Correlated gene modules uncovered by high-precision single-cell transcriptomics.
Correlated gene modules uncovered by high-precision single-cell transcriptomics.
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DOI：
10.1073/pnas.2206938119
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发表时间：
2022-12-20
影响因子：
11.1
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中科院分区：
文献类型：
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在人类细胞中，特定基因的转录总是随时间波动，并且多个转录本的波动可以相关，因为它们受到相同转录因子的调节。通过开发一种具有高可检测性的单细胞转录组方法（MALBAC-DT）来测量稳态条件下mRNA丰度之间的成对相关性，我们发现了相关基因模块（CGM），这是一组表达同步的基因，以便它们共同执行某些生物学功能，例如蛋白质合成或胆固醇合成。 CGM 通过蛋白质与蛋白质的相互作用提供有关基因组生物学功能的信息。基因表达的相关性用于推断基因之间的功能和调控关系。然而，相关性通常是在不同的细胞类型或扰动之间计算的，导致具有不相关功能的基因相关。在这里，我们证明通过测量单细胞中稳态基因表达波动的相关性可以更好地捕获相关模块。我们报告了一种称为 MALBAC-DT 的高精度单细胞 RNA 测序方法，用于测量同质细胞群中任何一对基因之间的相关性。使用这种方法，我们能够识别许多细胞类型特异性且功能丰富的相关基因模块。我们通过敲除证实，富集 p53 信号传导的模块比 ChIP-seq 研究的共识更准确地预测 p53 调控目标，并且稳态相关性可以预测转录组范围内对扰动的反应模式。这种方法提供了一种强有力的方法来促进我们对基因组功能的理解。
In a human cell, transcription of a particular gene invariably fluctuates with time, and the fluctuations of several transcripts can be correlated because they are regulated by the same transcription factor. By developing a single-cell transcriptome method with high detectability (MALBAC-DT) to measure pair-wise correlation among mRNA abundance under steady-state conditions, we discovered correlated gene modules (CGMs), a group of genes whose expression are synchronized in order for them to work together to carry out certain biological functions, such as protein synthesis or cholesterol synthesis. CGMs provide information regarding genome’s biological functions through protein-to-protein interactions. Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.
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