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Single-particle combinatorial multiplexed liposome fusion mediated by DNA

Nature Chemistry volume 14, pages 558–565 (2022)Cite this article

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Abstract

Combinatorial high-throughput methodologies are central for both screening and discovery in synthetic biochemistry and biomedical sciences. They are, however, often reliant on large-scale analyses and thus limited by a long running time and excessive materials cost. We here present a single-particle combinatorial multiplexed liposome fusion mediated by DNA for parallelized multistep and non-deterministic fusion of individual subattolitre nanocontainers. We observed directly the efficient (>93%) and leakage free stochastic fusion sequences for arrays of surface-tethered target liposomes with six freely diffusing populations of cargo liposomes, each functionalized with individual lipidated single-stranded DNA and fluorescently barcoded by a distinct ratio of chromophores. The stochastic fusion resulted in a distinct permutation of fusion sequences for each autonomous nanocontainer. Real-time total internal reflection imaging allowed the direct observation of >16,000 fusions and 566 distinct fusion sequences accurately classified using machine learning. The high-density arrays of surface-tethered target nanocontainers (~42,000 containers per mm2) offers entire combinatorial multiplex screens using only picograms of material.

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Fig. 1: Combinatorial liposome fusion mediated by DNA for the parallelized fusion with a stochastic sequence of individual zeptolitre lipid nanocontainers.
Fig. 2: Classification accuracy of barcoded liposomes using supervised ML.
Fig. 3: Quantitative and specific content mixing of subattolitre lipid nanocontainers.
Fig. 4: Quantification of HTP multiplexing.

Data availability

Data supporting the findings of this study are available in the article and Supplementary Information.https://sid.erda.dk/sharelink/DWSndIUDNTSource data are provided with this paper. All data used for the paper are also available through UCPH Erda at https://sid.erda.dk/sharelink/DWSndIUDNT.

Code availability

All the code used for tracking, event finding and ML, as well as the trained ML models, are available through UCPH Erda at https://sid.erda.dk/sharelink/DWSndIUDNT and can be found in https://github.com/hatzakislab/SPARCLD.

References

  1. Afek, A. et al. DNA mismatches reveal conformational penalties in protein–DNA recognition. Nature 587, 291–296 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fodor, S. P. et al. Multiplexed biochemical assays with biological chips. Nature 364, 555–556 (1993).

    Article  CAS  PubMed  Google Scholar 

  3. Colin, P.-Y. et al. Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics. Nat. Commun. 6, 10008 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Eun Chung, S. et al. One-step pipetting and assembly of encoded chemical-laden microparticles for high-throughput multiplexed bioassays. Nat. Commun. 5, 3468 (2014).

    Article  PubMed  CAS  Google Scholar 

  5. Attene-Ramos, M. S., Austin, C. P. & Xia, M. in Encyclopedia of Toxicology (ed. Wexler, P.) 916–917 (Elsevier, 2014).

  6. Christensen, S. M., Bolinger, P.-Y., Hatzakis, N. S., Mortensen, M. W. & Stamou, D. Mixing subattolitre volumes in a quantitative and highly parallel manner with soft matter nanofluidics. Nat. Nanotechnol. 7, 51–55 (2011).

    Article  PubMed  CAS  Google Scholar 

  7. Jahn, R. & Scheller, R. H. SNAREs—engines for membrane fusion. Nat. Rev. Mol. Cell Biol. 7, 631–643 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Solon, J., Streicher, P., Richter, R., Brochard-Wyart, F. & Bassereau, P. Vesicles surfing on a lipid bilayer: self-induced haptotactic motion. Proc. Natl Acad. Sci. USA 103, 12382–12387 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Robson Marsden, H., Elbers, N. A., Bomans, P. H. H., Sommerdijk, N. A. J. M. & Kros, A. A reduced SNARE model for membrane fusion. Angew. Chem. Int. Ed. 48, 2330–2333 (2009).

    Article  CAS  Google Scholar 

  10. Mora, N. L. et al. Controlled peptide-mediated vesicle fusion assessed by simultaneous dual-colour time-lapsed fluorescence microscopy. Sci Rep. 10, 3087 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Löffler, P. M. G. et al. A DNA-programmed liposome fusion cascade. Angew. Chem. Int. Ed. 56, 13228–13231 (2017).

    Article  CAS  Google Scholar 

  12. van Lengerich, B., Rawle, R. J., Bendix, P. M. & Boxer, S. G. Individual vesicle fusion events mediated by lipid-anchored DNA. Biophys. J. 105, 409–419 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Agasti, S. S. et al. DNA-barcoded labeling probes for highly multiplexed Exchange-PAINT imaging. Chem. Sci. 8, 3080–3091 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li, Y., Cu, Y. T. H. & Luo, D. Multiplexed detection of pathogen DNA with DNA-based fluorescence nanobarcodes. Nat. Biotechnol. 23, 885–889 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lin, C. et al. Submicrometre geometrically encoded fluorescent barcodes self-assembled from DNA. Nat. Chem. 4, 832–839 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lin, C., Liu, Y. & Yan, H. Self-assembled combinatorial encoding nanoarrays for multiplexed biosensing. Nano Lett. 7, 507–512 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hatzakis, N. S. et al. How curved membranes recruit amphipathic helices and protein anchoring motifs. Nat. Chem. Biol. 5, 835–841 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Stella, S. et al. Conformational activation promotes CRISPR-Cas12a catalysis and resetting of the endonuclease activity. Cell 175, 1856–1871.e21 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Bohr, S. S.-R., Thorlaksen, C., Kühnel, R. M., Günther-Pomorski, T. & Hatzakis, N. S. Label-free fluorescence quantification of hydrolytic enzyme activity on native substrates reveals how lipase function depends on membrane curvature. Langmuir 36, 6473–6481 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Neher, R. & Neher, E. Optimizing imaging parameters for the separation of multiple labels in a fluorescence image. J. Microsc. 213, 46–62 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Valm, A. M. et al. Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546, 162–167 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Han, M., Gao, X., Su, J. Z. & Nie, S. Quantum-dot-tagged microbeads for multiplexed optical coding of biomolecules. Nat. Biotechnol. 19, 631–635 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Fournier-Bidoz, S. et al. Facile and rapid one-step mass preparation of quantum-dot barcodes. Angew. Chem. Int. Ed. 47, 5577–5581 (2008).

    Article  CAS  Google Scholar 

  24. Wei, L. et al. Super-multiplex vibrational imaging. Nature 544, 465–470 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hu, F. et al. Supermultiplexed optical imaging and barcoding with engineered polyynes. Nat. Methods 15, 194–200 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Li, M. et al. Single enzyme experiments reveal a long-lifetime proton leak state in a heme-copper oxidase. J. Am. Chem. Soc. 137, 16055–16063 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Veshaguri, S. et al. Direct observation of proton pumping by a eukaryotic P-type ATPase. Science 351, 1469–1473 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Thomsen, R. P. et al. A large size-selective DNA nanopore with sensing applications. Nat. Commun. 10, 5655 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Bendix, P. M., Pedersen, M. S. & Stamou, D. Quantification of nano-scale intermembrane contact areas by using fluorescence resonance energy transfer. Proc. Natl Acad. Sci. USA 106, 12341–12346 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Löffler, P. M. G. et al. Lipidated polyaza crown ethers as membrane anchors for DNA-controlled content mixing between liposomes. Sci Rep. 9, 13856 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Christensen, S. M., Mortensen, M. W. & Stamou, D. G. Single vesicle assaying of SNARE-synaptotagmin-driven fusion reveals fast and slow modes of both docking and fusion and intrasample heterogeneity. Biophys. J. 100, 957–967 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Haluska, C. K. et al. Time scales of membrane fusion revealed by direct imaging of vesicle fusion with high temporal resolution. Proc. Natl Acad. Sci. USA 103, 15841–15846 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Martens, S., Kozlov, M. M. & McMahon, H. T. How synaptotagmin promotes membrane fusion. Science 316, 1205–1208 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Xu, W. et al. A programmable DNA origami platform to organize snares for membrane fusion. J. Am. Chem. Soc. 138, 4439–4447 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gao, Y. et al. Single reconstituted neuronal SNARE complexes zipper in three distinct stages. Science 337, 1340–1343 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Dragunow, M. High-content analysis in neuroscience. Nat. Rev. Neurosci. 9, 779–788 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, J. et al. New means to control molecular assembly. J. Phys. Chem. C 124, 6405–6412 (2020).

    Article  CAS  Google Scholar 

  40. Ezzoukhry, Z. et al. Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation. Nat. Commun. 9, 2031 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. He, Y. & Liu, D. R. Autonomous multistep organic synthesis in a single isothermal solution mediated by a DNA walker. Nat. Nanotechnol. 5, 778–782 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hansen, M. H. et al. A yoctoliter-scale DNA reactor for small-molecule evolution. J. Am. Chem. Soc. 131, 1322–1327 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Erb, T. J., Jones, P. R. & Bar-Even, A. Synthetic metabolism: metabolic engineering meets enzyme design. Curr. Opin. Chem. Biol. 37, 56–62 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Joesaar, A. et al. DNA-based communication in populations of synthetic protocells. Nat. Nanotechnol. 14, 369–378 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Adamala, K. P., Martin-Alarcon, D. A., Guthrie-Honea, K. R. & Boyden, E. S. Engineering genetic circuit interactions within and between synthetic minimal cells. Nat. Chem. 9, 431–439 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Gunnarsson, A. et al. Kinetics of ligand binding to membrane receptors from equilibrium fluctuation analysis of single binding events. J. Am. Chem. Soc. 133, 14852–14855 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Kaminski, T., Gunnarsson, A. & Geschwindner, S. Harnessing the versatility of optical biosensors for target-based small-molecule drug discovery. ACS Sens. 2, 10–15 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Ries, O., Löffler, P. M. G. & Vogel, S. Convenient synthesis and application of versatile nucleic acid lipid membrane anchors in the assembly and fusion of liposomes. Org. Biomol. Chem. 13, 9673–9680 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Larsen, J., Hatzakis, N. S. & Stamou, D. Observation of inhomogeneity in the lipid composition of individual nanoscale liposomes. J. Am. Chem. Soc. 133, 10685–10687 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Mortensen, K. I., Tassone, C., Ehrlich, N., Andresen, T. L. & Flyvbjerg, H. How to characterize individual nanosize liposomes with simple self-calibrating fluorescence microscopy. Nano Lett. 18, 2844–2851 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Bohr, S. S.-R. et al. Direct observation of Thermomyces lanuginosus lipase diffusional states by single particle tracking and their remodeling by mutations and inhibition. Sci Rep. 9, 16169 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Cavaluzzi, M. J. & Borer, P. N. Revised UV extinction coefficients for nucleoside-5′-monophosphates and unpaired DNA and RNA. Nucleic Acids Res. 32, e13 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank K. J. Jensen for useful discussions. This work was funded by the Villum Foundation by being part of BioNEC (grant 18333) for M.G.M., P.M.G.L., N.A.R., S.V. and N.S.H., Lundbeck Foundation grants R250-2017-1293 and R346-2020-1759 for M.Z., a Villum Foundation young investigator fellowship (grant 10099), the Carlsberg Foundation Distinguished Associate Professor Program (CF16-0797) and the NovoNordisk Center for Biopharmaceuticals and Biobarriers in Drug Delivery (NNF16OC0021948) for N.S.H. Work at The Novo Nordisk Foundation Center for Protein Research (CPR) is funded by a generous donation from the Novo Nordisk Foundation (grant no. NNF14CC0001). N.S.H. is a member of the Integrative Structural Biology Cluster (ISBUC) at the University of Copenhagen.

Author information

Authors and Affiliations

  1. Department of Chemistry & Nanoscience Centre, University of Copenhagen, Copenhagen, Denmark

    Mette Galsgaard Malle, Søren S.-R. Bohr, Magnus Berg Sletfjerding, Simon Bo Jensen, Min Zhang & Nikos S. Hatzakis

  2. Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Mette Galsgaard Malle, Søren S.-R. Bohr, Magnus Berg Sletfjerding, Simon Bo Jensen, Min Zhang & Nikos S. Hatzakis

  3. Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense, Denmark

    Philipp M. G. Löffler, Nikolaj Alexander Risgaard, Stefan Vogel & Stefan Vogel

  4. Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

    Per Hedegård

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Contributions

M.G.M., S.S.-R.B., P.M.G.L. and N.S.H. wrote the paper with feedback from all the authors. S.V. and P.M.G.L. designed the LiNAs, N.A.R. synthesized the sequences and P.M.G.L. performed all the characterization measurements in bulk. M.G.M. designed, carried out and analysed all the TIRF microscopy experiments, prepared all the liposomes and trained the MML algorithm. M.B.S. implemented the ML algorithm together with M.G.M. S.S.-R.B. and M.G.M. wrote the automated tracking and event finding analysis. M.G.M. and P.M.G.L., with inputs from S.V. and N.S.H., planned the encapsulation and content mixing liposome assays which were carried out by M.G.M. and P.M.G.L. S.B.J. and M.Z. helped with imaging and analysis. P.H. helped in analysing the data. N.S.H. conceived the project idea, in collaboration with S.V., and had the overall project management and strategy.

Corresponding authors

Correspondence to Stefan Vogel or Nikos S. Hatzakis.

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Extended data

Extended Data Fig. 1 LiNA library with aligned sequences.

Measured and calculated Tm Values and free energies of LiNA Duplexes: [a]Tm measured at 1 µM DNA, 10 mM HEPES, 110 mM Na + , pH 7.0, paired with unmodified complementary DNA and [b]measured in two unmodified sequences11. Calculated Tm [c]calculated for recognition sequence (17 bp) 110 mM Na+. [d]calculated at 10 mM HEPES, 500 mM Na + (TIRF microscope conditions). Tm values and free energies were calculated using NUPACK (www.nupack.org, and see Supporting Information).

Extended Data Fig. 2 LiNA purification and mass spectrometry data.

HPLC methods: [a] Solvent A = 0,05 M TEAA, pH = 7, solvent B = 0,05 M TEAA / ACN (1:3, v,v), pH = 7. [b]Flow = 1,4 mL/min, starting conditions are 32% B, gradient: 0 → 1, 32% B; 1 → 20, 100% B; 20 → 25, 100% B; 25 → 27, 32% B; 27 → 30, 32% B. [c]Flow = 1,4 mL/min, starting conditions are 32% B, gradient: 0 → 1, 32% B; 1 → 16, 100% B; 16 → 19, 100% B; 19 → 20, 32% B; 20 → 23,5, 32% B. [d]Flow = 2,5 mL/min, starting conditions are 4% B, gradient: 0 → 10, 100% B; 10 → 11, 100% B; 11 → 11,5, 4% B; 11,5 → 15,5, 4% B. [e] Flow = 1,4 mL/min, starting conditions are 32% B, gradient: 0 → 1, 32% B; 1 → 16, 100% B; 16 → 25, 100% C; 25 → 27, 32% C; 27 → 31, 32% C. [f] Calculated according to nearest-neighbor model52.

Extended Data Fig. 3 Quantification of the total number of specific subsequent docking and fusion events as well as the respective ad control for non-complimentary LiNA mediated fusion.

a) Red Bars: Number of fusion events divided into the number of subsequent fusion events per target liposomes. Data for specific interactions driven by both complementary LiNA. Black bars correspond to non-specific for liposomes loaded with non-complementary DNA. b) We analyzed the low number of non-specific events from Supplementary Fig. 4a, by normalizing the data with the number of experiments. The non-specific binding of one or more subsequent fusion per target liposome constitutes 4.8 ± 0.9% of the total events. Analyzing target liposomes undergoing two or more subsequent fusion events, the non-specific binding decreases to 1.4%, for targets undergoing three or more we see 0.9% non-specific binding which decreases to 0% analyzing four and above subsequent events.

Source data

Extended Data Fig. 4 Confusion matrices displaying classifications accuracy for varying number of barcode populations.

a) Confusion Matrix for the complete model (‘base’) in Supplementary Fig. 6c. Top number in each cell corresponds to the absolute number of selected data points, the parenthesized number represents the row-wise percentage of selected data points. The diagonals represent the correctly classified data, where the diagonal percentages represent the class-specific recall as a percentage. b)-d) Confusion Matrices for subset models with one, two, and three populations excluded, respectively. Removal of the most difficult to discern population by backward selection results in improved balanced accuracy. This also supports the argument for prioritizing the rank from backward selection experiments, as the recall for the LiNA A’ population in subfigure B rises drastically.

Source data

Extended Data Fig. 5 Possible distinct permutations for different sizes of cargo libraries for multiplexing assay.

Maximum number of possible distinct permutations for cargo fusions follows a Power law dependence on both number of LiNA sequences (ɣ) and number of subsequent fusion events (n). This allows recording of ɣN distinct combinatorial fusions. We have shown the robustness of the method for six barcodes and six associated distinct LiNA sequence. The table summarizes the number of possible distinct permutations for three to 6 six barcodes (and LiNAs) with one to 7 subsequent fusion events. From the table it can be seen that both number of barcodes ɣ and subsequent events (n) are important for establishing a high throughput method.

Extended Data Fig. 6 Number of docking and fusion events are not limited by LiNA depletion.

a) Doubling the amount of LiNA functionalization (gray barplots) on cargo vesicles, does not change the occurrence profile (compared to the red barplot). Data shown for B and C LiNA functionalized liposomes. b) Doubling the amount of the six LiNA sequences on all targets (gray barplot, compared to red barplot) does not alter measurably the number of successive fusion events. Data for 8830 target liposomes which was subjected to 16143 individual fusion of cargos.

Source data

Extended Data Fig. 7 Lipid mole percentage for liposome barcode preparation.

All fluorescent lipids added to the lipid mixture is stated in the units of mole percentage of ATT0-655 DOPE, ATTO-550 DOPE and DIO. The red-green-blue (RGB) barcode and the associated LiNA sequence it notated. See methods section for full liposome preparation.

Supplementary information

Supplementary Information

Supplementary Figs. 1–22, Note 1 and Table 1.

Supplementary Video 1

Intensity data from all three fluorescent channels for the ten populations in the barcoding library

Source data

Source Data Fig. 1

Intensity fusion traces (Fig. 1d,e).

Source Data Fig. 2

Raw Intensity data for machine learning (Fig. 2a), Intensity fusion traces (Fig. 2c), Barplot data (Fig. 2b), Barplot data with mean and SD (Fig. 2d,e,f).

Source Data Fig. 3

Raw Tiff images used for 3D visualization (Fig. 3b), Intensity fusion traces (Fig. 3c,d,e), Barplot data with mean and SD (Fig. 3f,g), Intensity leakages traces (Fig. 3h,i).

Source Data Fig. 4

Barplot data (Fig. 4c), Intensity fusion traces (Fig. 4e).

Source Data Extended Data Fig. 3

Barplot data with mean and SD.

Source Data Extended Data Fig. 4

Confusion matrix made upon our machine learning on the intensity data from main Fig. 2a, Folder with all machine learning code and all intensities from main Fig. 2a (also available as Source Data for Fig. 2).

Source Data Extended Data Fig. 6

Barplot data with mean and SD.

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Malle, M.G., Löffler, P.M.G., Bohr, S.SR. et al. Single-particle combinatorial multiplexed liposome fusion mediated by DNA. Nat. Chem. 14, 558–565 (2022). https://doi.org/10.1038/s41557-022-00912-5

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