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. 2023 Sep 13;10(9):1081.
doi: 10.3390/bioengineering10091081.

Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy

Affiliations

Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy

Tristan C Paul et al. Bioengineering (Basel). .

Abstract

Super-resolution structured illumination microscopy (SR-SIM) is an optical fluorescence microscopy method which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing raw data and coarser illumination patterns, we imaged through a 150-micrometer-thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7-fold beyond conventional widefield imaging.

Keywords: Bayesian methods; brain; fluorescence microscopy; structured illumination; super-resolution.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Simplified optical diagram (left) and connection diagram (right). The connection setup for the two-wavelength acquisition is shown, and in this study, only 470 nm illumination was used.
Figure 2
Figure 2
(a) Overview of the OS-SIM image. The yellow box indicates the temporal association area where the neurons were imaged with super-resolution MAP-SIM. (b) Nissl (left) and anatomical annotations (right) from the Allen mouse brain atlas and the Allen Reference Atlas—Mouse Brain, at the same slice position as (a) (slice 92 of 132, Allen Mouse Brain Atlas, mouse.brain-map.org and atlas.brain-map.org).
Figure 3
Figure 3
(a) TeA neuron imaged at a depth of 41 μm to 66 μm using a 100×/1.4 NA oil immersion objective. (b,c) Zoomed in views of the selected areas indicated in (a) by yellow boxes. The width of the spine neck, selected in (d), was fit to a Gaussian function (FWHM 164.0 ± 4.9 nm).
Figure 4
Figure 4
(a) TeA neuron shown in widefield, basic OS-SIM, and MAP-SIM. (b) MAP-SIM image color-coded by depth.
Figure 5
Figure 5
(a) TeA neuron imaged at a depth of 71 μm to 83 μm using a 100×/1.4 NA oil immersion objective. The inset shows the fast Fourier transform (FFT) of the image in (a), the boundary of which indicates the resolution. (b) Zoomed-in view of the selected area indicated in (a) by a yellow box. (c) A measurement of the resolution determined by measuring the power spectral density. (d) The MAP-SIM image color-coded by depth.
Figure 6
Figure 6
Neurons of the subiculum, ventral part, pyramidal layer (SUBv-sp), with an imaging depth of 0 to 113 μm (60×/1.42 NA oil immersion objective). The maximum-intensity projections of the imaged area have depths of (a) 0.2–28.4 μm, (b) 28.6–56.6 μm, (c) 56.8–84.8 μm, and (d) 85.0–113.0 μm. (e) X-Z projection of the imaged area.
Figure 7
Figure 7
(a) Plot of modulation vs. axial depth for the different SIM patterns. (b) High-frequency pattern imaging of the TeA cortical neurons at the surface of the slice (0–10 μm). (c) High-frequency pattern imaging of the TeA cortical neurons at a depth of 41–45 μm. (d) Low-frequency pattern imaging of the same field of view shown in (c).

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