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Designing chemigenetic DNA nanotrap for norepinephrine dynamic imaging in organelles

Nature Chemical Biology (2026)Cite this article

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Abstract

The lack of organelle-targeted neurotransmitter probes limits understanding of their intracellular roles. Here we created an organelle-targeted neurotransmitter nanoprobe using specific molecule-trapped DNA nanostructures for multiple recognition effects. In particular, we designed phenylboronic acid derivatives for chemical reaction and hydroxymethyl groups for forming hydrogen bonding with norepinephrine (NE), which were confined into tetrahedral DNA nanostructures with the optimized spatial effects, achieving the specific and rapid NE identification. Moreover, cyanine 3 providing built-in correction was designed for accurate NE quantification and the HaloTag ligand was synthesized for HaloTag protein targeting onto the organelle membrane, which were bonded to tips of the DNA nanostructure. The developed nanotrap demonstrated high selectivity, fast response (~50 ms), good stability and biocompatibility for organelle NE imaging. Using this tool, we discovered that traumatic brain injury triggers NE bursts in the endoplasmic reticulum, inducing endoplasmic reticulum (ER) stress, altering ER–mitochondrial protein regulation, promoting mitophagy and mitochondrial dysfunction and ultimately causing neuronal death.

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Fig. 1: Design and characterization of DNA nanotraps.
Fig. 2: Selectivity and response kinetics studies of developed DNA nanotraps toward NE.
Fig. 3: Characterization and performance of chemigenetic DNA nanotrap for NE sensing.
Fig. 4: Mapping and quantification of NE in various organelles of neurons.
Fig. 5: Real-time imaging and simultaneous quantification of NEER in response to TBI-induced ER stress in neurons.
Fig. 6: Organelle proteome and metabolomic analysis of neuronal death caused by ER stress and mitophagy through NE changes in ER.

Data availability

All data supporting the findings of this study are available in this paper and its Supplementary Information or from corresponding authors upon request. Source data are provided with this paper.

References

  1. Redhai, S. & Boutros, M. The role of organelles in intestinal function, physiology, and disease. Trends Cell Biol. 31, 485–499 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Ferri, K. F. & Kroemer, G. Organelle-specific initiation of cell death pathways. Nat. Cell Biol. 3, 255–263 (2001).

    Article  Google Scholar 

  3. Leung, K., Chakraborty, K., Saminathan, A. & Krishnan, Y. A DNA nanomachine chemically resolves lysosomes in live cells. Nat. Nanotechnol. 14, 176–183 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Jan, C. H., Williams, C. C. & Weissman, J. S. Principles of ER cotranslational translocation revealed by proximity-specific ribosome profiling. Science 346, 1257521 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rowland, A. A. & Voeltz, G. K. Endoplasmic reticulum–mitochondria contacts: function of the junction. Nat. Rev. Mol. Cell Bio. 13, 607–615 (2012).

    Article  CAS  Google Scholar 

  6. Zhang, Z. et al. CGI1746 targets σ1R to modulate ferroptosis through mitochondria-associated membranes. Nat. Chem. Biol. 20, 699–709 (2024).

    Article  CAS  PubMed  Google Scholar 

  7. Zhang, K. & Kaufman, R. J. From endoplasmic-reticulum stress to the inflammatory response. Nature 454, 455–462 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang, Y. et al. Exosomes derived from microglia overexpressing miR-124-3p alleviate neuronal endoplasmic reticulum stress damage after repetitive mild traumatic brain injury. Neural. Regen. Res. 19, 2010–2018 (2024).

    Article  PubMed  Google Scholar 

  9. Hussain, R. et al. Potentiating glymphatic drainage minimizes post-traumatic cerebral oedema. Nature 623, 992–1000 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hascup, E. R. et al. Histological studies of the effects of chronic implantation of ceramic-based microelectrode arrays and microdialysis probes in rat prefrontal cortex. Brain Res. 1291, 12–20 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Watson, C. J., Venton, B. J. & Kennedy, R. T. In vivo measurements of neurotransmitters by microdialysis sampling. Anal. Chem. 78, 1391–1399 (2006).

    Article  PubMed  Google Scholar 

  12. Bang, D. et al. Sub-second dopamine and serotonin signaling in human striatum during perceptual decision-making. Neuron 108, 999–1010 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wightman, R. M. Probing cellular chemistry in biological systems with microelectrodes. Science 311, 1570–1574 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Zhou, N., Huo, F., Yue, Y. & Yin, C. Specific fluorescent probe based on ‘protect–deprotect’ to visualize the norepinephrine signaling pathway and drug intervention tracers. J. Am. Chem. Soc. 142, 17751–17755 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Yan, H., Wang, Y., Huo, F. & Yin, C. Fast-specific fluorescent probes to visualize norepinephrine signaling pathways and its flux in the epileptic mice brain. J. Am. Chem. Soc. 145, 3229–3237 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Yue, Y., Huo, F. & Yin, C. Noradrenaline-specific, efficient visualization in brain tissue triggered by unique cascade nucleophilic substitution. Anal. Chem. 91, 2255–2259 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Feng, J. et al. A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102, 745–761 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sun, F. et al. A genetically encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice. Cell 174, 481–496 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Peng, W. et al. Regulation of sleep homeostasis mediator adenosine by basal forebrain glutamatergic neurons. Science 369, eabb0556 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Hu, Z. et al. Aptamer based biosensor platforms for neurotransmitters analysis. Trends Anal. Chem. 162, 117021 (2023).

    Article  CAS  Google Scholar 

  21. Wu, Y. et al. Genetically encoded fluorogenic DNA aptamers for imaging metabolite in living cells. J. Am. Chem. Soc. 147, 1529–1541 (2025).

    Article  CAS  PubMed  Google Scholar 

  22. Zou, L., Liu, Y. & Liu, J. Capture-SELEX of DNA aptamers for label-free detection of epinephrine and norepinephrine in urine. Biosens. Bioelectron. 279, 117392 (2025).

    Article  CAS  PubMed  Google Scholar 

  23. Mao, L., Han, Y., Zhang, Q. W. & Tian, Y. Two-photon fluorescence imaging and specifically biosensing of norepinephrine on a 100-ms timescale. Nat. Commun. 14, 1419 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhao, Y., Mei, Y., Sun, J. & Tian, Y. A supramolecular fluorescent chemosensor enabling specific and rapid quantification of norepinephrine dynamics. J. Am. Chem. Soc. 147, 5025–5034 (2025).

    Article  CAS  PubMed  Google Scholar 

  25. Ge, Z., Gu, H., Li, Q. & Fan, C. Concept and development of framework nucleic acids. J. Am. Chem. Soc. 140, 17808–17819 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Li, D., Song, S. & Fan, C. Target-responsive structural switching for nucleic acid-based sensors. Acc. Chem. Res. 43, 631–641 (2010).

    Article  PubMed  Google Scholar 

  27. Li, J., Green, A. A., Yan, H. & Fan, C. Engineering nucleic acid structures for programmable molecular circuitry and intracellular biocomputation. Nat. Chem. 9, 1056–1067 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu, J. et al. Mechano-fluorescence actuation in single synaptic vesicles with a DNA framework nanomachine. Sci. Rob. 7, eabq5151 (2022).

    Article  Google Scholar 

  29. Liu, X. et al. Complex silica composite nanomaterials templated with DNA origami. Nature 559, 593–598 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Yao, G. et al. Programming nanoparticle valence bonds with single-stranded DNA encoders. Nat. Mater. 19, 781–788 (2019).

    Article  PubMed  Google Scholar 

  31. Lv, H. et al. DNA-based programmable gate arrays for general-purpose DNA computing. Nature 622, 292–300 (2023).

    Article  CAS  PubMed  Google Scholar 

  32. Deng, J. H. et al. π–π stacking interactions: non-negligible forces for stabilizing porous supramolecular frameworks. Sci. Adv. 6, eaax9976 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li, D. et al. Time-resolved encryption via a kinetics-tunable supramolecular photochromic system. Adv. Sci. 9, e2104790 (2022).

    Article  Google Scholar 

  34. Zhang, H., Fang, Y., Yang, F., Liu, X. & Lu, X. Aromatic organic molecular crystal with enhanced π–π stacking interaction for ultrafast Zn-ion storage. Energy Environ. Sci. 13, 2515–2523 (2020).

    Article  CAS  Google Scholar 

  35. Tolstoy, P. M. & Tupikina, E. Y. IR and NMR spectral diagnostics of hydrogen bond energy and geometry. in Spectroscopy and Computation of Hydrogen-Bonded Systems (eds Wójcik, M. J. & Ozaki, Y.) Ch. 12 (Wiley, 2023).

  36. Rodríguez Ortega, P. G., Montejo, M., Valera, M. S. & López González, J. J. Studying the effect of temperature on the formation of hydrogen bond dimers: a FTIR and computational chemistry lab for undergraduate students. J. Chem. Educ. 96, 1760–1766 (2019).

    Article  Google Scholar 

  37. Pacak, K., Palkovits, M., Kopin, I. J. & Goldstein, D. S. Stress-induced norepinephrine release in the hypothalamic paraventricular nucleus and pituitary-adrenocortical and sympathoadrenal activity: in vivo microdialysis studies. Front. Neuroendocrinol. 16, 89–150 (1995).

    Article  CAS  PubMed  Google Scholar 

  38. Zhu, W. et al. Chemigenetic indicators based on synthetic chelators and green fluorescent protein. Nat. Chem. Biol. 19, 38–44 (2022).

    Article  PubMed  Google Scholar 

  39. Frei, M. S. et al. Engineered HaloTag variants for fluorescence lifetime multiplexing. Nat. Methods 19, 65–70 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wang, B., Zhao, Z., Xiong, M., Yan, R. & Xu, K. The endoplasmic reticulum adopts two distinct tubule forms. Proc. Natl Acad. Sci. USA 119, e2117559119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tebo, A. G. & Gautier, A. A split fluorescent reporter with rapid and reversible complementation. Nat. Commun. 10, 2822 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hambrock, A., Löffler-Walz, C. & Quast, U. Glibenclamide binding to sulphonylurea receptor subtypes: dependence on adenine nucleotides. Brit. J. Pharmacol. 136, 995–1004 (2002).

    Article  CAS  Google Scholar 

  43. Bayir, H. et al. Selective early cardiolipin peroxidation after traumatic brain injury: an oxidative lipidomics analysis. Ann. Neurol. 62, 154–169 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Schroeder, J. P. A. lisha, Epps, S., Grice, T. W. & Weinshenker, D. The selective dopamine β-hydroxylase inhibitor nepicastat attenuates multiple aspects of cocaine-seeking behavior. Neuropsychopharmacology 38, 1032–1038 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ellis, E. F., McKinney, J. S., Willoughby, K. A., Liang, S. & Povlishock, J. T. A new model for rapid stretch-induced injury of cells in culture: characterization of the model using astrocytes. J. Neurotraum. 12, 325–339 (1995).

    Article  CAS  Google Scholar 

  46. Yang, Y. et al. Endoplasmic reticulum stress and the unfolded protein response: emerging regulators in progression of traumatic brain injury. Cell Death Dis. 15, 156 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Munro, S. & Pelham, H. R. B. An hsp70-like protein in the ER: Identity with the 78 kd glucose-regulated protein and immunoglobulin heavy chain binding protein. Cell 46, 291–300 (1986).

    Article  CAS  PubMed  Google Scholar 

  48. Mandela, P., Chandley, M., Xu, Y.-Y., Zhu, M. Y. & Ordway, G. A. Reserpine-induced reduction in norepinephrine transporter function requires catecholamine storage vesicles. Neurochem. Int. 56, 760–767 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Herranen, A. et al. Deficiency of the ER-stress-regulator MANF triggers progressive outer hair cell death and hearing loss. Cell Death Dis. 11, 100 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tedeschi, P. M., Vazquez, A., Kerrigan, J. E. & Bertino, J. R. Mitochondrial methylenetetrahydrofolate dehydrogenase (MTHFD2) overexpression is associated with tumor cell proliferation and is a novel target for drug development. Mol. Cancer Res. 13, 1361–1366 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen, Y., Craigen, W. J. & Riley, D. J. Nek1 regulates cell death and mitochondrial membrane permeability through phosphorylation of VDAC1. Cell Cycle 8, 257–267 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, J. & Ney, P. A. Role of BNIP3 and NIX in cell death, autophagy, and mitophagy. Cell Death Differ. 16, 939–946 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Achreja, A. et al. Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer. Nat. Metab. 4, 1119–1137 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Kawai, J. et al. Discovery of a potent, selective, and orally available MTHFD2 Inhibitor (DS18561882) with in vivo antitumor activity. J. Med. Chem. 62, 10204–10220 (2019).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (22393930 and 22393933 to Y.T.; 22474043 to Z.C.L.), National Key Research and Development Program of China (2022YFF0710000 to Y.T.), ‘Shanghai Science and Technology Innovation Action Plan’ Fundamental Research Project (22JC1401200 to Y.T.; 24ZR1418600 to Z.C.L.), Shanghai Agricultural Science and Technology Innovation Program (T2024327 to Z.C.L.), Shanghai Municipal Science and Technology Commission (25511102400 to X. H.) and Fundamental Research Funds for the Central Universities. We also thank W. Wang from the Material Characterization Center of East China Normal University for his assistance in fluorescence imaging.

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China

    Yilin Chen, Zhichao Liu, Yating Wang, Yuxiao Mei, Qi-Wei Zhang, Jing Sun, Xiao He, Jingjing Wan & Yang Tian

  2. Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China

    Zhichao Liu & Yang Tian

  3. Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, Shanghai, China

    Jing Sun & Xiao He

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  1. Yilin Chen
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Contributions

Z.C.L. and Y.T. designed the experiments and wrote the manuscript. Y.L.C. performed the experiments. Y.T.W. helped with the proteomic measurements. Y.X.M. helped with organic ligand synthesis. Q.-W.Z. and J.S. helped with language polishing. X.H. helped with theoretical calculations. J.J.W. helped with proteomics analysis. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Zhichao Liu or Yang Tian.

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Nature Chemical Biology thanks Chunhai Fan, Caixia Yin and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Theoretical calculations and molecular docking.

a, The conformation of (I) NEtrap2.4, (II) NEtrap4.4-OH and (III) NEtrap4.4 constructed based on the corresponding deoxynucleotide sequence after molecular dynamics simulation. The gray color represented the backbone structure of the tetrahedron, green represented the structure of BAN, and red represented the nucleotides with hydroxyl groups. As can be seen, there was π-π stacking between BAN molecules and the nucleotides with hydroxyl groups (-CH2OH) were all positioned inside the tetrahedral structure. b, Overall schematic diagram of interaction between NEtrap2.4-OH with (I) E and (II) DA through hydrogen bonding. c, Overall schematic diagram of interaction between (I) NEtrap2.4, (II) NEtrap2D-OH and (III) NEtrap4.4-OH in the presence of NE.

Extended Data Fig. 2 Neuronal types analysis.

a, Fluorescence images of the neurons obtained from (I) DAPI signal (nuclear marker, blue color), (II) DBH signal (NE neuron marker, cyan color), (III) probe signal (red color) and (V) merged signals. b, Immunoblotting analysis of (I) DBH and (II) GAPDH in the used neurons obtained from LC region of mice. c, Co-localization imaging of neurons pre-transfected with (I) membrane-targeted, (II) endoplasmic reticulum-targeted, and (III) mitochondrial-targeted HaloTag proteins, respectively. The neurons were co-stained with NEtrapHTL and commercial organelle-targeted probes. Hoechst, CellLight™ PM-RFP, CellLight™ ER-RFP, and CellLight™ Mito-RFP were used to target nucleus, PM, ER, and Mito, respectively. Three independent experiments yielded similar results.

Extended Data Fig. 3 NE measurements with NEtrapHTL in neurons.

a, Confocal fluorescence images and NE levels in NEtrapHTL-labeled plasma membrane, ER, and Mito in neurons. (Scale bars: 5 μm). b, NE levels in various organelles including PM, ER and Mito. Data are presented as mean ± S.D. Error bars: S.D., n = 30 neurons.

Source data

Extended Data Fig. 4 Stability tests of NEtrapHTL in the absence and presence of Glyburide.

a, Confocal fluorescence images of neurons treated with NEtrapHTL and CellLight™ ER-RFP for different times (2, 6, 14, 32, 48 h). b, Confocal fluorescence images of neurons treated with NEtrapHTL and CellLight™ ER-RFP for different times (2, 6, 14, 32, 48 h) in the presence of Glyburide. c, The colocation correlation of neurons treated with NEtrapHTL and CellLight™ ER-RFP. Data are presented as mean ± S.D. Error bars: S.D., n = 3 independent experiments. d, PAGE (15%) analysis of DNA reporter incubated with neurons for different times (2, 14, 32, 52, 60, 72 h). Three independent experiments yielded similar results.

Source data

Extended Data Fig. 5 Synthesis and characterization of NEtrapTSDA.

a, Schematic diagram of NEtrapTSDA. b, PAGE analysis of the formation process of NEtrapTSDA. c, MALDI spectra of NEtrapTSDA. d, Confocal fluorescence images of neurons treated with the developed NEtrapTSDA nanoprobe and CellLight™ ER-RFP. e, Confocal fluorescence images of neurons treated with NEtrapTSDA and CellLight™ ER-RFP for different times (2, 6, 14, 32, 48 h). Three independent experiments yielded similar results.

Extended Data Fig. 6 Stability and degradation analysis of NEtrapHTL@LNP in vivo.

a, Schematic diagram of NEtrapHTL@LNP. b, TEM image of NEtrapHTL@LNP. c, Confocal image of NEtrapHTL@LNP collected from fluorescence signal between 520-650 nm. Three independent experiments yielded similar results. d, FT-IR spectra of (I) NEtrapHTL, (II) LNP, (III) Xen2174 and (IV) NEtrapHTL@LNP. e, Confocal fluorescence images of brain slices: (I) overview of brain slices; (II) DAPI signal (nuclear marker); (III) probe signal; (IV) DBH signal (NE neuron marker); (V) merged image. Three independent experiments yielded similar results. f, Fluorescence images of LC region in brain slices at different time points after NEtrapHTL@LNP was injected into the LC region of mice. Three independent experiments yielded similar results. g, Quantitative analysis of fluorescence imaging intensity in (f). Data are presented as mean ± S.D. Error bars: S.D., n = 3 independent experiments. h, Fluorescence images of various organs at different time points after NEtrapHTL@LNP was injected into the LC region of mice. Three independent experiments yielded similar results.

Source data

Extended Data Fig. 7 Stability and degradation analysis of NEtrap1080@LNP in vivo.

a, Schematic diagram of NEtrap1080. b, Agarose gel electrophoresis analysis of the assembly of NEtrap1080. I-IX represent A-CHO, B-BAN, C-BAN, D-FD1080, A(CHO)B(BAN), A(CHO)B(BAN)C(BAN), A(CHO)B(BAN)C(BAN)D(FD-1080) and NEtrap1080. c, MALDI mass spectrum of NEtrap1080. d, (I) UV-vis absorption spectrum and (II) fluorescence emission spectrum of NEtrap1080. e, Schematic diagram of NEtrap1080@LNP. f, TEM image of NEtrap1080@LNP. Three independent experiments yielded similar results. g, Confocal image of NEtrap1080@LNP collected from fluorescence signal between 400-500 nm. h, FT-IR spectra of (I) NEtrap1080, (II) LNP, (III) Xen2174 and (IV) NEtrap1080@LNP. i, In vivo whole-body fluorescence imaging at different time points after NEtrap1080@LNP was injected into the brain. Three independent experiments yielded similar results. j, Quantitative analysis of in vivo fluorescence imaging intensity in (i). Data are presented as mean ± S.D. Error bars: S.D., n = 3 independent experiments. k, Fluorescence images of various organs at different time points after NEtrap1080@LNP was injected into the LC region of mice brain. Three independent experiments yielded similar results.

Source data

Extended Data Fig. 8 Stability and degradation analysis of NEtrap1080@LNP-1 in vivo.

a, Schematic diagram of NEtrap1080@LNP-1. b, FT-IR spectra of (I) NEtrap1080@LNP, (II) AGP-2 and (III) NEtrap1080@LNP-1. c, TEM image of NEtrap1080@LNP-1. Three independent experiments yielded similar results. d, In vivo whole-body fluorescence imaging at different times after tail vein injection of the probe. Three independent experiments yielded similar results. e, Quantitative analysis of fluorescence imaging intensity in (d). Data are presented as mean ± S.D. Error bars: S.D., n = 3 independent experiments. f, Fluorescence images of various organs at different time points after NEtrap1080@LNP-1 was injected into the LC region of mice brain. Three independent experiments yielded similar results.

Source data

Supplementary information

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Chen, Y., Liu, Z., Wang, Y. et al. Designing chemigenetic DNA nanotrap for norepinephrine dynamic imaging in organelles. Nat Chem Biol (2026). https://doi.org/10.1038/s41589-026-02158-5

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