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Linear Regression Links Transcriptomic Data and Cellular Raman Spectra.

Koseki J Kobayashi-Kirschvink1, Hidenori Nakaoka2, Arisa Oda3

  • 1Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan.

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|June 25, 2018
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Summary
This summary is machine-generated.

Researchers connected cellular Raman spectra and transcriptomic profiles using computational methods. This breakthrough allows for interpreting Raman data and enables spectroscopic live-cell omics studies.

Keywords:
Raman microscopyhigh-dimensional biological data analysislive-cell omicssingle-cell analysistranscriptome

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Area of Science:

  • Biophysics
  • Molecular Biology
  • Genomics

Background:

  • Raman microscopy assesses molecular compositions of living cells for characterization.
  • Interpreting cellular Raman spectra is challenging due to molecular diversity and peak assignment difficulties.

Purpose of the Study:

  • To computationally connect cellular Raman spectra with transcriptomic profiles.
  • To establish a method for interpreting cellular Raman spectra.
  • To advance spectroscopic live-cell omics studies.

Main Methods:

  • Applied Raman microscopy to Schizosaccharomyces pombe and Escherichia coli.
  • Utilized RNA sequencing to measure transcriptomic profiles.
  • Reduced dimensionality of Raman spectra and transcriptomes.
  • Identified a shared low-dimensional subspace for linear connection.

Main Results:

  • Established a linear correspondence between cellular Raman spectra and transcriptomes.
  • Successfully predicted global gene expression profiles from Raman spectra and vice versa.
  • Found that non-coding RNAs significantly contributed to the Raman-transcriptome correspondence in S. pombe.

Conclusions:

  • Cellular Raman spectra and transcriptomic profiles can be computationally linked and interpreted.
  • This work paves the way for spectroscopic live-cell omics.
  • Non-coding RNAs play a crucial role in the observed spectral-transcriptomic relationship.