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早期和非破坏性预测人类诱导多能干细胞的分化效率,使用成像和机器学习

Miki Arai Hojo¹, Taku Tsuzuki², Yosuke Ozawa³

¹Department of Clinical Application, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan. miki.a.hojo@gmail.com.

Scientific reports

|July 23, 2025

概括

研究人员使用生物图像信息学开发了人类诱导多能干细胞 (hiPSCs) 差异化效率的早期预测系统. 这种方法有助于优化再生医学协议,特别是肌肉干细胞 (MuSCs).

科学领域:

干细胞生物学干细胞生物学
再生医学是一种再生医学.
生物图像信息学信息学

背景情况:

人类诱导多能干细胞 (hiPSCs) 的定向分化协议往往受到低可再生性和强度的影响.
差异化协议的延长诱导期阻碍了高效的优化,影响了再生疗法的应用.
肌肉干细胞 (MuSCs) 是治疗肌肉衰竭的关键细胞来源,但它们的分化协议需要优化.

研究的目的:

使用生物图像信息学开发一个早期的,非破坏性的预测系统,以提高hiPSC分化诱导效率.
为了能够更快地优化定向分化协议,特别是在肌肉干细胞生成方面.
提高基于细胞的再生疗法的可靠性和效率.

主要方法:

利用来自hiPSCs的肌肉干细胞 (MuSCs) 的定向分化协议.
在第14天和第38天之间捕获了5,712个相位对比细胞图像,在六个实验中分化.
采用快速富里埃转换 (FFT) 来从细胞图像中提取特征,并应用机器学习进行分类.

主要成果:

生物分析表明,14-38日是82日对差异化效率的预测.
快速里埃转换 (FFT) 在分化过程中成功捕获了细胞特征.
机器学习分类预测了高和低的诱导效率,在最终评估前大约有50天的时间.

结论:

通过使用生物图像信息学和机器学习,成功开发了hiPSC差异化效率的早期预测系统.
开发的系统利用FT特征提取,可以在过程中更早地预测差异化结果.
预计这种方法将加速协议优化,有助于推进肌肉衰竭等疾病的再生疗法.

关键词:

生物图像信息学生物图像信息学有针对性的差异化.早期和非破坏性的预测.人类诱导的多能干细胞 (hiPSCs)机器学习机器学习肌肉干细胞 (MuSCs) 是一种干细胞.

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