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Tracking endocytosis and intracellular distribution of spherical nucleic acids with correlative single-cell imaging

Nature Protocols volume 16, pages 383–404 (2021)Cite this article

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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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Fig. 1: Overview of the procedure.
Fig. 2: Image of the dual-mode DFM–FM microscope in our lab.
Fig. 3: In situ correlative DFM–FM imaging with a dual-mode microscope (Step 10, Option A).
Fig. 4: In situ correlative DFM–FM imaging with a dark-field microscope and a confocal microscope (Step 10, Option B).
Fig. 5: Correlative DFM–SEM imaging of SNAs in solution (Step 10, Option C).
Fig. 6: Schematic illustration of SNA classification process with the colorimetry-based algorithm.
Fig. 7: Analysis of intracellular SNA clustering states (Step 11).
Fig. 8: Examples of anticipated results of correlative imaging of SNAs in living cells.

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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.

Author information

Authors and Affiliations

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

    Mengmeng Liu, Lihua Wang & Yang Tian

  2. Joint Research Center for Precision Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South Campus, Southern Medical University Affiliated Fengxian Hospital, Shanghai, China

    Fei Wang & Xueli Zhang

  3. School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Fei Wang, Chunhai Fan & Qian Li

  4. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xiuhai Mao & Chunhai Fan

  5. Bioimaging Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

    Lihua Wang

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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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Correspondence to Qian Li.

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The authors declare no competing interests.

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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.

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

Related links

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.

Reporting Summary

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

Statistical Source Data of Supplementary Fig 1.

Supplementary Data 2

Statistical source data of Supplementary Fig 5.

Supplementary Data 3

Statistical source data of Supplementary Fig 7.

Source data

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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