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Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions

Nature Protocols volume 16, pages 634–676 (2021)Cite this article

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Abstract

Stable isotope labeling of microbial taxa of interest and their sorting provide an efficient and direct way to answer the question “who does what?” in complex microbial communities when coupled with fluorescence in situ hybridization or downstream ‘omics’ analyses. We have developed a platform for automated Raman-based sorting in which optical tweezers and microfluidics are used to sort individual cells of interest from microbial communities on the basis of their Raman spectra. This sorting of cells and their downstream DNA analysis, such as by mini-metagenomics or single-cell genomics, or cultivation permits a direct link to be made between the metabolic roles and the genomes of microbial cells within complex microbial communities, as well as targeted isolation of novel microbes with a specific physiology of interest. We describe a protocol from sample preparation through Raman-activated live cell sorting. Subsequent cultivation of sorted cells is described, whereas downstream DNA analysis involves well-established approaches with abundant methods available in the literature. Compared with manual sorting, this technique provides a substantially higher throughput (up to 500 cells per h). Furthermore, the platform has very high sorting accuracy (98.3 ± 1.7%) and is fully automated, thus avoiding user biases that might accompany manual sorting. We anticipate that this protocol will empower in particular environmental and host-associated microbiome research with a versatile tool to elucidate the metabolic contributions of microbial taxa within their complex communities. After a 1-d preparation of cells, sorting takes on the order of 4 h, depending on the number of cells required.

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Fig. 1: Workflow describing the direct linking between phenotypes and genomes of microbial cells from complex communities.
Fig. 2: Design of the microfluidic device to sort cells of interest via RACS.
Fig. 3: Selection of working fluid for the RACS.
Fig. 4: Raman microspectroscope system.
Fig. 5: Considerations in the choice of an objective for the Raman measurement and optical tweezing.
Fig. 6: Installation of the microfluidic device and syringe pumps on the Raman microscope system.
Fig. 7: Anticipated results.
Fig. 8: RACS software platform and operation algorithm.
Fig. 9: Design of the master mold (for the fabrication of eight identical devices) and configuration for the microfluidic device fabrication.
Fig. 10: Three components required to operate the RACS.

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Data availability

The datasets generated or analyzed in this protocol are available from the corresponding authors upon request. The source data used to create Figs. 3, 5c, and 7 and Supplementary Figs. 2 and 3c,d are provided as Source Data files. Source data are provided with this paper.

Code availability

The MATLAB code to operate the RACS is included in Supplementary Software 1 and 2 of this paper and is also available from GitHub (https://github.com/harubang2/MATLAB-platform-for-Raman-activated-cell-sorting-RACS).

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Acknowledgements

We acknowledge support from a US Department of Energy Joint Genome Institute Emerging Technologies Opportunity grant (DE-AC02-05CH11231 to R.S. and M.W.). R.S. acknowledges support from a Gordon and Betty Moore Foundation Marine Microbial 1775 Initiative Investigator Award (GBMF3783), a Gordon and Betty Moore Symbiosis in Aquatic Systems Initiative Investigator Award 1776 (GBMF9197), a grant from the Simons Foundation (542395) as part of the Principles of Microbial Ecosystems (PriME) Collaborative and a grant (#315230_176189) from the Swiss National Science Foundation. F.C.P. and D.B. were supported by the Austrian Science Fund (FWF; P26127-B20 and P27831-B28) and the European Research Council (Starting Grant: FunKeyGut 741623). F.C.P. was also supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation (grant no.658718). M.P. and M.W. were supported by the European Research Council via the Advanced Grant project NITRICARE 294343 and the FWF Wittgenstein award (to M.W.). L.B. was supported by grants from the Swedish Research Council (2019-04401) and the Science for Life Laboratory. We gratefully acknowledge funding from the European Molecular Biology Organization (EMBO; ALTF 1109-2016) and from the Human Frontier Science Program (HFSP; LT001209/2017) to U.A. We thank Horiba, Renishaw, and Bruker for providing their system specifications. We thank Cetoni for permission to display their software window. We thank R. Naisbit for scientific editing and M. Schmid for help with maintaining the Raman systems at the University of Vienna. We thank the M. Polz group, University of Vienna, Austria, for providing Vibrio alginolyticus 12G01 (wild type; NCBI:txid314288) and Vibrio cyclitrophicus 1F111 (wild type; NCBI:txid1136159). We thank S. Jun’s group, University of California, San Diego, for providing Escherichia coli NCM3722 ∆motA (non-motile mutant).

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Authors and Affiliations

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

    Kang Soo Lee, Uria Alcolombri & Roman Stocker

  2. Centre for Microbiology and Environmental Systems Science, Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria

    Fátima C. Pereira, Márton Palatinszky, David Berry & Michael Wagner

  3. Science for Life Laboratory, Department of Environmental Toxicology, Uppsala University, Uppsala, Sweden

    Lars Behrendt

  4. Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna, Vienna, Austria

    David Berry

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

    Michael Wagner

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Contributions

K.S.L., F.C.P., M.P., L.B., U.A. and D.B. designed the protocol and performed the experiments. R.S. and M.W. supervised the research. K.S.L. and R.S. wrote the manuscript. All authors have approved the final version of the manuscript.

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Correspondence to Kang Soo Lee, Michael Wagner or Roman Stocker.

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Key references using this protocol

Lee, K. S. et al. Nat. Microbiol. 4, 1035–1048 (2019): https://doi.org/10.1038/s41564-019-0394-9

Pereira, F. C. et al. Nat. Commun. 11, 5104 (2020): https://doi.org/10.1038/s41467-020-18928-1

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

41596_2020_427_MOESM3_ESM.mp4

Supplementary Video 1 Raman-activated cell sorting (RACS) procedures. The movie is composed of four sections: (i) visualization of flow in the microfluidic device; (ii) system calibration; (iii) case 1, the process for a captured and selected cell (sorted); and (iv) case 2, the process for a captured and rejected cell (not sorted). In section (i), the two separate flow streams do not interfere with each other–the sample flow traverses the analysis region and exits into the waste outlet by default, whereas the flow carries a cell released at the ‘release location’ (blue box) through the collection outlet. Section (ii) shows the Raman measurement of the working fluid (in the absence of a cell) to be used to calculate the PC values (eqn. (1) in the main text) during the RACS. For sections (iii) and (iv), the left and right panels represent the CCD image (obtained by the lower microscope) and the corresponding operation of the RACS software, respectively. The details of the sorting procedures are described in the lower panel of the movie.

41596_2020_427_MOESM4_ESM.zip

Supplementary Data 1 Computer-aided design (CAD) diagrams for the microfluidic sorter. The CAD file is provided as a separate supplement, “Supplementary_Data_1.dxf”. Users with access to a cleanroom facility can prepare the device from scratch (i.e., fabrication from a master mold using this photomask design and creation of the PDMS microfluidic device), whereas those who do not have expertise in microfabrication can ask commercial manufacturers to fabricate the master mold and then fabricate the PDMS microfluidic device in their laboratory.

Supplementary Data 2

Statistical source data for Supplementary Fig. 2

Supplementary Data 3

Statistical source data for Supplementary Fig. 3cd

41596_2020_427_MOESM7_ESM.zip

Supplementary Software 1 MATLAB GUI platform and corresponding script code to operate the RACS. ‘fig’ and ‘m’ files are bound together and thus should be identically named. See ‘README.pdf’ file that accompanies in the same zip file for system requirements and instructions to run this code.

41596_2020_427_MOESM8_ESM.zip

Supplementary Software 2 MATLAB GUI platform and corresponding script code to operate the RACS based on a machine-learning (K-means clustering) algorithm. ‘fig’ and ‘m’ files are bound together and thus should be identically named. See ‘README.pdf’ file that accompanies in the same zip file for system requirements and instructions to run this code.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5c

Statistical source data.

Source Data Fig. 7

Statistical source data.

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Lee, K.S., Pereira, F.C., Palatinszky, M. et al. Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions. Nat Protoc 16, 634–676 (2021). https://doi.org/10.1038/s41596-020-00427-8

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