scCamAge:一个对细胞年龄,与衰老相关的生物活性和形态测量的上下文感知预测引擎
Vishakha Gautam1, Subhadeep Duari1, Saveena Solanki1
1Department of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India.
Cell reports
|February 7, 2025
概括
我们开发了scCamAge,这是一个新的AI工具,可以分析细胞图像以了解衰老. 它准确地预测细胞衰老和对跨物种长寿治疗的反应.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 老年学是一门学科.
背景情况:
- 目前的深度学习模型难以捕捉衰老过程中复杂的时空细胞变化.
- 对细胞衰老的整体分析需要整合各种数据类型,包括成像,形态测量和分子生物活性.
研究的目的:
- 介绍scCamAge,一个用于分析细胞衰老的上下文感知多式预测引擎.
- 为了利用单细胞图像特征,形态测量和与衰老相关的生物活性进行全面的衰老预测.
- 使用酵母模型验证scCamAge的有效性,并评估其跨物种适用性.
主要方法:
- scCamAge集成基于图像的细胞时空特征与形态测量和衰老生物活性 (基因组不稳定性,线粒体功能障碍等). ) 的情况.
- 使用了大约100万个单个酵母细胞的异质数据集.
- 使用支持长寿药物,遗传突变物和压力诱导的衰老模型验证了scCamAge.
主要成果:
- scCamAge准确地预测了在热应激下酵母的亲长寿反应,由omics分析证实.
- 在酵母培训中,scCamAge在没有额外培训的情况下展示了人类纤维细胞衰老的跨物种预测能力.
- 在人类纤维细胞数据上重新训练scCamAge进一步提高了其衰老细胞预测能力.
结论:
- scCamAge为了解细胞衰老提供了一种强大的多模式方法.
- 这项研究强调了与衰老相关的形态学的进化保护.
- scCamAge显示了在跨物种的衰老研究和药物发现中广泛应用的潜力.
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