Deep learning-based identification of sinoatrial node-like pacemaker cells from SHOX2/HCN4 double positive cells differentiated from human iPS cells

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K08423
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 53020:Cardiology-related
• Research Institution: Tottori University
• Principal Investigator: HISATOME Ichiro 鳥取大学, 医学部, 特任教員 (60211504)
• Co-Investigator(Kenkyū-buntansha): 三明 淳一朗 鳥取大学, 医学部, 准教授 (40372677) ; 經遠 智一 鳥取大学, 医学部, 助教 (60730207) ; 白吉 安昭 鳥取大学, 医学部, 准教授 (90249946) ; 森川 久未 国立研究開発法人産業技術総合研究所, 生命工学領域, 研究員 (90707217)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: iPS / sinoatrial node / deep learning / automaticity

Outline of Final Research Achievements

Cardiomyocytes derived from human iPS cells (hiPSCs) include cells showing sinoatrial node (SAN)- and non-SAN (atrial and ventricular)-like spontaneous action potentials (APs). We examined whether the deep learning technology could identify hiPSC-derived SAN-like cells showing SAN-type-APs by their shape. We acquired phase-contrast images for hiPSC-derived SHOX2/HCN4 double positive SAN-like and non-SAN-like cells, and made VGG16-based convolutional neural network (CNN) model, which classify an input image as SAN-like or non-SAN-like cell. Compared to human discriminability all parameter values as accuracy, recall, specificity and precision obtained from trained CNN model were higher than those of human classification. Deep learning technology could identify hiPSC-derived SAN-like cells with considerable accuracy.

Free Research Field

Cardiology

Academic Significance and Societal Importance of the Research Achievements

二値化モデルを用いた機械学習は遺伝子搭載ヒトiPS細胞由来ペースメーカ細胞の形態を認識していることが判明した。この結果は機械学習が遺伝子を搭載したヒトiPS細胞から分化誘導したペースメーカ細胞を選別採取出来る可能性を示している。今後は本機械学習を用いて遺伝子改変をしないヒトiPS細胞から心筋分化誘導後のペースメーカ細胞を純化できるかを検討するが、本研究が成功すれば徐脈性不整脈患者に対しての新たな再生医療となり得るために、医療に与える好影響は計り知れない。