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通过深度学习辅助的Sort-Seq可实现高通量基因表达特征的高精度分析

Huibao Feng¹, Fan Li¹, Tianmin Wang^2,3

¹MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.

Science advances

|November 8, 2023

概括

我们开发了一种深度学习的Sort-Seq方法,用于对基因表达的高通量分析. 这种方法精确地测量表达噪声和强度,揭示了细菌细胞行为和噪声控制策略的洞察力.

科学领域:

分子生物学分子生物学
系统生物学系统生物学
生物工程是生物工程.

背景情况:

在克隆群体中,细胞内蛋白质的丰富性遵循因基因表达的固有变异性而导致的分布.
了解表达强度和噪声对于细胞行为至关重要,但传统方法缓慢且费力.

研究的目的:

引入一种高通量,精确的方法来分析基因表达性质.
调查大肠杆菌中基因表达中的噪声来源.
在细菌系统中探索潜在的噪音控制策略.

主要方法:

开发一种深度学习辅助的Sort-Seq (dSort-Seq) 方法,用于高通量基因表达分析.
在生物传感器的大规模剂量反应测定中使用dSort-Seq.
对大肠杆菌对基因表达噪声的转录和转化贡献的全面调查.

主要成果:

dSort-Seq能够精确,高通量对基因表达特征进行分析.
大肠杆菌中的表达噪声与平均表达水平有很强的相关性.
由重叠的RpoD结合点引起的转录干扰对噪声产生有显著的贡献.

结论:

dSort-Seq方法为基因表达的定量分析提供了一个强大的工具.
研究结果阐明了细菌表达噪声和平均表达之间的关系.
识别转录干扰表明了对大肠杆菌噪声控制的可行策略.

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