A DNA-nanoassembly-based approach to map membrane protein nanoenvironments


Most proteins at the plasma membrane are not uniformly distributed but localize to dynamic domains of nanoscale dimensions. To investigate their functional relevance, there is a need for methods that enable comprehensive analysis of the compositions and spatial organizations of membrane protein nanodomains in cell populations. Here we describe the development of a non-microscopy-based method for ensemble analysis of membrane protein nanodomains. The method, termed nanoscale deciphering of membrane protein nanodomains (NanoDeep), is based on the use of DNA nanoassemblies to translate membrane protein organization information into a DNA sequencing readout. Using NanoDeep, we characterized the nanoenvironments of Her2, a membrane receptor of critical relevance in cancer. Importantly, we were able to modulate by design the inventory of proteins analysed by NanoDeep. NanoDeep has the potential to provide new insights into the roles of the composition and spatial organization of protein nanoenvironments in the regulation of membrane protein function.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Schematic of the NanoDeep method.
Fig. 2: NanoComb characterization.
Fig. 3: Toehold exchange reversibly blocked the hybridization of affibody–oligonucleotide conjugates to the NanoCombs.
Fig. 4: DNA polymerase and nuclease reactions generated barcoded dsDNA sequences.
Fig. 5: NanoDeep on model SPR surfaces.
Fig. 6: NanoDeep on cells.
Fig. 7: NanoDeep with expanded library.

Data availability

All data supporting the results of this study are available from the Swedish National Data Service (https://snd.gu/se/en). Source data are provided with this paper.

Code availability

Codes for UMI processing and barcode association are available online at https://github.com/Intertangler/NanoDeep.


  1. 1.

    Sengupta, P. et al. Probing protein heterogeneity in the plasma membrane using PALM and pair correlation analysis. Nat. Methods 8, 969–975 (2011).

    CAS  Article  Google Scholar 

  2. 2.

    Bethani, I., Skånland, S. S., Dikic, I. & Acker-Palmer, A. Spatial organization of transmembrane receptor signalling. EMBO J. 29, 2677–2688 (2010).

    CAS  Article  Google Scholar 

  3. 3.

    Rossier, O. et al. Integrins β1 and β3 exhibit distinct dynamic nanoscale organizations inside focal adhesions. Nat. Cell Biol. 14, 1057–1067 (2012).

    CAS  Article  Google Scholar 

  4. 4.

    Winckler, P. et al. Identification and super-resolution imaging of ligand-activated receptor dimers in live cells. Sci. Rep. 3, 2387 (2013).

    Article  Google Scholar 

  5. 5.

    Garcia-Parajo, M. F., Cambi, A., Torreno-Pina, J. A., Thompson, N. & Jacobson, K. Nanoclustering as a dominant feature of plasma membrane organization. J. Cell. Sci. 127, 4995–5005 (2014).

    Article  CAS  Google Scholar 

  6. 6.

    Sigal, Y. M., Zhou, R. & Zhuang, X. Visualizing and discovering cellular structures with super-resolution microscopy. Science 361, 880–887 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. Cell Biol. 18, 685–701 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Jungmann, R. et al. Quantitative super-resolution imaging with qPAINT. Nat. Methods 13, 439–442 (2016).

    CAS  Article  Google Scholar 

  9. 9.

    Jungmann, R. et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11, 313–318 (2014).

    CAS  Article  Google Scholar 

  10. 10.

    Klevanski, M. et al. Automated highly multiplexed super-resolution imaging of protein nano-architecture in cells and tissues. Nat. Commun. 11, 1552 (2020).

    CAS  Article  Google Scholar 

  11. 11.

    Beghin, A. et al. Localization-based super-resolution imaging meets high-content screening. Nat. Methods 14, 1184–1190 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Dirks, R. M. & Pierce, N. A. Triggered amplification by hybridization chain reaction. Proc. Natl Acad. Sci. USA 101, 15275–15278 (2004).

    CAS  Article  Google Scholar 

  13. 13.

    Söderberg, O. et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat. Methods 3, 995–1000 (2006).

    Article  CAS  Google Scholar 

  14. 14.

    Fredriksson, S. et al. Protein detection using proximity-dependent DNA ligation assays. Nat. Biotechnol. 20, 473–477 (2002).

    CAS  Article  Google Scholar 

  15. 15.

    Hoffecker, I. T., Yang, Y., Bernardinelli, G., Orponen, P. & Högberg, B. A computational framework for DNA sequencing microscopy. Proc. Natl Acad. Sci. USA 116, 19282–19287 (2019).

    CAS  Article  Google Scholar 

  16. 16.

    Weinstein, J. A., Regev, A. & Zhang, F. DNA microscopy: optics-free spatio-genetic imaging by a stand-alone chemical reaction. Cell 178, 229–241.e216 (2019).

    CAS  Article  Google Scholar 

  17. 17.

    Boulgakov, A. A., Ellington, A. D. & Marcotte, E. M. Bringing microscopy-by-sequencing into view. Trends Biotechnol. 38, 154–162 (2019).

    Article  CAS  Google Scholar 

  18. 18.

    Rubin, I. & Yarden, Y. The basic biology of HER2. Ann. Oncol. 12(Suppl. 1), S3–S8 (2001).

    Article  Google Scholar 

  19. 19.

    Yarden, Y. Biology of HER2 and its importance in breast cancer. Oncology 61(Suppl. 2), 1–13 (2001).

    CAS  Article  Google Scholar 

  20. 20.

    Yarden, Y. & Sliwkowski, M. X. Untangling the ErbB signalling network. Nat. Rev. Mol. Cell Biol. 2, 127–137 (2001).

    CAS  Article  Google Scholar 

  21. 21.

    Peckys, D. B., Korf, U. & de Jonge, N. Local variations of HER2 dimerization in breast cancer cells discovered by correlative fluorescence and liquid electron microscopy. Sci. Adv. 1, e1500165 (2015).

    Article  CAS  Google Scholar 

  22. 22.

    Stove, C. & Bracke, M. Roles for neuregulins in human cancer. Clin. Exp. Metastasis 21, 665–684 (2004).

    CAS  Article  Google Scholar 

  23. 23.

    Ho-Pun-Cheung, A. et al. Quantification of HER expression and dimerization in patients’ tumor samples using time-resolved Förster resonance energy transfer. PLoS ONE 7, e37065 (2012).

    CAS  Article  Google Scholar 

  24. 24.

    Weitsman, G. et al. HER2–HER3 dimer quantification by FLIM-FRET predicts breast cancer metastatic relapse independently of HER2 IHC status. Oncotarget 7, 51012–51026 (2016).

    Article  Google Scholar 

  25. 25.

    Claus, J. et al. Inhibitor-induced HER2–HER3 heterodimerisation promotes proliferation through a novel dimer interface. eLife 7, e32271 (2018).

    Article  Google Scholar 

  26. 26.

    Jeon, M. et al. Dimerization of EGFR and HER2 induces breast cancer cell motility through STAT1-dependent ACTA2 induction. Oncotarget 8, 50570–50581 (2017).

    Article  Google Scholar 

  27. 27.

    Kaufmann, R., Müller, P., Hildenbrand, G., Hausmann, M. & Cremer, C. Analysis of Her2/neu membrane protein clusters in different types of breast cancer cells using localization microscopy. J. Microsc. 242, 46–54 (2011).

    CAS  Article  Google Scholar 

  28. 28.

    Iqbal, N. Human epidermal growth factor receptor 2 (HER2) in cancers: overexpression and therapeutic implications. Mol. Biol. Int. 2014, 852748 (2014).

    Article  CAS  Google Scholar 

  29. 29.

    Needham, S. R. et al. EGFR oligomerization organizes kinase-active dimers into competent signalling platforms. Nat. Commun. 7, 13307 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Hiroshima, M. et al. Transient acceleration of epidermal growth factor receptor dynamics produces higher-order signaling clusters. J. Mol. Biol. 430, 1386–1401 (2018).

    CAS  Article  Google Scholar 

  31. 31.

    Liang, S. I. et al. Phosphorylated EGFR dimers are not sufficient to activate Ras. Cell. Rep. 22, 2593–2600 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    van Lengerich, B., Agnew, C., Puchner, E. M., Huang, B. & Jura, N. EGF and NRG induce phosphorylation of HER3/ERBB3 by EGFR using distinct oligomeric mechanisms. Proc. Natl Acad. Sci. USA 114, E2836–E2845 (2017).

    Article  CAS  Google Scholar 

  33. 33.

    Baumann, C. G., Smith, S. B., Bloomfield, V. A. & Bustamante, C. Ionic effects on the elasticity of single DNA molecules. Proc. Natl Acad. Sci. USA 94, 6185–6190 (1997).

    CAS  Article  Google Scholar 

  34. 34.

    Bernardinelli, G. & Högberg, B. Entirely enzymatic nanofabrication of DNA–protein conjugates. Nucleic Acids Res. 45, e160 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Yurke, B., Turberfield, A. J., Mills, A. P., Simmel, F. C. & Neumann, J. L. A DNA-fuelled molecular machine made of DNA. Nature 406, 605–608 (2000).

    CAS  Article  Google Scholar 

  36. 36.

    Xu, H. et al. Enhanced DNA toehold exchange reaction on a chip surface to discriminate single-base changes. Chem. Commun. 50, 14171–14174 (2014).

    CAS  Article  Google Scholar 

  37. 37.

    Tan, M., Grijalva, R. & Yu, D. Heregulin beta1-activated phosphatidylinositol 3-kinase enhances aggregation of MCF-7 breast cancer cells independent of extracellular signal-regulated kinase. Cancer Res. 59, 1620–1625 (1999).

    CAS  Google Scholar 

  38. 38.

    Breuleux, M. Role of heregulin in human cancer. Cell Mol. Life Sci. 64, 2358–2377 (2007).

    CAS  Article  Google Scholar 

  39. 39.

    Yang, C., Klein, E. A., Assoian, R. K. & Kazanietz, M. G. Heregulin beta1 promotes breast cancer cell proliferation through Rac/ERK-dependent induction of cyclin D1 and p21Cip1. Biochem. J. 410, 167–175 (2008).

    CAS  Article  Google Scholar 

  40. 40.

    Atlas, E. et al. Heregulin is sufficient for the promotion of tumorigenicity and metastasis of breast cancer cells in vivo. Mol. Cancer Res. 1, 165–175 (2003).

    CAS  Google Scholar 

  41. 41.

    Huang, Y. et al. Molecular basis for multimerization in the activation of the epidermal growth factor receptor. eLife 5, e14107 (2016).

    Article  CAS  Google Scholar 

  42. 42.

    Desgrosellier, J. S. & Cheresh, D. A. Integrins in cancer: biological implications and therapeutic opportunities. Nat. Rev. Cancer 10, 9–22 (2010).

    CAS  Article  Google Scholar 

  43. 43.

    Stoeltzing, O. et al. Inhibition of integrin α5β1 function with a small peptide (ATN-161) plus continuous 5-FU infusion reduces colorectal liver metastases and improves survival in mice. Int. J. Cancer 104, 496–503 (2003).

    CAS  Article  Google Scholar 

  44. 44.

    Khalili, P. et al. A non-RGD-based integrin binding peptide (ATN-161) blocks breast cancer growth and metastasis in vivo. Mol. Cancer Ther. 5, 2271–2280 (2006).

    CAS  Article  Google Scholar 

  45. 45.

    Kuwada, S. K., Kuang, J. & Li, X. Integrin α5/β1 expression mediates HER-2 down-regulation in colon cancer cells. J. Biol. Chem. 280, 19027–19035 (2005).

    CAS  Article  Google Scholar 

  46. 46.

    Wang, S. E. et al. Transforming growth factor β induces clustering of HER2 and integrins by activating Src-focal adhesion kinase and receptor association to the cytoskeleton. Cancer Res. 69, 475–482 (2009).

    CAS  Article  Google Scholar 

  47. 47.

    Pols, M. S. & Klumperman, J. Trafficking and function of the tetraspanin CD63. Exp. Cell. Res. 315, 1584–1592 (2009).

    CAS  Article  Google Scholar 

  48. 48.

    Khushman, M. et al. Exosomal markers (CD63 and CD9) expression and their prognostic significance using immunohistochemistry in patients with pancreatic ductal adenocarcinoma. J. Gastrointest. Oncol. 10, 695–702 (2019).

    Article  Google Scholar 

  49. 49.

    Ng, Y. H. et al. Endometrial exosomes/microvesicles in the uterine microenvironment: a new paradigm for embryo–endometrial cross talk at implantation. PLoS ONE 8, e58502 (2013).

    CAS  Article  Google Scholar 

  50. 50.

    de Goeij, B. E. et al. Efficient payload delivery by a bispecific antibody–drug conjugate targeting HER2 and CD63. Mol. Cancer Ther. 15, 2688–2697 (2016).

    Article  Google Scholar 

  51. 51.

    Livant, D. L. et al. Anti-invasive, antitumorigenic, and antimetastatic activities of the PHSCN sequence in prostate carcinoma. Cancer Res. 60, 309–320 (2000).

    CAS  Google Scholar 

  52. 52.

    Maier, K. E. et al. A new transferrin receptor aptamer inhibits new world hemorrhagic fever mammarenavirus entry. Mol. Ther. Nucleic Acids 5, e321 (2016).

    CAS  Article  Google Scholar 

  53. 53.

    Porciani, D. et al. Modular cell-internalizing aptamer nanostructure enables targeted delivery of large functional RNAs in cancer cell lines. Nat. Commun. 9, 2283 (2018).

    Article  CAS  Google Scholar 

  54. 54.

    Orlova, A. et al. Tumor imaging using a picomolar affinity HER2 binding affibody molecule. Cancer Res. 66, 4339–4348 (2006).

    CAS  Article  Google Scholar 

  55. 55.

    Malm, M. et al. Inhibiting HER3-mediated tumor cell growth with affibody molecules engineered to low picomolar affinity by position-directed error-prone PCR-like diversification. PLoS ONE 8, e62791 (2013).

    CAS  Article  Google Scholar 

  56. 56.

    Andersson, K. G. et al. Feasibility of imaging of epidermal growth factor receptor expression with ZEGFR:2377 affibody molecule labeled with 99mTc using a peptide-based cysteine-containing chelator. Int. J. Oncol. 49, 2285–2293 (2016).

    CAS  Article  Google Scholar 

  57. 57.

    Popovic, M., Mazzega, E., Toffoletto, B. & de Marco, A. Isolation of anti-extra-cellular vesicle single-domain antibodies by direct panning on vesicle-enriched fractions. Microb. Cell Fact. 17, 6 (2018).

    Article  CAS  Google Scholar 

  58. 58.

    Oloketuyi, S. et al. Electrochemical immunosensor functionalized with nanobodies for the detection of the toxic microalgae Alexandrium minutum using glassy carbon electrode modified with gold nanoparticles. Biosens. Bioelectron. 154, 112052 (2020).

    CAS  Article  Google Scholar 

  59. 59.

    Reddington, S. C. & Howarth, M. Secrets of a covalent interaction for biomaterials and biotechnology: SpyTag and SpyCatcher. Curr. Opin. Chem. Biol. 29, 94–99 (2015).

    CAS  Article  Google Scholar 

  60. 60.

    Keeble, A. H. et al. Evolving accelerated amidation by SpyTag/SpyCatcher to analyze membrane dynamics. Angew. Chem. Int. Ed. Engl. 56, 16521–16525 (2017).

    CAS  Article  Google Scholar 

  61. 61.

    Li, L., Fierer, J. O., Rapoport, T. A. & Howarth, M. Structural analysis and optimization of the covalent association between SpyCatcher and a peptide tag. J. Mol. Biol. 426, 309–317 (2014).

    CAS  Article  Google Scholar 

  62. 62.

    Zakeri, B. et al. Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proc. Natl Acad. Sci. USA 109, E690–E697 (2012).

    CAS  Article  Google Scholar 

  63. 63.

    Hatlem, D., Trunk, T., Linke, D. & Leo, J. C. Catching a SPY: using the SpyCatcher–SpyTag and related systems for labeling and localizing bacterial proteins. Int. J. Mol. Sci. 20, 2129 (2019).

    CAS  Article  Google Scholar 

Download references


We acknowledge B. Reinius for helpful discussions and S. Dal Zilio for the development and fabrication of micropatterned surfaces, performed at the Facility of Nano Fabrication FNF-IOM, CNR, Trieste. A.I.T. acknowledges support from the European Research Council under the European Union’s Seventh Framework Programme (ERC, grant no. 617711), the Swedish Research Council (grant no. 2015-03520) and the Knut and Alice Wallenberg Foundation (grant no. KAW 2017.0114, A.I.T. and B.H.).

Author information




E.A. designed the study and performed the experiments; G.B. designed the affibody plasmids; G.B. and B.H. provided key insights for the design of experiments; I.H. developed NGS data analysis; L.H. and R.S. contributed to performance and interpretation of the NGS experiments; G.K. and A.d.M. contributed to development of the expanded library. A.I.T. conceived and supervised the study; E.A. and A.I.T. wrote the manuscript, with input from all authors; all authors contributed to the manuscript revision and gave approval to the final version.

Corresponding author

Correspondence to Ana I. Teixeira.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Binding affinity and target selectivity characterization of VirD2-affibody fusion proteins.

a, SPR binding analysis of VirD2-affibody fusion proteins targeting Her2, Her3 and EGFR performed at concentrations ranging between 0.33 nM and 5 nM, to cover the kinetic spectrum. Fitting was performed using a 1:1 kinetic model to determine the dissociation constant, KD, association (kon) and dissociation (koff) rate constants. Recorded sensorgrams are shown in black, while fitting curves are in red. b, Selective binding of VirD2-affibody fusion proteins to their specific targets was verified by recoding the binding sensorgrams of VirD2-affibodies to the ECDs of Her2, Her3 and EGFR immobilized on three different flow cells.

Extended Data Fig. 2 Characterization of affibody-oligo conjugates.

a, Equivalent amounts of anti-Her2-, anti-Her3- or anti-EGFR-VirD2-affibody fusion proteins were incubated with increasing concentrations of their corresponding oligos for 2 h at 37 °C. Native PAGE (10%) was used to detect DNA by staining with SybrGold. The same gel was stained with Coomassie Blue to visualize proteins, thereby revealing the formation of DNA–protein conjugates. b, SPR real-time kinetic analysis of VirD2-affibodies (light color) and the respective VirD2-affibody-oligo conjugates (dark color) on sensor surfaces functionalized with their respective target proteins. The dissociation rate constants (koff) reported for each sensorgram were determined by fitting with 1:1 kinetic model.

Extended Data Fig. 3 Toehold exchange reversibly blocked the hybridization of oligos to the NanoCombs.

Biotinylated versions of the oligos used to produce the affibody-oligo conjugates were immobilized onto streptavidin sensor surfaces. a, Hybridization with the blocking strand caused an increase in the SPR signal, which was followed by a decrease in the signal when the invading strand was injected. The resulting unblocked oligos were capable of hybridizing to the NanoCombs. b, Without the strand invasion step, the blocked oligos were not able to bind to the NanoCombs (negative control). c, In the positive control there were no blocking/unblocking steps and the NanoComb hybridized to the anchored oligos.

Extended Data Fig. 4 Binding sensorgrams of library binders on SPR surfaces presenting different compositions of EGFR family receptors.

We created SPR surfaces presenting different combinations of the ECD-Her2, -Her3 and -EGFR, covalently attached to three different flow cells of the same sensor chip. Then we performed NanoDeep using Her2-NanoCombs and binder libraries consisting of anti-Her2, -Her3 and -EGFR conjugates. Sensorgrams display the binding of each of the library binders to anchored proteins. The binding of each of the affibody-oligo conjugates is specific to the targets present on the surface.

Extended Data Fig. 5 Assessment of the specificity of the binding of NanoCombs to cells.

a, NanoDeep was performed on SKBR3 cells, using Her2-NanoCombs or NanoCombs that were not functionalized with anti-Her2 affibody-oligo conjugate on the reference prong, as a negative control. Reference and detection sequences, amplified by PCR and visualized on native PAGE (13%), were recovered only in presence of Her2-NanoComb (1) and not on the negative control (2), showing that NanoCombs bound to the cells through a specific interaction between the binder and the reference protein and not through a non-specific DNA interaction with the cell surface. b, NanoDeep was performed on SKBR3 cells, testing different conditions in parallel on four different cell plates with equal number of cells. Plate 1 was incubated with Her2-Nanocombs and then with binder library; Plate 2 was treated with NanoCombs that were not functionalized with anti-Her2 affibody-oligo conjugate on the reference prong and then with the binder library; Plate 3 was treated only with NanoCombs that were not functionalized with anti-Her2 affibody-oligo conjugate on the reference prong and Plate 4 was treated only with the binder library. After performing NanoDeep, reference and detection sequences were amplified by PCR and stained in solution to quantify the amount of DNA recovered. Optical Density (OD) measurements for each condition are plotted in the histogram; error bars represent SD. Source data

Extended Data Fig. 6 Characterization of Her3-NanoCombs and performance in NanoDeep.

a, 1:1 mixtures of ECD-Her2 and ECD-Her3 were covalently attached to the SPR surfaces of two sensor chips. Her2- and Her3-NanoCombs were injected and binding to the anchored target proteins was detected by single cycle kinetic mode. Fitting was performed using a 1:1 kinetic model to determine the dissociation constant, KD, association (kon) and dissociation (koff) rate constants. b, Micropatterned surfaces were used to verify the specificity of binding of Her2- and Her3-Nanocombs to the immobilized proteins. ECD-Her2, ECD-Her3 were anchored to different surfaces exploiting chemical amine coupling. Surfaces without immobilized protein were used as a negative control. We treated the ECD-Her2, ECD-Her3 and control surfaces with Her2- or Her3-Nanocombs modified with desthiobiotin at the 3’ end of the backbone. Peroxidase conjugated to streptavidin could bind to the desthiobiotin and catalysed a substrate conversion to obtain a luminescence signal proportional to the amount of NanoCombs. Luminescence values are presented in the histogram. c, SKBR3 cells were analysed by NanoDeep using Her3-NanoCombs and anti-Her2, -Her3 and -EGFR binder libraries. Measurements were performed in duplicate and presented as mean values in two types of heatmaps, showing the reads from the reference sequences (top) and detection sequences (bottom). d, SKBR3 cells were treated with Her2- and Her3-Nanocombs and binding was measured by the chemiluminescence assay; error bars represent SD. Source data

Extended Data Fig. 7 NanoDeep on SH-SY5Y cells.

a, Her2 RNA expression levels reported in “The Human Protein Atlas” (www.proteinatlas.org). We used a chemiluminescence assay to detect Her2-NanoCombs bound to SH-SY5Y cells, which show minimal levels of expression of Her2, SKBR3 cells (Her2 overexpression) and MCF7 cells (basal levels of Her2). Luminescence signals are presented in the histogram on the right; error bars represent SD. b, NanoDeep was performed on SH-SY5Y cells and SKBR3 cells, as positive control. Measurements were performed in duplicate and presented as mean values in two types of heatmaps, showing the reads from the reference sequences (top) and detection sequences (bottom). Source data

Extended Data Fig. 8 Anti-integrin α5β1 binder-oligo conjugate production and characterization.

a, Schematic representation of conjugation of anti-integrin α5β1 peptide and oligo sequence; click chemistry amine coupling reaction was used in order to covalently attach DNA oligo to the peptide, exploiting amine groups present on both molecules. This bioconjugation is a three step procedure in which both peptide and DNA oligo are first modified with specific chemical groups, then the two modified biomolecules react, resulting in the formation of a stable peptide-oligo bond. The total degree of peptide-oligo conjugation was visualised by UV spectrophotometry, since the bis-arylhydrazone group formed in the peptide-oligo anchoring points adsorbs at a specific wavelength (354 nm). b, The formation of the conjugate was assessed by loading DNA oligo and the peptide-oligo conjugate on native PAGE (16%). Staining with SybrGold revealed a slight band shift corresponding to the conjugate with respect to unmodified DNA oligo.

Extended Data Fig. 9 Anti-CD63 binder-oligo conjugate production and characterization.

a, Schematic representation of conjugation of anti-CD63 nanobody (VHH) and oligo sequence; SpyCatcher protein with N-terminal cysteine was first conjugated to a maleimide-oligo sequence. Then SpyCatcher–oligo complex was bound to SpyTag–nanobody specific for CD63. b, Two steps-based conjugation was visualised by Electrophoretic Mobility Shift assay (EMSA). Protein staining of PA gel shows the delayed migration of SpyCatcher after the conjugation with oligo sequence and a further band shift was observed due to the binding with SpyTag–nanobody. c, SPR real-time kinetic analysis of SpyTag-VHH (top) and the respective VHH-oligo conjugates (bottom) on sensor surfaces functionalized with CD63 proteins. Fitting was performed using a 1:1 kinetic model to the single cycle kinetic analysis to determine the dissociation constant, KD, association (kon) and dissociation (koff) rate constants.

Extended Data Fig. 10 Anti-CD71 binder-oligo conjugate modification and characterization.

a, Schematic representation of anti-CD71 aptamer modified with oligo sequence; b, The binding of the modified anti-CD71 aptamer to its target CD71 was verified by SPR real-time kinetic analysis. After immobilizing a biotinylated oligo that is complementary to the RNA sequence used to modify the aptamer on a streptavidin SPR surface, DNA–RNA hybridization was exploited to anchor the aptamer–oligo conjugate. Binding of CD71 to the immobilized aptamer was verified by single cycle kinetic mode measurement. Fitting was performed using a two-state reaction kinetic model to the single cycle kinetic analysis to determine the dissociation constant, KD, association (kon) and dissociation (koff) rate constants.

Supplementary information

Supplementary Information

Supplementary Fig.1 and Table 1.

Reporting Summary

Source data

Source Data Fig. 5

Heatmap numerical data.

Source Data Fig. 6

Heatmap numerical data.

Source Data Fig. 7

Heatmap numerical data.

Source Data Extended Data Fig. 5

Histogram numerical data.

Source Data Extended Data Fig. 6

Histogram and heatmap numerical data.

Source Data Extended Data Fig. 7

Histogram and heatmap numerical data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ambrosetti, E., Bernardinelli, G., Hoffecker, I. et al. A DNA-nanoassembly-based approach to map membrane protein nanoenvironments. Nat. Nanotechnol. 16, 85–95 (2021). https://doi.org/10.1038/s41565-020-00785-0

Download citation


Quick links

Find nanotechnology articles, nanomaterial data and patents all in one place. Visit Nano by Nature Research