Single-Cell Biophysics: Measurement, Modulation, and Modeling

Single-Cell Biophysics: Measurement, Modulation, and Modeling

Poster Abstracts

41-POS Board 21 The Correspondence between Raman Spectra and Total Omics Koseki J. Kobayashi-Kirschvink 1 , Hidenori Nakaoka 1 , Arisa Oda 2 , Kunihiro Ohta 2,3 , Yuichi Wakamoto 1 . 1 The University of Tokyo, Meguro-ku, Tokyo, Japan, 2 The University of Tokyo, Meguro-ku, Tokyo, Japan, 3 The University of Tokyo, Bunkyo-ku, Tokyo, Japan. Fluorescent reporters have been the de facto standard tool of in vivo research in the past two decades, allowing us to monitor specific molecules in living cells with high precision. However, the cell is a complex system with numerous molecules and interactions, and due to the specificity of fluorescent reporters, they often fail to elucidate the cellular behavior as a whole. On the other hand, omics techniques such as next generation sequencing or mass-spectrometry give us much more comprehensive molecular information about the cell, but are inherently destructive. No existing technique allows us to further analyze the dynamic molecular changes occurring at the single-cell level. In contrast, Raman micro-spectroscopy has advanced in recent years, and is one of the very few imaging techniques that can potentially report on whole-cell molecular compositions at the single-cell level in both comprehensive and non-destructive manners. However, the complexity of Raman spectra has hampered its interpretation and thus its routine use. Previous attempts on interpreting spectra mostly relied on preparing Raman spectral databases of purified materials, which is usually time-consuming and laborious, especially when dealing with biological samples. Here, we propose a method that can possibly circumvent the preparation of such databases, and instead understand how Raman spectra and “total” omics correspond to each other. Our method is based on the linear relationship between the two data sets that holds in principle, and actively employs the intrinsic low-dimensionality of gene/protein expression profiles. Example data of Raman spectra and mRNA-seq data of Schizosaccharomyces Pombe under various stress conditions are obtained, and using supervised machine learning algorithms such as the partial least squares regression, it is shown that if certain conditions are met, mRNA-seq data can possibly be predicted from Raman spectra.

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