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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.

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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.

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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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