Raman-based sorting of microbial cells to link functions to their genes

Authors:

Kang Soo Lee1, Michael Wagner2,3 and Roman Stocker1

doi: 10.15698/mic2020.03.709
Volume 7, pp. 62 to 65, published 10/02/2020.

Affiliations:

1 Institute for Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland.

2 University of Vienna, Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, Althanstrasse 14, 1090 Vienna, Austria.

3 Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark.

Keywords: 

single cell Raman microspectroscopy, stable isotope probing, optical tweezers, microfluidic sorting, metagenomics, optofluidics

Corresponding Author(s):

Roman Stocker, Institute for Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland; romanstocker@ethz.ch

Conflict of interest statement:

The authors declare no conflict of interest.

Please cite this article as:

Kang Soo Lee, Michael Wagner and Roman Stocker (2020). Raman-based sorting of microbial cells to link functions to their genes. Microbial Cell 7(3): 62-65. doi: 10.15698/mic2020.03.709

© 2020 Lee et al. This is an open-access article released under the terms of the Creative Commons Attribution (CC BY) license, which allows the unrestricted use, distribution, and reproduc-tion in any medium, provided the original author and source are acknowledged.

Abstract:

In our recent work, we developed an optofluidic platform that allows a direct link to be made between the phenotypes (functions) and the genotypes (genes) of microbial cells within natural communities. By combining stable isotope probing, optical tweezers, Raman microspectroscopy, and microfluidics, the platform performs automated Raman-based sorting of taxa from within a complex community in terms of their functional properties. In comparison with manual sorting approaches, our method provides high throughput (up to 500 cells per hour) and very high sorting accuracy (98.3 ± 1.7%), and significantly reduces the human labour required. The system provides an efficient manner to untangle the contributions of individual members within environmental and host-associated microbiomes. In this News and Thoughts, we provide an overview of our platform, describe potential applications, suggest ways in which the system could be improved, and discuss future directions in which Raman-based analysis of microbial populations might be developed.