Paving the way for live-cell omics by Raman microscopy

A cell contains thousands of genes, and condition-dependent expression of those genes is thought to determine the cellular state collectively. However, the conventional methods for measuring comprehensive molecular compositions of cells such as transcriptome and proteome require the destruction of cells to extract intracellular RNAs and proteins to determine the amount of each component. Therefore, the methods cannot be applied to living cells. Raman spectroscopy is a method gaining attention that allows the measurement of signals coming from all molecules in living cells. Raman spectroscopy is a laser-based analytical technique that measures the shift in the energy of scattered photons caused by molecular bond vibrations. Specific molecules have unique Raman spectral signatures, which in turn allows scientists to determine the chemical species in target samples. However, owing to the complex molecular compositions of cells, discerning constituent molecular species in comprehensive manners is widely recognized as difficult, making the interpretation of cellular Raman spectra nearly impossible.

Instead of pursuing the spectral decomposition, we took a different approach and searched for the linkage between transcriptome and cellular Raman spectra computationally. We indeed found a linkage between these two high-dimensional data and revealed that one could predict the expression levels of thousands of genes from the Raman spectra in a snapshot-like manner. 

Cellular Raman spectra are measurable by a few seconds of laser exposure without destructing a cell, unlike conventional omics measurements. Furthermore, Raman measurements do not require the sample preparation procedure of extracting the cellular molecules such as RNAs and proteins. Therefore, the Raman-transcriptome correspondence characterized in this study leads to nondestructive, rapid and inexpensive measurement of transcriptomes, and paves the way toward conducting spectroscopic live-cell omics studies. 

See also:

1. Kobayashi-Kirschvink, K. J., et al. (2018) Linear Regression Links Transcriptomic Data and Cellular Raman Spectra. Cell Systems 7(1): 104-117.E4.