profiles using a LASSO model. The earliest and minimum effort predictions require 6-30 h with 40 FOVs and 6-90 h with 15 FOVs, respectively. CONCLUSION: We successfully developed a noninvasive morphology-based machine learning model to enhance the efficiency of establishing cell banks with single-cell-derived RECs for quantitatively predicting the future serial-passage potencies of clones. Conventional methods that can make noninvasive and quantitative predictions without wasting precious cells in the early stage are lacking; the proposed method will provide a more efficient and robust cell bank establishment process for allogenic therapeutic product manufacturing.

    DOI: 10.1186/s41232-022-00214-w

    Web of Science

    Scopus

    PubMed

    researchmap

  2. Myc Supports Self-Renewal of Basal Cells in the Esophageal Epithelium. Reviewed International journal

    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 - 786031   2022.3

     More details

    Language:English   Publishing type:Research paper (scientific journal)  

    It is widely believed that cellular senescence plays a critical role in both aging and cancer, and that senescence is a fundamental, permanent growth arrest that somatic cells cannot avoid. Here we show that Myc plays an important role in self-renewal of esophageal epithelial cells, contributing to their resistance to cellular senescence. Myc is homogeneously expressed in basal cells of the esophageal epithelium and Myc positively regulates their self-renewal by maintaining their undifferentiated state. Indeed, Myc knockout induced a loss of the undifferentiated state of esophageal epithelial cells resulting in cellular senescence while forced MYC expression promoted oncogenic cell proliferation. A superoxide scavenger counteracted Myc knockout-induced senescence, therefore suggesting that a mitochondrial superoxide takes part in inducing senescence. Taken together, these analyses reveal extremely low levels of cellular senescence and senescence-associated phenotypes in the esophageal epithelium, as well as a critical role for Myc in self-renewal of basal cells in this organ. This provides new avenues for studying and understanding the links between stemness and resistance to cellular senescence.

    DOI: 10.3389/fcell.2022.786031

    Web of Science

    Scopus

    PubMed

    researchmap

  3. Predicting quality decay in continuously passaged mesenchymal stem cells by detecting morphological anomalies. Reviewed

    Yuto Takemoto, Yuta Imai, Kei Kanie, Ryuji Kato

    Journal of bioscience and bioengineering   Vol. 131 ( 2 ) page: 198 - 206   2021.2

     More details

    Language:English   Publishing type:Research paper (scientific journal)  

    With rapid advances in cell therapy, technologies enabling both consistency and efficiency in cell manufacturing are becoming necessary. Morphological monitoring allows practical quality maintenance in cell manufacturing facilities, but relies heavily on human skill. For more reproducible and data-driven quality evaluation, image-based morphological analysis provides multiple advantages over manual observation. Our group has investigated the performance of multiple morphological parameters obtained from time-course images to non-invasively and quantitatively predict cellular quality using machine learning algorithms. Although such morphology-based computational models succeeded in early cell quality predictions, it was difficult to introduce our approach in cell manufacturing facilities owing to data variation issues. Since manufacturing facilities have fixed their protocol to minimize anomalies as much as possible, most accumulated data are normal, and anomalies are scarce. Thus, our morphological analysis had to adapt to such practical situation where it was difficult to observe a wide range of data variations, including both normal samples and anomalies, which is typically essential to improve most machine learning models' performance. In the present study, we introduce a practical morphological analysis concept by investigating the performance of anomalous quality decay discrimination during the continuous passaging of human mesenchymal stem cells (hMSCs). Combining the visualization method and asymmetric statistic discrimination, we describe an effective morphology-based, in-process quality monitoring concept to detect quality anomalies throughout cell culture process. Our results showed that the use of morphological parameters to reflect cellular population heterogeneity can predict hMSC quality decay within 6 h after seeding.

    DOI: 10.1016/j.jbiosc.2020.09.022

    Web of Science

    Scopus

    PubMed

    researchmap

  4. Reproducible production and image-based quality evaluation of retinal pigment epithelium sheets from human induced pluripotent stem cells. Reviewed International journal

    Ke Ye, Yuto Takemoto, Arisa Ito, Masanari Onda, Nao Morimoto, Michiko Mandai, Masayo Takahashi, Ryuji Kato, Fumitaka Osakada

    Scientific reports   Vol. 10 ( 1 ) page: 14387 - 14387   2020.9

     More details

    Language:English   Publishing type:Research paper (scientific journal)  

    Transplantation of retinal pigment epithelial (RPE) sheets derived from human induced pluripotent cells (hiPSC) is a promising cell therapy for RPE degeneration, such as in age-related macular degeneration. Current RPE replacement therapies, however, face major challenges. They require a tedious manual process of selecting differentiated RPE from hiPSC-derived cells, and despite wide variation in quality of RPE sheets, there exists no efficient process for distinguishing functional RPE sheets from those unsuitable for transplantation. To overcome these issues, we developed methods for the generation of RPE sheets from hiPSC, and image-based evaluation. We found that stepwise treatment with six signaling pathway inhibitors along with nicotinamide increased RPE differentiation efficiency (RPE6iN), enabling the RPE sheet generation at high purity without manual selection. Machine learning models were developed based on cellular morphological features of F-actin-labeled RPE images for predicting transepithelial electrical resistance values, an indicator of RPE sheet function. Our model was effective at identifying low-quality RPE sheets for elimination, even when using label-free images. The RPE6iN-based RPE sheet generation combined with the non-destructive image-based prediction offers a comprehensive new solution for the large-scale production of pure RPE sheets with lot-to-lot variations and should facilitate the further development of RPE replacement therapies.

    DOI: 10.1038/s41598-020-70979-y

    Web of Science

    Scopus

    PubMed

    researchmap

Books 1

  1. 論文図表を読む作法 : はじめて出会う実験&解析法も正しく解釈! : 生命科学・医学論文をスラスラ読むためのFigure事典

    牛島 俊和 , 中山 敬一( Role: Contributor)

    羊土社  2022.6  ( ISBN:9784758122603

     More details

    Total pages:288   Language:Japanese