Linear Regression Links Transcriptomic Data and Cellular Raman Spectra
Linear Regression Links Transcriptomic Data and Cellular Raman Spectra
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DOI：
10.1016/j.cels.2018.05.015
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发表时间：
2018-07-25
期刊：
影响因子：
9.3
通讯作者：
Wakamoto, Yuichi
中科院分区：
文献类型：
--
作者：
Kobayashi-Kirschvink, Koseki J.;Nakaoka, Hidenori;Wakamoto, Yuichi
拉曼显微镜是一种成像技术，已被应用于评估活细胞的分子组成，以表征细胞的类型和状态。然而，由于细胞中不同的分子种类和将峰分配给特定分子的挑战，如何解释细胞拉曼光谱尚不清楚。在这里，我们提供了确凿的证据，证明裂糖菌和大肠杆菌的细胞拉曼光谱和转录组学特征可以通过计算联系起来，从而进行解释。我们发现，通过RNA测序测量的高维拉曼光谱和转录组的维度可以减少并通过共享的低维子空间线性连接。因此，我们能够通过将计算的变换矩阵应用于拉曼光谱来预测全局基因表达谱，反之亦然。高表达的非编码rna对S. pombe的拉曼-转录组线性对应的贡献比mrna更显著。细胞拉曼光谱和转录组之间的对应证明是建立光谱活细胞组学研究的有希望的一步。
Raman microscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra and transcriptomic profiles of Schizosaccharomyces pombe and Escherichia coli can be computationally connected and thus interpreted. We find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA sequencing can be reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Highly expressed non-coding RNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs in S. pombe. This demonstration of correspondence between cellular Raman spectra and transcriptomes is a promising step toward establishing spectroscopic live-cell omics studies.