Robust nanoscopy of a synaptic protein in living mice by organic-fluorophore labeling

2018 
Extending superresolution fluorescence microscopy to living animals has remained a challenging frontier ever since the first demonstration of STED (stimulated emission depletion) nanoscopy in the mouse visual cortex. The use of fluorescent proteins (FPs) in in vivo STED analyses has been limiting available fluorescence photon budgets and attainable image contrasts, in particular for far-red FPs. This has so far precluded the definition of subtle details in protein arrangements at sufficient signal-to-noise ratio. Furthermore, imaging with longer wavelengths holds promise for reducing photostress. Here, we demonstrate that a strategy based on enzymatic self-labeling of the HaloTag fusion protein by high-performance synthetic fluorophore labels provides a robust avenue to superior in vivo analysis with STED nanoscopy in the far-red spectral range. We illustrate our approach by mapping the nanoscale distributions of the abundant scaffolding protein PSD95 at the postsynaptic membrane of excitatory synapses in living mice. With silicon-rhodamine as the reporter fluorophore, we present imaging with high contrast and low background down to ∼70-nm lateral resolution in the visual cortex at ≤25-µm depth. This approach allowed us to identify and characterize the diversity of PSD95 scaffolds in vivo. Besides small round/ovoid shapes, a substantial fraction of scaffolds exhibited a much more complex spatial organization. This highly inhomogeneous, spatially extended PSD95 distribution within the disk-like postsynaptic density, featuring intricate perforations, has not been highlighted in cell- or tissue-culture experiments. Importantly, covisualization of the corresponding spine morphologies enabled us to contextualize the diverse PSD95 patterns within synapses of different orientations and sizes.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    61
    References
    48
    Citations
    NaN
    KQI
    []