Raman_PhenoDiv

This repository accompanies the manscript "Raman spectroscopy-based measurements of single-cell phenotypic diversity in microbial communities" by C. Garcia-Timermans, R. Props, B. Zacchetti, M. Sakarika, F. Delvigne and N. Boon.

ABSTRACT:

1. Microbial cells can experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This, in turn, has been shown to affect community assembly and other processes such as stress tolerance, virulence or cell physiology. Metabolic stress is one such physiological change and is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species (ROS) or cell permeability. However, community measurements do not take into account phenotypic diversity, important for a better understanding and management of microbial populations. Raman spectroscopy offers a faster, non-destructive alternative that is label-free and provides detailed information on the biochemical make-up of each individual cell.

2. Here, we introduce a computational method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectral data. Using the biomolecular profile of individual cells captured in the Raman spectra, we obtain a metric by which cellular states can be compared. We calculated the minimum sample size needed for an optimal understanding of single-cell phenotypic diversity in a population. Then, we applied these methods in two case studies and described the effects in the single-cell phenotypic diversity. First, at the population level, using two Escherichia coli populations either treated with ethanol or non-treated. Then, at the subpopulation level, using two Saccharomyces cerevisiae subpopulations sorted out from the same culture based on their expression of a GFP stress reporter following nutrient limitation.

3. First, we discuss how Raman spectra can be used for single-cell phenotypic quantification using Hill numbers, and apply this framework to two case studies. Also, we were able to discriminate metabolically stressed cells using a clustering algorithm. Finally, we used the information from the Raman spectra to understand how the lipid, protein and nucleic acid composition had changed after the exposure to stress.

4. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial communities.

Acknowledgements

If you find our study useful, please consider citing:

@Article{García-Timermans2020,
  Title       = {Raman spectroscopy-based measurements of single-cell phenotypic diversity in microbial communities},
  Author      = {García-Timermans, C. and and Prop and Zacchetti, B. and Sakarika, M. and Delvigne, F. and Boon, N.},
  Journal     = {In preparation},
  Year        = {2020},
}