Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
Department of Genetics, Yale University School of Medicine, New Haven, CT, 06510, USA.
Nat Commun. 2019 Jan 9;10(1):95. doi: 10.1038/s41467-018-07981-6.
Measuring multiple omics profiles from the same single cell opens up the opportunity to decode molecular regulation that underlies intercellular heterogeneity in development and disease. Here, we present co-sequencing of microRNAs and mRNAs in the same single cell using a half-cell genomics approach. This method demonstrates good robustness (~95% success rate) and reproducibility (R = 0.93 for both microRNAs and mRNAs), yielding paired half-cell microRNA and mRNA profiles, which we can independently validate. By linking the level of microRNAs to the expression of predicted target mRNAs across 19 single cells that are phenotypically identical, we observe that the predicted targets are significantly anti-correlated with the variation of abundantly expressed microRNAs. This suggests that microRNA expression variability alone may lead to non-genetic cell-to-cell heterogeneity. Genome-scale analysis of paired microRNA-mRNA co-profiles further allows us to derive and validate regulatory relationships of cellular pathways controlling microRNA expression and intercellular variability.
从同一个单细胞中测量多个组学谱，为解码发育和疾病中细胞间异质性的分子调控提供了机会。在这里，我们使用半细胞基因组学方法展示了同一单细胞中小 RNA 和 m RNA 的共测序。该方法表现出良好的稳健性（成功率约为 95%）和重现性（microRNA 和 m RNA 的 R 值分别为 0.93），产生了配对的半细胞 microRNA 和 m RNA 谱，我们可以独立验证。通过将 microRNA 的水平与预测的靶 m RNA 在 19 个表型相同的单细胞中的表达联系起来，我们观察到预测的靶基因与大量表达的 microRNA 的变化呈显著负相关。这表明 microRNA 表达的可变性本身可能导致非遗传的细胞间异质性。配对 microRNA-mRNA 共谱的全基因组分析还使我们能够推导和验证控制 microRNA 表达和细胞间变异性的细胞通路的调控关系。
