Linear Regression Links Transcriptomic Data and Cellular Raman Spectra

Abstract

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.

Raman image-activated cell sorting

“…Fifth, while in this Article we used SRS to focus on the high-frequency region (2800-3100 cm −1 ), the molecular specificity of the RIACS can be significantly increased by extending it to the fingerprint region (200-1800 cm −1 ) via high-speed coherent anti-Stokes Raman scattering spectroscopy 46 , provided that its sensitivity is high enough for probing molecular vibrations in the cell. Finally, the RIACS can be directly combined with a DNA/RNA sequencing machine to enable a large statistical study of the genotype-phenotype relations of intracellular molecules 47 , in particular small molecules including metabolites, which are previously difficult to label with fluorescent probes.…”

“…Human stem cells are useful materials for biomedical research and cell therapies such as hematopoietic stem cell transplantation 47 , mesenchymal stem cell therapy 48 , 68 , and hiPSC therapy 12 . In the case of hiPSC therapy, label-free enrichment and applications of naïve stem cells is important for increasing the efficiency of preparing target types of differentiated cells such as retinal cells for human macular degeneraiton 12 .…”

μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

“…The integration of on-chip valves in the device allowed us to actively select cells of interest, making the μCB-seq platform applicable for studies that focus on rare cell populations. 63 On-chip isolation valves prevent cellular motion due to fluid flow, thereby allowing the acquisition of even prolonged spectroscopic measurements 64 on our device. Compatibility of μCB-seq with a standard inverted microscope configuration enables the implementation of any single-objective imaging technique with working distance of 220 μm, such as coherent Raman scattering microscopy 57 or super-resolution microscopy.…”

Investigating Origins of FLIm Contrast in Atherosclerotic Lesions Using Combined FLIm-Raman Spectroscopy

“…Predictive modeling approaches based on RS in combination with other analytical methods have been reported for a wide variety of applications, such as cell ( 37 ) or transcriptome ( 38 ) identification, or active pharmaceutical ingredient content quantification ( 39 ). Both predictive and descriptive modeling may rely on the same tools (e.g., multiple regression techniques), but the purpose of predictive approaches is to use high dimensional Raman data to develop non-invasive prediction models of specific phenotypes or components.…”