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相关概念视频

Replicative Cell Senescence02:15

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds the telomeric...
Replicative Cell Senescence02:15

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds the telomeric...

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Techniques to Induce and Quantify Cellular Senescence
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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
PubMed
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.

关键词:
细胞形态 细胞形态纤维细胞细胞.机器学习是机器学习.显微镜的使用方法复制性衰老是一种复制性衰老.

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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.