Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high
Huibao Feng1, Fan Li1, Tianmin Wang2,3
1MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.
Science Advances
|November 8, 2023
Summary
We developed a deep-learning Sort-Seq method for high-throughput analysis of gene expression. This approach precisely measures expression noise and strength, revealing insights into cellular behavior and noise control strategies in bacteria.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioengineering
Background:
- Intracellular protein abundance in clonal populations follows a distribution due to gene expression's inherent variability.
- Understanding expression strength and noise is crucial for cellular behavior but traditional methods are slow and laborious.
Purpose of the Study:
- To introduce a high-throughput, precise method for profiling gene expression properties.
- To investigate the sources of noise in gene expression in Escherichia coli.
- To explore potential noise control strategies in bacterial systems.
Main Methods:
- Development of a deep-learning-assisted Sort-Seq (dSort-Seq) approach for high-throughput gene expression analysis.
- Application of dSort-Seq for large-scale dose-response assays of biosensors.
- Comprehensive investigation of transcriptional and translational contributions to gene expression noise in E. coli.
Main Results:
- dSort-Seq enables precise, high-throughput profiling of gene expression characteristics.
- Expression noise in E. coli is strongly correlated with the mean expression level.
- Transcriptional interference from overlapping RpoD-binding sites contributes significantly to noise production.
Conclusions:
- The dSort-Seq method offers a powerful tool for quantitative analysis of gene expression.
- Findings elucidate the relationship between expression noise and mean expression in bacteria.
- Identification of transcriptional interference suggests a feasible strategy for noise control in E. coli.
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