Abstract
A comprehensive understanding of interactions between nanoparticles (NPs) and biological components is critical to the clinical application of NPs and nanomedicine. Here we provide a step-by-step correlative imaging approach to investigate plasmonic NPs of different aggregation states at the single-cell level. Traceable spherical nucleic acids (SNAs) are fabricated by decorating 50-nm spherical gold NPs with fluorophore-labeled DNA, serving as dually emissive (fluorescent and plasmonic) NPs. The in situ correlative imaging with dark-field microscopy (DFM) and fluorescence microscopy (FM) reveals intracellular distribution of SNAs, whereas DFM combined with scanning electron microscopy (SEM) allows semi-quantification of SNA clustering states in solution. The imaging data are analyzed by ImageJ and a colorimetry-based algorithm written in Python. The clustering states of SNAs in a single cell can be efficiently distinguished within 20 s. This method can be readily installed to monitor real-time endocytosis and cellular distribution of plasmonic NPs of different aggregation states and to quantitatively image targets of interest (e.g., specific DNA, messenger RNA, peptides or proteins) in living cells. The entire procedure can be completed in 3–5 d and requires standard DFM, FM and SEM imaging and data analysis skills and equipment.
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Data availability
The data that support the findings of this study are provided in the article and its Supplementary Information files. Additional data are available from the corresponding author upon reasonable request. The primary data for Figs. 5c and 8b are provided as Source Data files with this protocol, and the primary data for Supplementary Figs. 1, 5 and 7b are provided as Supplementary Data 1, 2 and 3, respectively.
Code availability
The Python scripts for image analysis and aggregation classification of SNAs are available as Supplementary Software 1 of this protocol.
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Acknowledgements
We thank the National Natural Science Foundation of China (91953106, 21904041 and 21804088) and the China Postdoctoral Science Foundation (2019M661417) for support.
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Contributions
Q.L. conceived the study. M.L., F.W., C.F. and Q.L. designed experiments. M.L. and F.W. performed experiments. X.Z. assisted with cellular culture and FM imaging. F.W. and X.M. assisted with writing the Python scripts. M.L., F.W., L.W., Y.T., C.F. and Q.L. analyzed data. M.L. and Q.L. wrote the paper.
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Peer review information Nature Protocols thanks Ramsey Majzoub, Bo Tang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol:
Liu, M. et al. Nat. Commun. 8, 15646 (2017): https://doi.org/10.1038/ncomms15646
Liu, M. et al. Anal. Chem. 92, 1333–1339 (2020): https://pubs.acs.org/doi/10.1021/acs.analchem.9b04500
Xie, X. et al. Nano Lett. 20, 5228–5235 (2020): https://doi.org/10.1021/acs.nanolett.0c01503
Supplementary information
Supplementary Information
Supplementary Figs. 1–7, Supplementary Tables 1 and 2, Supplementary Method 1 and Supplementary Note 1.
Supplementary Software 1
It contains five Python scripts, a .doc file giving a detailed protocol on how to run the five Python scripts and a file folder containing test data.
Supplementary Data 1
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Supplementary Data 2
Statistical source data of Supplementary Fig 5.
Supplementary Data 3
Statistical source data of Supplementary Fig 7.
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Source Data Fig. 5
Statistical source data of Fig. 5c.
Source Data Fig. 8
Statistical source data of Fig. 8b.
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Liu, M., Wang, F., Zhang, X. et al. Tracking endocytosis and intracellular distribution of spherical nucleic acids with correlative single-cell imaging. Nat Protoc 16, 383–404 (2021). https://doi.org/10.1038/s41596-020-00420-1
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DOI: https://doi.org/10.1038/s41596-020-00420-1
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