Abstract

Our understanding of endocytic pathway dynamics is restricted by the diffraction limit of light microscopy. Although super-resolution techniques can overcome this issue, highly crowded cellular environments, such as nerve terminals, can also dramatically limit the tracking of multiple endocytic vesicles such as synaptic vesicles (SVs), which in turn restricts the analytical dissection of their discrete diffusional and transport states. We recently introduced a pulse–chase technique for subdiffractional tracking of internalized molecules (sdTIM) that allows the visualization of fluorescently tagged molecules trapped in individual signaling endosomes and SVs in presynapses or axons with 30- to 50-nm localization precision. We originally developed this approach for tracking single molecules of botulinum neurotoxin type A, which undergoes activity-dependent internalization and retrograde transport in autophagosomes. This method was then adapted to localize the signaling endosomes containing cholera toxin subunit-B that undergo retrograde transport in axons and to track SVs in the crowded environment of hippocampal presynapses. We describe (i) the construction of a custom-made microfluidic device that enables control over neuronal orientation; (ii) the 3D printing of a perfusion system for sdTIM experiments performed on glass-bottom dishes; (iii) the dissection, culturing and transfection of hippocampal neurons in microfluidic devices; and (iv) guidance on how to perform the pulse–chase experiments and data analysis. In addition, we describe the use of single-molecule-tracking analytical tools to reveal the average and the heterogeneous single-molecule mobility behaviors. We also discuss alternative reagents and equipment that can, in principle, be used for sdTIM experiments and describe how to adapt sdTIM to image nanocluster formation and/or tubulation in early endosomes during sorting events. The procedures described in this protocol take 1 week.

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Acknowledgements

The super-resolution imaging was carried out at the Queensland Brain Institute's (QBI's) Advanced Microimaging and Analysis Facility with the help of A. Chien. We thank R.G. Parton (Institute for Molecular Bioscience, The University of Queensland, Australia) for helpful comments, N. Valmas (QBI) for the photography and the schematic illustration in Figure 1, J. Carroll and his team (QBI) for research computing infrastructure support, data management and processing, and R. Tweedale (QBI) for critical appraisal of the manuscript. We also thank J.B. Sibarita (Centre National de la Recherche Scientifique, Interdisciplinary Institute for Neuroscience, University of Bordeaux. France) for helpful comments and help with the PalmTracer software. We thank T. Wang and A. Papadopoulos for performing the original studies. This work was supported by an Australian Research Council Discovery Project grant (DP150100539) an Australian Research Council Linkage Infrastructure, Equipment, and Facilities grant (LE130100078) and a National Health and Medical Research Council (NHMRC) grant (APP1120381) to F.A.M. M.J. is supported by an Academy of Finland Postdoctoral Research Fellowship (298124), G.B. by the Finnish Cancer Foundation, P.P. and N.D. by University of Queensland Postdoctoral Research Fellowships and N.D. by a UQ Early Career Researcher Grant (UQECR1718789). R.M.-M. is funded by the Clem Jones Centre for Ageing Dementia Research. F.A.M. is a Senior Research Fellow of the National Health and Medical Research Council (1060075).

Author information

Author notes

    • Merja Joensuu
    •  & Ramon Martínez-Mármol

    These authors contributed equally to this work.

Affiliations

  1. Clem Jones Centre for Ageing Dementia Research, The University of Queensland, Brisbane, Queensland, Australia.

    • Merja Joensuu
    • , Ramon Martínez-Mármol
    • , Matthew Pelekanos
    • , Mahdie Mollazade
    •  & Frédéric A Meunier
  2. Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.

    • Merja Joensuu
    • , Ramon Martínez-Mármol
    • , Pranesh Padmanabhan
    • , Nela Durisic
    • , Matthew Pelekanos
    • , Mahdie Mollazade
    • , Rumelo Amor
    • , Geoffrey J Goodhill
    •  & Frédéric A Meunier
  3. Minerva Foundation Institute for Medical Research, Helsinki, Finland.

    • Merja Joensuu
  4. Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.

    • Nick R Glass
    •  & Justin J Cooper-White
  5. Division of General Microbiology, Department of Biosciences, University of Helsinki, Helsinki, Finland.

    • Giuseppe Balistreri
    •  & Justin J Cooper-White
  6. School of Chemical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

    • Justin J Cooper-White
  7. Materials Science and Engineering Division, CSIRO, Clayton, Victoria, Australia.

    • Justin J Cooper-White
  8. UQ Centre for Stem Cell Ageing and Regenerative Engineering, The University of Queensland, Brisbane, Queensland, Australia.

    • Justin J Cooper-White
  9. School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia.

    • Geoffrey J Goodhill

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Contributions

M.J. and R.M.-M. performed the experiments and analyzed the data. M.J. prepared the figures and wrote the paper with R.M.-M., P.P., N.D., R.A., G.B. and F.A.M. N.R.G. prepared the microfluidic devices and wrote the description of their preparation, which was supervised by J.J.C.-W. The perfusion lid was designed by M.P. and M.J. and was constructed by M.P. M.J. performed the nanobody fluorescence intensity analysis with N.D. P.P. performed the Bayesian model selection to HMM analysis, which was supervised by G.J.G. N.D. and P.P. estimated the localization precision. N.D. performed the single-molecule tracking with TrackMate. M.J., M.M. and G.B. prepared the schematic in Figure 2, and Supplementary Table 1. F.A.M. designed and supervised the studies. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Frédéric A Meunier.

Integrated supplementary information

Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5 and Supplementary Table 1.

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    Supplementary Data 1–3

    Supplementary Data 1. Description of steps for single-molecule tracking with TrackMate and MATLAB routine. Supplementary Data 2. 3D-print design. Supplementary Data 3. mask.gds file.

Videos

  1. 1.

    Retrograde transport of CTB.

    Hippocampal neurons cultured in microfluidic devices are stimulated for 5 min in high K+ in the presence of Alexa-647-CTB (50 ng μ−1) applied in the nerve terminal chamber. Following a 2-h chase, single Alexa-647-CTB molecules undergoing retrograde transport (left to right) can be detected in the axons that pass through the microfluidic channels. Note that a few molecules also undergo anterograde transport (right to left). Time-lapse imaging was carried out at 50 Hz and 20 ms exposure at +37 °C on a microscope equipped with an iLas2 double-laser illuminator, a CFI Apo TIRF 100× 1.49 NA objective and an Evolve512 delta EMCCD camera. Image acquisition was performed using Metamorph software. For presentation purposes, 2,500-frame acquisition is presented. Playback = 50 frames s−1. Scale bar, 5 μm. Imaging was carried out in accordance with relevant institutional and governmental ethical guidelines and regulations (Animal Ethics Approval QBI/254/16/NHMRC).

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https://doi.org/10.1038/nprot.2017.116

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