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在机器学习的基础上,对人类纤维细胞的复制性衰老进行形态定量
Emma M Welter1, Sofia Benavides1, Trevor K Archer2
1Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA.
GeroScience
|November 20, 2023
概括
衰老细胞经历了显著的形态变化,包括增加大小和改变形状,因为它们达到复制性衰老. 这项研究使用机器学习管道量化了这些细胞变化.
科学领域:
- 细胞生物学 细胞生物学
- 衰老的研究研究.
- 计算生物学是一种计算生物学.
背景情况:
- 衰老在生物体和细胞层面被广泛研究.
- 单个细胞在其寿命期间的形态变化仍然很少得到量化.
- 量化细胞形态提供了对衰老过程的洞察力.
研究的目的:
- 开发和应用一种机器学习管道,用于量化复制生命周期中的细胞形态变化.
- 为了研究人类纤维细胞在培养中老化时的形态变化.
- 评估管道在不同的人类纤维细胞种群中的适用性.
主要方法:
- 使用基于机器学习的管道与标准光显微镜.
- 采用开放式访问软件用于高通量细胞表型化.
- 分析了IMR-90纤维细胞系和初级人类皮肤纤维细胞的形态变化.
主要成果:
- 量化显著增加细胞表面积,周围,假肢体和核表面积与复制性衰老.
- 观察到细胞循环度下降与复制性衰老相关.
- 从不同捐赠者的初级纤维细胞中证明了这些变化的重复.
- 成功地分类和量化了类似衰老的细胞,显示它们的数量随着衰老而增加.
结论:
- 在衰老研究中建立了一个易于访问的计算管道,用于高通量细胞表型化.
- 提供了对复制性衰老的形态特征的定量见解.
- 强调了机器学习在剖析细胞衰老过程中的实用性.
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