Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA.
Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH, 45223, USA.
Commun Biol. 2021 Nov 19;4(1):1308. doi: 10.1038/s42003-021-02807-6.
Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. To address this, we applied and benchmarked multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form clusters highly corresponding to transcriptomic and morphological cell types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. The aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.
最近的单细胞多模态数据揭示了单细胞的多尺度特征，如转录组学、形态学和电生理学。然而，整合和分析这些多模态数据以更深入地了解各种细胞特征中的功能基因组学和基因调控仍然难以实现。为了解决这个问题，我们应用并基准测试了多种机器学习方法，以对齐来自大脑倡议的小鼠大脑中单神经元细胞的基因表达和电生理数据。我们发现，非线性流形学习优于其他方法。在流形对齐后，细胞形成与转录组学和形态学细胞类型高度对应的簇，表明在细胞类型水平上基因表达与电生理学之间存在很强的非线性关系。此外，电生理特征可以通过流形对齐后的潜在空间中的基因表达高度预测。对齐后的细胞进一步显示出电生理特征的连续变化，暗示了跨簇基因表达的转变。对这些细胞簇的功能富集和基因调控网络分析揭示了从基因表达到神经元电生理学的潜在基因组功能和分子机制。
