Single cell profiling framework reveals metabolic subpopulations as drivers of bioproduction heterogeneity

single-cell-profiling-framework-reveals-metabolic-subpopulations-as-drivers-of-bioproduction-heterogeneity
Single cell profiling framework reveals metabolic subpopulations as drivers of bioproduction heterogeneity

References

  1. Binder, D. et al. Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity. Metab. Eng. 42, 145–156 (2017).

    Google Scholar 

  2. Levy, S. F., Ziv, N. & Siegal, M. L. Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant. PLOS Biol. 10, e1001325 (2012).

    Google Scholar 

  3. Avraham, N., Soifer, I., Carmi, M. & Barkai, N. Increasing population growth by asymmetric segregation of a limiting resource during cell division. Mol. Syst. Biol. 9, 656 (2013).

    Google Scholar 

  4. Solopova, A. et al. Bet-hedging during bacterial diauxic shift. Proc. Natl. Acad. Sci. 111, 7427–7432 (2014).

    Google Scholar 

  5. van Heerden, J. H. et al. Lost in Transition: Start-Up of Glycolysis Yields Subpopulations of Nongrowing Cells. Science 343, 1245114 (2014).

    Google Scholar 

  6. Bagamery, L. E., Justman, Q. A., Garner, E. C. & Murray, A. W. A Putative Bet-Hedging Strategy Buffers Budding Yeast against Environmental Instability. Curr. Biol. 30, 4563–4578.e4 (2020).

    Google Scholar 

  7. Shabestary, K. et al. Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity. Nat. Commun. 15, 6515 (2024).

    Google Scholar 

  8. Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).

    Google Scholar 

  9. Kamrad, S. et al. Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC. Nat. Microbiol. 8, 441–454 (2023).

    Google Scholar 

  10. Hu, K. K. Y., Suri, A., Dumsday, G. & Haritos, V. S. Cross-feeding promotes heterogeneity within yeast cell populations. Nat. Commun. 15, 418 (2024).

    Google Scholar 

  11. Mustafi, N. et al. Application of a Genetically Encoded Biosensor for Live Cell Imaging of L-Valine Production in Pyruvate Dehydrogenase Complex-Deficient Corynebacterium glutamicum Strains. PLOS ONE 9, e85731 (2014).

    Google Scholar 

  12. Münch, K. M. et al. Polar Fixation of Plasmids during Recombinant Protein Production in Bacillus megaterium Results in Population Heterogeneity. Appl Environ. Microbiol 81, 5976–5986 (2015).

    Google Scholar 

  13. Xiao, Y., Bowen, C. H., Liu, D. & Zhang, F. Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. Nat. Chem. Biol. 12, 339–344 (2016).

    Google Scholar 

  14. Bao, Z. et al. New insights into phenotypic heterogeneity for the distinct lipid accumulation of Schizochytrium sp. H016. Biotechnol. Biofuels Bioprod. 15, 33 (2022).

    Google Scholar 

  15. Wright, N. R. et al. Emergence of Phenotypically Distinct Subpopulations Is a Factor in Adaptation of Recombinant Saccharomyces cerevisiae under Glucose-Limited Conditions. Appl. Environ. Microbiol. 88, e02307–e02321 (2022).

    Google Scholar 

  16. Xu, M., Vallières, C., Finnis, C., Winzer, K. & Avery, S. V. Exploiting phenotypic heterogeneity to improve production of glutathione by yeast. Micro. Cell Fact. 23, 267 (2024).

    Google Scholar 

  17. Lv, Y. et al. Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction. Metab. Eng. 54, 109–116 (2019).

    Google Scholar 

  18. Rugbjerg, P., Sarup-Lytzen, K., Nagy, M. & Sommer, M. O. A. Synthetic addiction extends the productive life time of engineered Escherichia coli populations. Proc. Natl. Acad. Sci. 115, 2347–2352 (2018).

    Google Scholar 

  19. Ali, A. et al. Single-cell metabolomics by mass spectrometry: Advances, challenges, and future applications. TrAC Trends Anal. Chem. 120, 115436 (2019).

    Google Scholar 

  20. Liu, Y., Liu, Y. & Wang, M. Design, Optimization and Application of Small Molecule Biosensor in Metabolic Engineering. Front. Microbiol. 8, 2012 (2017).

  21. Hossain, G. S., Saini, M., Miyake, R., Ling, H. & Chang, M. W. Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms. Trends Biotechnol. 38, 797–810 (2020).

    Google Scholar 

  22. Qiu, C., Zhai, H. & Hou, J. Biosensors design in yeast and applications in metabolic engineering. FEMS Yeast Res. 19, foz082 (2019).

    Google Scholar 

  23. Rogers, J. K., Taylor, N. D. & Church, G. M. Biosensor-based engineering of biosynthetic pathways. Curr. Opin. Biotechnol. 42, 84–91 (2016).

    Google Scholar 

  24. Torello Pianale, L., Rugbjerg, P. & Olsson, L. Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors. Front. Microbiol. 12, 802169 (2022).

  25. Newman, J. R. S. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

    Google Scholar 

  26. Silander, O. K. et al. A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli. PLoS Genet 8, e1002443 (2012).

    Google Scholar 

  27. Lee, M. E., DeLoache, W. C., Cervantes, B. & Dueber, J. E. A Highly Characterized Yeast Toolkit for Modular, Multipart Assembly. ACS Synth. Biol. 4, 975–986 (2015).

    Google Scholar 

  28. Wang, Y.-H. et al. Advances in engineering the production of the natural red pigment lycopene: A systematic review from a biotechnology perspective. J. Adv. Res. 46, 31–47 (2023).

    Google Scholar 

  29. Ahmed, A. et al. Recent Advances in Synthetic, Industrial and Biological Applications of Violacein and Its Heterologous Production. J. Microbiol Biotechnol. 31, 1465–1480 (2021).

    Google Scholar 

  30. Di Salvo, E. et al. Natural Pigments Production and Their Application in Food, Health and Other Industries. Nutrients 15, 1923 (2023).

    Google Scholar 

  31. Mahon, M. J. pHluorin2: an enhanced, ratiometric, pH-sensitive green florescent protein. Adv. Biosci. Biotechnol. 2, 132–137 (2011).

    Google Scholar 

  32. Ortega, A. D. et al. A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux. Cell Chem. Biol. 28, 1554–1568.e8 (2021).

    Google Scholar 

  33. Airoldi, E. M. et al. Predicting Cellular Growth from Gene Expression Signatures. PLoS Comput Biol. 5, e1000257 (2009).

    Google Scholar 

  34. Knudsen, J. D., Carlquist, M. & Gorwa-Grauslund, M. NADH-dependent biosensor in Saccharomyces cerevisiae: principle and validation at the single cell level. AMB Express 4, 81 (2014).

    Google Scholar 

  35. Zhang, J. et al. Engineering an NADPH/NADP+ Redox Biosensor in Yeast. ACS Synth. Biol. 5, 1546–1556 (2016).

    Google Scholar 

  36. Botman, D., van Heerden, J. H. & Teusink, B. An Improved ATP FRET Sensor For Yeast Shows Heterogeneity During Nutrient Transitions. ACS Sens 5, 814–822 (2020).

    Google Scholar 

  37. Nguyen, P. T. M., Ishiwata-Kimata, Y. & Kimata, Y. Monitoring ADP/ATP ratio in yeast cells using the fluorescent-protein reporter PercevalHR. Biosci., Biotechnol., Biochem. 83, 824–828 (2019).

    Google Scholar 

  38. Valkonen, M., Mojzita, D., Penttilä, M. & Benčina, M. Noninvasive High-Throughput Single-Cell Analysis of the Intracellular pH of Saccharomyces cerevisiae by Ratiometric Flow Cytometry. Appl. Environ. Microbiol. 79, 7179–7187 (2013).

    Google Scholar 

  39. Dechant, R. et al. Cytosolic pH is a second messenger for glucose and regulates the PKA pathway through V-ATPase. EMBO J. 29, 2515–2526 (2010).

    Google Scholar 

  40. Lucena, R. M., Dolz-Edo, L., Brul, S., de Morais, M. A. & Smits, G. Extreme Low Cytosolic pH Is a Signal for Cell Survival in Acid Stressed Yeast. Genes 11, 656 (2020).

    Google Scholar 

  41. Huberts, D. H. E. W., Niebel, B. & Heinemann, M. A flux-sensing mechanism could regulate the switch between respiration and fermentation. FEMS Yeast Res. 12, 118–128 (2012).

    Google Scholar 

  42. Yuan, H.-X., Xiong, Y. & Guan, K.-L. Nutrient Sensing, Metabolism, and Cell Growth Control. Mol. Cell 49, 379–387 (2013).

    Google Scholar 

  43. Tantama, M., Martínez-François, J. R., Mongeon, R. & Yellen, G. Imaging energy status in live cells with a fluorescent biosensor of the intracellular ATP-to-ADP ratio. Nat. Commun. 4, 2550 (2013).

    Google Scholar 

  44. Xiao, W., Wang, R.-S., Handy, D. E. & Loscalzo, J. NAD(H) and NADP(H) Redox Couples and Cellular Energy Metabolism. Antioxid. Redox Signal 28, 251–272 (2018).

    Google Scholar 

  45. Botman, D., de Groot, D. H., Schmidt, P., Goedhart, J. & Teusink, B. In vivo characterisation of fluorescent proteins in budding yeast. Sci. Rep. 9, 2234 (2019).

    Google Scholar 

  46. Ullah, A., Chandrasekaran, G., Brul, S. & Smits, G. J. Yeast adaptation to weak acids prevents futile energy expenditure. Front. Microbiol. 4, 142 (2013).

  47. Stratford, M. et al. Weak-acid preservatives: pH and proton movements in the yeast Saccharomyces cerevisiae. Int. J. Food Microbiol. 161, 164–171 (2013).

    Google Scholar 

  48. Monteiro, F. et al. Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor. Mol. Syst. Biol. 15, e9071 (2019).

    Google Scholar 

  49. Ansell, R., Granath, K., Hohmann, S., Thevelein, J. M. & Adler, L. The two isoenzymes for yeast NAD+-dependent glycerol 3-phosphate dehydrogenase encoded by GPD1 and GPD2 have distinct roles in osmoadaptation and redox regulation. EMBO J. 16, 2179–2187 (1997).

    Google Scholar 

  50. Shaw, W. M., Khalil, A. S. & Ellis, T. A Multiplex MoClo Toolkit for Extensive and Flexible Engineering of Saccharomyces cerevisiae. ACS Synth. Biol. 12, 3393–3405 (2023).

    Google Scholar 

  51. Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R. & Gresham, D. Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. eLife 9, e51254 (2020).

    Google Scholar 

  52. Gobert, A. et al. Non-Saccharomyces Yeasts Nitrogen Source Preferences: Impact on Sequential Fermentation and Wine Volatile Compounds Profile. Front. Microbiol. 8, 2175 (2017).

  53. González, A. & Hall, M. N. Nutrient sensing and TOR signaling in yeast and mammals. EMBO J. 36, 397–408 (2017).

    Google Scholar 

  54. Isom, D. G. et al. Coordinated regulation of intracellular pH by two glucose-sensing pathways in yeast. J. Biol. Chem. 293, 2318–2329 (2018).

    Google Scholar 

  55. Elsutohy, M. M. et al. Real-time measurement of the intracellular pH of yeast cells during glucose metabolism using ratiometric fluorescent nanosensors. Nanoscale 9, 5904–5911 (2017).

    Google Scholar 

  56. Valkonen, M., Mojzita, D., Penttilä, M. & Benčina, M. Noninvasive High-Throughput Single-Cell Analysis of the Intracellular pH of Saccharomyces cerevisiae by Ratiometric Flow Cytometry. Appl. Environ. Microbiol. 79, 7179 (2013).

    Google Scholar 

  57. Orij, R., Postmus, J., Ter Beek, A., Brul, S. & Smits, G. J. In vivo measurement of cytosolic and mitochondrial pH using a pH-sensitive GFP derivative in Saccharomyces cerevisiae reveals a relation between intracellular pH and growth. Microbiology. (Read.) 155, 268–278 (2009).

    Google Scholar 

  58. Duncan, J. D., Devillers, H., Camarasa, C., Setati, M. E. & Divol, B. Oxygen alters redox cofactor dynamics and induces metabolic shifts in Saccharomyces cerevisiae during alcoholic fermentation. Food Microbiol. 124, 104624 (2024).

    Google Scholar 

  59. Vasdekis, A. E., Silverman, A. M. & Stephanopoulos, G. Origins of Cell-to-Cell Bioprocessing Diversity and Implications of the Extracellular Environment Revealed at the Single-Cell Level. Sci. Rep. 5, 17689 (2015).

    Google Scholar 

  60. Hartigan, J. A. & Hartigan, P. M. The Dip Test of Unimodality. Ann. Stat. 13, 70–84 (1985).

    Google Scholar 

  61. Davidson, G. S. et al. The proteomics of quiescent and nonquiescent cell differentiation in yeast stationary-phase cultures. MBoC 22, 988–998 (2011).

    Google Scholar 

  62. Jacquel, B., Aspert, T., Laporte, D., Sagot, I. & Charvin, G. Monitoring single-cell dynamics of entry into quiescence during an unperturbed life cycle. eLife 10, e73186 (2021).

    Google Scholar 

  63. Lama, S. et al. Production of 3-hydroxypropionic acid from acetate using metabolically-engineered and glucose-grown Escherichia coli. Bioresour. Technol. 320, 124362 (2021).

    Google Scholar 

  64. Chen, Y. et al. Lycopene overproduction in Saccharomyces cerevisiae through combining pathway engineering with host engineering. Microb. Cell Factories 15, 113 (2016).

    Google Scholar 

  65. Wehrs, M. et al. Production efficiency of the bacterial non-ribosomal peptide indigoidine relies on the respiratory metabolic state in S. cerevisiae. Microb. Cell Factories 17, 193 (2018).

    Google Scholar 

  66. Sakihama, Y., Hidese, R., Hasunuma, T. & Kondo, A. Increased flux in acetyl-CoA synthetic pathway and TCA cycle of Kluyveromyces marxianus under respiratory conditions. Sci. Rep. 9, 5319 (2019).

    Google Scholar 

  67. Schroeder, L. & Ikui, A. E. Tryptophan confers resistance to SDS-associated cell membrane stress in Saccharomyces cerevisiae. PLoS One 14, e0199484 (2019).

    Google Scholar 

  68. Hirasawa, T. et al. Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis. J. Biotechnol. 131, 34–44 (2007).

    Google Scholar 

  69. Ren, X. et al. A comprehensive review and comparison of L-tryptophan biosynthesis in Saccharomyces cerevisiae and Escherichia coli. Front. Bioeng. Biotechnol. 11, 1261832 (2023).

  70. Marroquin, L. D., Hynes, J., Dykens, J. A., Jamieson, J. D. & Will, Y. Circumventing the Crabtree Effect: Replacing Media Glucose with Galactose Increases Susceptibility of HepG2 Cells to Mitochondrial Toxicants. Toxicol. Sci. 97, 539–547 (2007).

    Google Scholar 

  71. Martínez, J. L., Bordel, S., Hong, K.-K. & Nielsen, J. Gcn4p and the Crabtree effect of yeast: drawing the causal model of the Crabtree effect in Saccharomyces cerevisiae and explaining evolutionary trade-offs of adaptation to galactose through systems biology. FEMS Yeast Res. 14, 654–662 (2014).

    Google Scholar 

  72. Hewitt, C. J. & Nienow, A. W. The Scale-Up of Microbial Batch and Fed-Batch Fermentation Processes. in Advances in Applied Microbiology vol. 62, 105–135 (Academic Press, 2007).

  73. Shaner, N. C. et al. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10, 407–409 (2013).

    Google Scholar 

  74. Dietler, N. et al. A convolutional neural network segments yeast microscopy images with high accuracy. Nat. Commun. 11, 5723 (2020).

    Google Scholar 

  75. Luzia, L. et al. A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells. iScience 27, 108767 (2024).

  76. Ellis, B. et al. flowCore: Basic structures for flow cytometry data. https://doi.org/10.18129/B9.bioc.flowCore (2024).

  77. Maechler, M. diptest: Hartigan’s Dip Test Statistic for Unimodality – Corrected. (2024).

  78. Scrucca, L., Fraley, C., Murphy, T. B. & Raftery, A. E. Model-Based Clustering, Classification, and Density Estimation Using Mclust in R. (Chapman and Hall/CRC, New York, 2023). https://doi.org/10.1201/9781003277965.

Download references