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评估基于MS的多组组的样本规范化方法,并将其应用于神经退行性小鼠模型.

Gwang Bin Lee1, Cha Yang2, Fenghua Hu2

  • 1Department of Chemistry & Biochemistry, University of Maryland, College Park, MD, 20742, USA. linghao1@umd.edu.

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概括
此摘要是机器生成的。

准确的多omics分析需要有效的样本正常化. 一种两步方法,通过组织重量然后蛋白质度进行正常化,最大限度地减少变异,并揭示复杂样品的真实生物差异.

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科学领域:

  • 生物医学研究生物医学研究
  • 分析化学 分析化学
  • 基因组学就是基因组学.

背景情况:

  • 基于质谱学 (MS) 的欧米学方法对于生物学中的高通量分子分析至关重要.
  • 从单个样本分析蛋白质,脂质和代谢物的多omics研究越来越可行.
  • 样本规范化至关重要,但对于每个omics类型来说通常是独立执行的,这对集成的多omics构成了挑战.

研究的目的:

  • 评估和确定最佳的样本规范化方法用于多omics分析.
  • 为了比较不同规范化策略对定量结果的影响.
  • 为了建立基于组织的多omics研究可靠的规范化协议.

主要方法:

  • 对蛋白质,脂质和代谢物的各种样本规范化技术的评估.
  • 基于组织重量和蛋白质度的正常化比较,提取前后.
  • 对GRN缺乏的小鼠大脑多omics数据应用经过验证的两步正常化方法 (组织重量然后蛋白质度).

主要成果:

  • 通过组织重量或蛋白质度进行正常化,产生了不同的定量结果.
  • 两步正常化 (提取前的组织重量,提取后的蛋白质度) 尽量减少了样本变化.
  • 对GRN淘汰赛小鼠大脑的多omics分析揭示了与溶酶体功能障碍和神经炎症相关的途径.

结论:

  • 选择的规范化方法显著影响多omics数据的解释.
  • 两步规范化策略 (组织重量,其次是蛋白质度) 提高了基于组织的多组学的可靠性.
  • 这种方法确保了准确的生物分子量化,以便在多学科研究中进行可靠的生物比较.