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从单细胞测序数据中联合推断细胞系和线粒体进化

Palash Sashittal¹, Viola Chen¹, Amey Pasarkar¹

¹Department of Computer Science, Princeton University, Princeton, NJ 08540, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

概括

线粒体突变有助于追踪细胞系,但现有的方法忽略了细胞异质体. 默林通过模拟异质体和一致性,准确地推断细胞和线粒体克隆树,改进了谱系分析.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
细胞生物学细胞生物学

背景情况:

细胞拥有具有自身基因组的线粒体,容易发生比核DNA更高的突变率.
线粒体突变对于追踪细胞谱系非常有价值,特别是在单细胞测序方面.
现有的谱系推断方法无法解释线粒体异质体,即细胞内存在多个线粒体克隆.

研究的目的:

开发一种从单细胞测序数据推断一致细胞系和线粒体克隆树的方法,明确建模异质细胞.
为了解决当前在重建细胞进化历史的方法的局限性.

主要方法:

将这个问题正式化为嵌套完美族系混合 (NPPM) 问题.
开发了MERLIN算法,使用混合整数线性程序获得精确的解决方案.
在模拟数据和来自胃癌细胞系的真实单细胞全基因组测序数据上评估了MERLIN的性能.

主要成果:

梅林准确地推断了线粒体克隆树和细胞系树,超过了不模拟异质体或树一致性的方法.
对胃癌细胞系数据的分析显示,与现有方法相比,MERLIN推断出更具生物学可信性的进化关系.
该研究提供了对NPPM问题的解决方案的组合性特征.

结论:

在重建细胞谱系方面,MERLIN通过准确地建模线粒体异质体及其与细胞谱系的一致性,为重建细胞谱系提供了重大进展.
这种方法提高了从单细胞测序数据推断的细胞谱系树的生物学可信性和准确性.
默林提供了一个强大的计算工具,用于分析细胞进化和疾病中的线粒体基因组学.

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