Skip to main content

Recent Progress in the Development of Droplet-based Microfluidic Technologies for Phenotypic Screening using Cell-cell Interactions

Abstract

Specific cell-cell interactions enable the complex metabolic tasks associated with natural microbial communities and facilitates their improved adaptation to environmental changes when compared to monocultures. Understanding these interactions can help resolve many of the underlying mechanisms regulating these complex microbial ecosystems and supply novel insights for various applications. However, the complexity of microbial interactions makes it difficult to evaluate them individually. However, droplet-based microfluidic methods can be used to compartmentalize individual responses to specific conditions in a massively parallel manner allowing for evaluations at a single-cell resolution. Moreover, individual droplets can be withdrawn from these systems without washing or dilution and can thus be used to determine the impact of specific substances used in intercellular interactions via further analysis such as next-generation sequencing or mass spectrometry. In this review, we summarized the recent progress around droplet-based microfluidic technologies for phenotypic characterization and screening using cell-cell interaction, which continues to diversify over time expanding its application to a variety of topics.

This is a preview of subscription content, access via your institution.

References

  1. Brenner, K., L. You, and F. H. Arnold (2008) Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 26: 483–489.

    CAS  PubMed  Article  Google Scholar 

  2. Kehe, J., A. Kulesa, A. Ortiz, C. M. Ackerman, S. G. Thakku, D. Sellers, S. Kuehn, J. Gore, J. Friedman, and P. C. Blainey (2019) Massively parallel screening of synthetic microbial communities. Proc. Natl. Acad. Sci. U. S. A. 116: 12804–12809.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Zhou, K., K. Qiao, S. Edgar, and G. Stephanopoulos (2015) Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33: 377–383.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Li, L., C. Yang, W. Lan, S. Xie, C. Qiao, and J. Liu (2008) Removal of methyl parathion from artificial off-gas using a bioreactor containing a constructed microbial consortium. Environ. Sci. Technol. 42: 2136–2141.

    CAS  PubMed  Article  Google Scholar 

  5. Caballero, S., S. Kim, R. A. Carter, I. M. Leiner, B. Sušac, L. Miller, G. J. Kim, L. Ling, and E. G. Pamer (2017) Cooperating commensals restore colonization resistance to vancomycin-resistant Enterococcus faecium. Cell Host Microbe. 21: 592–602.e4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Hsu, R. H., R. L. Clark, J. W. Tan, J. C. Ahn, S. Gupta, P. A. Romero, and O. S. Venturelli (2019) Microbial interaction network inference in microfluidic droplets. Cell Syst. 9: 229–242.e4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Burmeister, A., F. Hilgers, A. Langner, C. Westerwalbesloh, Y. Kerkhoff, N. Tenhaef, T. Drepper, D. Kohlheyer, E. von Lieres, S. Noack, and A. Grünberger (2018) A microfluidic co-cultivation platform to investigate microbial interactions at defined microenvironments. Lab. Chip. 19: 98–110.

    PubMed  Article  Google Scholar 

  8. Hengoju, S., M. Tovar, D. Man, S. Buchheim, and M. A. Rosenbaum (2020) Droplet microfluidics for microbial biotechnology. Adv. Biochem. Eng. Biotechnol. Advance online publication. https://doi.org/10.1007/10_2020_140

  9. Mitri, S. and K. R. Foster (2013) The genotypic view of social interactions in microbial communities. Annu. Rev. Genet. 47: 247–273.

    CAS  PubMed  Article  Google Scholar 

  10. Momeni, B., L. Xie, and W. Shou (2017) Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. Elife. 6: e25051.

    PubMed  PubMed Central  Article  Google Scholar 

  11. Demain, A. L. and S. Sanchez (2009) Microbial drug discovery: 80 years of progress. J. Antibiot. (Tokyo) 62: 5–16.

    CAS  Article  Google Scholar 

  12. Vo, T., S. B. Shah, J. S. Choy, and X. Luo (2020) Chemotropism among populations of yeast cells with spatiotemporal resolution in a biofabricated microfluidic platform. Biomicrofluidics. 14: 014108.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Saeys, Y., S. Van Gassen, and B. N. Lambrecht (2016) Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16: 449–462.

    CAS  PubMed  Article  Google Scholar 

  14. Kaminski, T. S., O. Scheler, and P. Garstecki (2016) Droplet microfluidics for microbiology: techniques, applications and challenges. Lab. Chip. 16: 2168–2187.

    CAS  PubMed  Article  Google Scholar 

  15. Dai, J., S. H. Yoon, H. Y. Sim, Y. S. Yang, T. K. Oh, J. F. Kim, and J. W. Hong (2013) Charting microbial phenotypes in multiplex nanoliter batch bioreactors. Anal. Chem. 85: 5892–5899.

    CAS  PubMed  Article  Google Scholar 

  16. Min, S. K., B. M. Lee, J. H. Hwang, S. H. Ha, and H. S. Shin (2012) Mathematical analysis of colonial formation of embryonic stem cells in microfluidic system. Korean J. Chem. Eng. 29: 392–395.

    CAS  Article  Google Scholar 

  17. Moore, T. I., H. Tanaka, H. J. Kim, N. L. Jeon, and T.-M. Yi (2013) Yeast G-proteins mediate directional sensing and polarization behaviors in response to changes in pheromone gradient direction. Mol. Biol. Cell 24: 521–534.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Jo, M. C., W. Liu, L. Gu, W. Dang, and L. Qin (2015) High-throughput analysis of yeast replicative aging using a microfluidic system. Proc. Natl. Acad. Sci. U. S. A. 112: 9364–9369.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Taylor, R. J., D. Falconnet, A. Niemistö, S. A. Ramsey, S. Prinz, I. Shmulevich, T. Galitski, and C. L. Hansen (2009) Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform. Proc. Natl. Acad. Sci. U. S. A. 106: 3758–3763.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Paliwal, S., P. A. Iglesias, K. Campbell, Z. Hilioti, A. Groisman, and A. Levchenko (2007) MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast. Nature. 446: 46–51.

    CAS  PubMed  Article  Google Scholar 

  21. Lee, S. S., P. Horvath, S. Pelet, B. Hegemann, L. P. Lee, and M. Peter (2012) Quantitative and dynamic assay of single cell chemotaxis. Integr. Biol. (Camb.) 4: 381–390.

    CAS  Article  Google Scholar 

  22. Moore, T. I., C.-S. Chou, Q. Nie, N. L. Jeon, and T.-M. Yi (2008) Robust spatial sensing of mating pheromone gradients by yeast cells. PLoS One. 3: e3865.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Jin, S. H., S. S. Lee, B. Lee, S.-G. Jeong, M. Peter, and C.-S. Lee (2017) Programmable static droplet array for the analysis of cell-cell communication in a confined microenvironment. Anal. Chem. 89: 9722–9729.

    CAS  PubMed  Article  Google Scholar 

  24. Park, J., A. Kerner, M. A. Burns, and X. N. Lin (2011) Microdroplet-enabled highly parallel co-cultivation of microbial communities. PLoS One. 6: e17019.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Hansen, S. K., P. B. Rainey, J. A. J. Haagensen, and S. Molin (2007) Evolution of species interactions in a biofilm community. Nature. 445: 533–536.

    CAS  PubMed  Article  Google Scholar 

  26. Lidstrom, M. E. and M. C. Konopka (2010) The role of physiological heterogeneity in microbial population behavior. Nat. Chem. Biol. 6: 705–712.

    CAS  PubMed  Article  Google Scholar 

  27. Wang, M., A. L. Schaefer, A. A. Dandekar, and E. P. Greenberg (2015) Quorum sensing and policing of Pseudomonas aeruginosa social cheaters. Proc. Natl. Acad. Sci. U. S. A. 112: 2187–2191.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Jang, S., B. Lee, H.-H. Jeong, S. H. Jin, S. Jang, S. G. Kim, G. Y. Jung, and C.-S. Lee (2016) On-chip analysis, indexing and screening for chemical producing bacteria in a microfluidic static droplet array. Lab. Chip. 16: 1909–1916.

    CAS  PubMed  Article  Google Scholar 

  29. Jeong, H.-H., S. H. Jin, B. J. Lee, T. Kim, and C.-S. Lee (2015) Microfluidic static droplet array for analyzing microbial communication on a population gradient. Lab. Chip. 15: 889–899.

    CAS  PubMed  Article  Google Scholar 

  30. Jeong, H.-H., B. Lee, S. H. Jin, S.-G. Jeong, and C.-S. Lee (2016) A highly addressable static droplet array enabling digital control of a single droplet at pico-volume resolution. Lab. Chip. 16: 1698–1707.

    CAS  PubMed  Article  Google Scholar 

  31. Sun, M., S. S. Bithi, and S. A. Vanapalli (2011) Microfluidic static droplet arrays with tuneable gradients in material composition. Lab. Chip. 11: 3949–3952.

    CAS  PubMed  Article  Google Scholar 

  32. Agresti, J. J., E. Antipov, A. R. Abate, K. Ahn, A. C. Rowat, J.-C. Baret, M. Marquez, A. M. Klibanov, A. D. Griffiths, and D. A. Weitz (2010) Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proc. Natl. Acad. Sci. U. S. A. 107: 4004–4009. (Erratum published 2010, Proc. Natl. Acad. Sci. U. S. A. 107: 6550)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Huang, M., Y. Bai, S. L. Sjostrom, B. M. Hallström, Z. Liu, D. Petranovic, M. Uhlén, H. N. Joensson, H. Andersson-Svahn, and J. Nielsen (2015) Microfluidic screening and whole-genome sequencing identifies mutations associated with improved protein secretion by yeast. Proc. Natl. Acad. Sci. U. S. A. 112: E4689–E4696.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Jeong, H.-H., D. Issadore, and D. Lee (2016) Recent developments in scale-up of microfluidic emulsion generation via parallelization. Korean J. Chem. Eng. 33: 1757–1766.

    CAS  Article  Google Scholar 

  35. Wang, B. L., A. Ghaderi, H. Zhou, J. Agresti, D. A. Weitz, G. R. Fink, and G. Stephanopoulos (2014) Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption. Nat. Biotechnol. 32: 473–478.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Sjostrom, S. L., Y. Bai, M. Huang, Z. Liu, J. Nielsen, H. N. Joensson, and H. Andersson Svahn (2014) High-throughput screening for industrial enzyme production hosts by droplet microfluidics. Lab. Chip. 14: 806–813.

    CAS  PubMed  Article  Google Scholar 

  37. Terekhov, S. S., I. V. Smirnov, A. V. Stepanova, T. V. Bobik, Y. A. Mokrushina, N. A. Ponomarenko, A. A. BelogurovJr., M. P. Rubtsova, O. V. Kartseva, M. O. Gomzikova, A. A. Moskovtsev, A. S. Bukatin, M. V. Dubina, E. S. Kostryukova, V. V. Babenko, M. T. Vakhitova, A. I. Manolov, M. V. Malakhova, M. A. Kornienko, A. V. Tyakht, A. A. Vanyushkina, E. N. Ilina, P. Masson, A. G. Gabibov, and S. Altman (2017) Microfluidic droplet platform for ultrahigh-throughput single-cell screening of biodiversity. Proc. Natl. Acad. Sci. U. S. A. 114: 2550–2555.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Jarosz, D. F., J. C. S. Brown, G. A. Walker, M. S. Datta, W. L. Ung, A. K. Lancaster, A. Rotem, A. Chang, G. A. Newby, D. A. Weitz, L. F. Bisson, and S. Lindquist (2014) Cross-kingdom chemical communication drives a heritable, mutually beneficial prion-based transformation of metabolism. Cell. 158: 1083–1093.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Scanlon, T. C., S. M. Dostal, and K. E. Griswold (2014) A high-throughput screen for antibiotic drug discovery. Biotechnol. Bioeng. 111: 232–243. (Erratum published 2019, Biotechnol. Bioeng. 116: 475)

    CAS  PubMed  Article  Google Scholar 

  40. Terekhov, S. S., I. V. Smirnov, M. V. Malakhova, A. E. Samoilov, A. I. Manolov, A. S. Nazarov, D. V. Danilov, S. A. Dubiley, I. A. Osterman, M. P. Rubtsova, E. S. Kostryukova, R. H. Ziganshin, M. A. Kornienko, A. A. Vanyushkina, O. N. Bukato, E. N. Ilina, V. V. Vlasov, K. V. Severinov, A. G. Gabibov, and S. Altman (2018) Ultrahigh-throughput functional profiling of microbiota communities. Proc. Natl. Acad. Sci. U. S. A. 115: 9551–9556.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Ohan, J., B. Pelle, P. Nath, J. H. Huang, B. Hovde, M. Vuyisich, A. E. Dichosa, and S. R. Starkenburg (2019) High-throughput phenotyping of cell-to-cell interactions in gel microdroplet pico-cultures. Biotechniques. 66: 218–224.

    CAS  PubMed  Article  Google Scholar 

  42. Saleski, T. E., A. R. Kerner, M. T. Chung, C. M. Jackman, A. Khasbaatar, K. Kurabayashi, and X. N. Lin (2019) Syntrophic co-culture amplification of production phenotype for high-throughput screening of microbial strain libraries. Metab. Eng. 54: 232–243.

    CAS  PubMed  Article  Google Scholar 

  43. Siedler, S., N. K. Khatri, A. Zsohár, I. Kjærbølling, M. Vogt, P. Hammar, C. F. Nielsen, J. Marienhagen, M. O. A. Sommer, and H. N. Joensson (2017) Development of a bacterial biosensor for rapid screening of yeast p-coumaric acid production. ACS Synth. Biol. 6: 1860–1869.

    PubMed  Article  CAS  Google Scholar 

  44. Meyer, A., R. Pellaux, S. Potot, K. Becker, H.-P. Hohmann, S. Panke, and M. Held (2015) Optimization of a whole-cell biocatalyst by employing genetically encoded product sensors inside nanolitre reactors. Nat. Chem. 7: 673–678.

    CAS  PubMed  Article  Google Scholar 

  45. Lee, H., J. I. Baek, S. J. Kim, K. K. Kwon, E. Rha, S.-J. Yeom, H. Kim, D.-H. Lee, D.-M. Kim, and S.-G. Lee (2020) Sensitive and rapid phenotyping of microbes with soluble methane monooxygenase using a droplet-based assay. Front. Bioeng. Biotechnol. 8: 358.

    PubMed  PubMed Central  Article  Google Scholar 

  46. Kim, S., S. H. Jin, H. G. Lim, B. Lee, J. Kim, J. Yang, S. W. Seo, C.-S. Lee, and G. Y. Jung (2021) Synthetic cellular communication-based screening for strains with improved 3-hydroxypropionic acid secretion. Lab. Chip. 21: 4455–4463.

    CAS  PubMed  Article  Google Scholar 

  47. Tumarkin, E., L. Tzadu, E. Csaszar, M. Seo, H. Zhang, A. Lee, R. Peerani, K. Purpura, P. W. Zandstra, and E. Kumacheva (2011) High-throughput combinatorial cell co-culture using microfluidics. Integr. Biol. (Camb.) 3: 653–662.

    CAS  Article  Google Scholar 

  48. Yanakieva, D., A. Elter, J. Bratsch, K. Friedrich, S. Becker, and H. Kolmar (2020) FACS-based functional protein screening via microfluidic co-encapsulation of yeast secretor and mammalian reporter cells. Sci. Rep. 10: 10182.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Fang, Y., T. H. Chu, M. E. Ackerman, and K. E. Griswold (2017) Going native: direct high throughput screening of secreted full-length IgG antibodies against cell membrane proteins. MAbs. 9: 1253–1261.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Research Foundation of Korea grant (NRF-2019R1A2C2084631).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gyoo Yeol Jung.

Additional information

Ethical Statements

The authors declare no conflict of interest.

Neither ethical approval nor informed consent was required for this study.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kim, S., Moon, J.H. & Jung, G.Y. Recent Progress in the Development of Droplet-based Microfluidic Technologies for Phenotypic Screening using Cell-cell Interactions. Biotechnol Bioproc E (2022). https://doi.org/10.1007/s12257-022-0081-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12257-022-0081-1

Keywords

  • droplet
  • microfluidic technologies
  • phenotypic screening
  • cell-cell interaction