A novel method (RIM-Deep) for enhancing imaging depth and resolution stability of deep cleared tissue in inverted confocal microscopy

  1. Yisi Liu
  2. Pu Wang
  3. Junjie Zou
  4. Hongwei Zhou  Is a corresponding author
  1. Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, China
  2. Nikon Precision Corporation, Guangzhou , China, China
  3. Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, The National Key Clinical Specialty, China
  4. State Key Laboratory of Organ Failure Research, Southern Medical University, China
3 figures and 1 additional file

Figures

Figure 1 with 10 supplements
Resolution characterization and image depth of standard adapter and Refractive Index Matching-Deep (RIM-Deep).

(A and B) Schematic diagram of a 10 X immersion objective with standard adapter (A) or RIM-Deep (B). (C–D) MIP of 3-μm-diameter beads imaged in the xy and yz planes using standard adapter (C) or RIM-Deep (D) at different axial positions. (E) Axial resolutions for a 10 X immersion objective paired with standard adapter or RIM-Deep at different axial positions. The resolution is estimated by FWHMs of intensity profiles with a Gaussian fit for 3-μm-diameter beads embedded in 1% agarose dissolved in CUBIC mounting solution. Data are presented as mean ± s.e.m. (n=10). (F) The experimental scheme for the brain clearing process in Macaca fascicularis. (G, H, I) Three-dimensional reconstructions of the Macaca fascicularis brain vasculature using three different tissue clearing methods (iDISCO, CUBIC or MACS) with standard adapter. (G0, H0, I0) MIP of G, H, or I in xz plane. (G1–G2, H1–H2, I1–I2) Optical section of (G, H, I) at varying depths. (J, K, L) Three-dimensional reconstructions of the Macaca fascicularis brain vasculature using iDISCO, CUBIC, or MACS with the RIM-Deep. (J0, K0, L0) MIP of J, K, or L in xz plane. (J1–J4, K1–K4, L1–L4) Optical section of (J, K, L) at varying depths.

Figure 1—source data 1

Source data for FWHM axial and lateral resolution comparison between RIM-Deep and Standard adapter across different focal depths.

https://cdn.elifesciences.org/articles/101143/elife-101143-fig1-data1-v1.csv
Figure 1—figure supplement 1
Illustration and actual picture of standard adapter (A) and the Refractive Index Matching-Deep (RIM-Deep) (B) mounted on the Nikon AXR microscope.
Figure 1—figure supplement 2
Design and configuration of Refractive Index Matching-Deep (RIM-Deep) for inverted confocal microscope.

(A) A Three-view diagram of a media reservoir. (B) ①-③, Three-view diagram of the support bracket. ④, Three-dimensional diagram of the specimen holder. (C–F) RIM-Deep assembly procedure.

Figure 1—figure supplement 3
High-depth imaging of Macaca fascicularis brain vasculature using Leica STELLARIS 5 with the Refractive Index Matching-Deep (RIM-Deep).

(A) Setup of Leica STELLARIS 5 microscope with the RIM-Deep assembly. (B) 3D imaging of cleared Macaca fascicularis brain vasculature. (C) Optical sections of (B) at varying depths.

Figure 1—video 1
Three-dimensional reconstruction of the Macaca fascicularis brain vasculature using iDISCO with standard adapter.
Figure 1—video 2
Three-dimensional reconstruction of the brain vasculature in Macaca fascicularis was achieved using CUBIC with standard adapter.
Figure 1—video 3
Three-dimensional reconstruction of the brain vasculature in Macaca fascicularis using the MACS method with standard adapter.
Figure 1—video 4
3D reconstruction of the brain vasculature in Macaca fascicularis utilizing iDISCO with the Refractive Index Matching-Deep (RIM-Deep).
Figure 1—video 5
Three-dimensional reconstruction of the Macaca fascicularis brain vasculature employing CUBIC with the Refractive Index Matching-Deep (RIM-Deep).
Figure 1—video 6
Three-dimensional mapping of the Macaca fascicularis brain vasculature using MACS in combination with the Refractive Index Matching-Deep (RIM-Deep).
Figure 1—video 7
High-depth imaging of Macaca fascicularis brain vasculature using Leica STELLARIS 5 with Refractive Index Matching-Deep (RIM-Deep).
Figure 2 with 1 supplement
Deep imaging of a Thy 1-EGFP mouse brain using Refractive Index Matching-Deep (RIM-Deep).

(A) Experimental scheme. (B) 3D reconstruction of the ~5 mm deep in the mouse brain (left), MIP views in XZ (middle) and YZ (right). (C and G) 3D reconstructions of neuronal structures within the hippocampus (C, yellow box) and thalamus (G, orange box), respectively, as indicated in B. (D and H) Lateral slices through the indicated lateral planes in (C and G). Zoom-in views of the selected areas in top right. (E–F) MIP view of hippocampus(C) in xy and xz. (I–J) MIP view of thalamus (G) in xy and yz.

Figure 2—video 1
Deep imaging of a single field in cleared Thy1-EGFP mouse brain tissue.
Figure 3 with 4 supplements
3D imaging and reconstruction of neural and vascular structures in intact brain tissues using Refractive Index Matching-Deep (RIM-Deep).

(A) 3×3 stitching pattern of deep imaging of a cleared brain. (B) MIP of yz side view of A. (C) Optical section of top layer in (B). Zoom-in views of the selected areas in top right. (D) 3D reconstruction of a half of cleared brain in a Thy1-eGFP mouse brain. The white box represents MIP. (E–I) Stitched single layer images in the Z-direction. (J) 3D imaging of the entire brain vasculature in ischemic stroke mice. The images along the z stack are colored by spectrum. (K) MIP of vascular imaging in the ischemic region (red box in J). (L) MIP of vascular imaging in the contralateral region (blue box in J).

Figure 3—video 1
Deep imaging in 3×3 tiling mode of cleared Thy1-EGFP mouse brain tissue.
Figure 3—video 2
Vascular network of the whole brain in MCAO mouse.
Figure 3—video 3
Vascular network of the ischemic side in MCAO mouse brain.
Figure 3—video 4
Vascular network of the control side in MCAO mouse brain.

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  1. Yisi Liu
  2. Pu Wang
  3. Junjie Zou
  4. Hongwei Zhou
(2025)
A novel method (RIM-Deep) for enhancing imaging depth and resolution stability of deep cleared tissue in inverted confocal microscopy
eLife 13:RP101143.
https://doi.org/10.7554/eLife.101143.3