2.1.

Animals

Wild-type mice (C57BL/6J, 8 to 12 weeks old) were used in this study. The animals were housed in a specific-pathogen-free (SPF) animal house under a 12/12 h light/dark cycle with unrestricted access to food and water. All animal experiments were performed under the Experimental Animal Management Ordinance of Hubei Province, P. R. China, and the Huazhong University of Science and Technology guidelines and approved by the Institutional Animal Ethics Committee of Huazhong University of Science and Technology.

2.2.

TBI Mouse Models

The mice were anesthetized with 4% isoflurane in a ${\mathrm{N}}_2\mathrm{O}/{\mathrm{O}}_2$ (70%/30%) mixture and maintained with 1.5% isoflurane in the same mixture for the whole surgery. The scalps of the mice were cut to expose the skull surface. The body temperature was maintained at $37°\mathrm{C}\pm 0.5°\mathrm{C}$ with a heating pad. The injury was triggered via a controlled cortical impact device consisting of a fixed impactor using the following parameters: a tube diameter of 4 mm, impact speed of $3\mathrm{m}{\mathrm{s}}^{-1}$, and impact depth of 1 mm. The resulting injury was obvious with these parameters. The mice then went through the vascular labeling procedure as follows before having the chance to wake up.

2.3.

Sample Preparation

Vascular labeling using Gel-Dex-FITC. We used the protocol described by Tsai et al. with slight modifications.²⁶ A gelatin solution was prepared by dissolving porcine skin gelatin (no. G1890; Sigma-Aldrich) in hot 0.01 M phosphate-buffered saline (PBS, Sigma, P3813). FITC-conjugated dextran powder was dissolved in a 2% (w/v) gelatin solution at a concentration of 0.5% (w/v), and the mixed solution was kept at 40°C to 45°C before use. For vascular labeling, anesthetized mice were transcardially perfused first with a 0.01 M PBS solution and then 10 mL of fluorescent solutions. Perfused mice bodies were then transferred to a low-temperature environment (e.g., 4°C) for rapid cooling and solidification of the gelatin solution in vessels. After cooling, the desired mouse organs were extracted and fixed in 4% paraformaldehyde (PFA, Sigma-Aldrich, 158127) overnight at 4°C.

Vascular labeling with DiI. The protocol used for DiI labeling was described previously.²⁷ DiI powder (Aladdin, D131225) was dissolved in 100% ethanol at a concentration of $6\mathrm{mg}/\mathrm{mL}$ to prepare a stock solution, which can be stored in the dark at room temperature for up to 1 year. A DiI working solution was prepared by adding $200\mu \mathrm{L}$ of the DiI stock solution to 10 mL of diluent (0.01 M PBS and 5% (wt/vol) glucose at a ratio of 1:4), as reported previously.²⁷ A DiI working solution should be freshly made before use. Anesthetized mice were first perfused with 0.01 M PBS at a rate of 1 to $2\mathrm{mL}/\min$ (total 3 to 4 min) and then with 10 to 15 mL of the DiI working solution at a rate of 1 to $2\mathrm{mL}/\min$ (total 10 to 15 min); the color of the mice’s ears, noses, and palms should become slightly purple during the perfusion of the DiI solution. Finally, desired organs were harvested and postfixed in 4% PFA overnight.

Vascular labeling with lectin and CD31 antibodies. The protocol used for lectin and CD31 antibodies was described previously.^15,22 A DyLight 649 conjugated L. esculentum (Tomato) lectin (LEL-D649, DL-1178, Vector Laboratories) and an Alexa Fluor 647 conjugated anti-mouse CD31 antibody (CD31-A647, 102416, BioLegend) were also used to label the vasculature. Lectin was diluted in saline to a concentration of $0.5\mathrm{mg}/\mathrm{mL}$ and injected into the tail vein (0.1 mL per mouse). The Alexa Fluor 647 conjugated anti-mouse CD31 antibody (10 to 15 mg) was diluted in saline and injected into the tail vein (total volume of $200\mu \mathrm{L}$ per mouse). Anesthetized mice were then transcardially perfused with 0.01 M PBS. The desired organs (e.g., brains, livers, kidneys) were excised from the perfused animal bodies. All harvested samples were postfixed in 4% PFA overnight at 4°C.

The fixed tissues were sliced using a commercial vibratome (Leica VT 1200 s, Germany).

2.4.

Tissue Clearing Protocols

We chose different types of tissue clearing methods: hydrophobic clearing procedures, including FDISCO, uDISCO, and PEGASOS; hydrophilic clearing techniques, including CUBIC and MACS; and a hydrogel-based protocol, PACT. All evaluated clearing protocols were performed according to the instructions in the original publications. The clearing procedures for each method are briefly summarized as follows.

FDISCO.²² The FDISCO procedure consists of two steps: dehydration and RI matching. First, the fixed tissues are dehydrated using tetrahydrofuran (THF) (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) solutions at concentration gradients of 50%, 70%, 80%, and 100%. The pH of each THF solution is adjusted to 9.0 by adding triethylamine. The dehydrated tissues are then incubated in pure dibenzyl ether (DBE) (Aladdin, Shanghai, China) for RI matching. The time for each dehydration step (50% to 100% THF) is 4 h (tissue slices)/12 h (whole organs), and the time for RI matching with DBE is 4 h (tissue slices)/12 h (whole organs). All steps are conducted at 4°C, with gentle shaking.

uDISCO.²¹ Dehydration reagents are prepared by mixing pure tert-butanol (Sigma-Aldrich, St. Louis) with distilled water at upgraded concentrations of 30%, 50%, 70%, 80%, 90%, 96%, and 100% (v/v). The fixed tissues are sequentially dehydrated in each reagent. The dehydrated tissues are then immersed in dichloromethane and BABB-D4 until the samples become transparent. The time for each dehydration step (30% to 100% tert-butanol) is 4 h (tissue slices)/12 h (whole organs), and the time for RI matching with BABB-D4 is 4 h (tissue slices)/12 h (whole organs). The dehydration steps are performed at 35°C, and the RI matching steps are performed at room temperature, with gentle shaking.

PEGASOS.²³ The samples are first decolorized with a 25% Quadrol decolorization solution. The samples are then dehydrated in tert-Butanol solution gradients, tB-PEG (70% v/v tert-Butanol, 27% v/v PEG methacrylate Mn 500 (PEGMMA500) (Sigma-Aldrich, 409529), and 3% w/v Quadrol (Sigma-Aldrich, 122262)). Next, the samples are immersed in BB-PEG [75% v/v benzyl benzoate (BB) (SigmaAldrich B6630) and 25% v/v PEGMMA500 (Sigma-Aldrich, 409529) containing a 3% w/v Quadrol (Sigma-Aldrich, 122262) added] medium for clearing. The time for decolorization is 12 h, and the time for each dehydration step and final RI matching is 4 h. All steps are performed at 37°C, with gentle shaking.

CUBIC.²⁴ CUBIC-L is prepared by mixing 10% (wt/wt) N-butyldiethanolamine (Tokyo Chemical Industry, B0725) with 10% (wt/wt) Triton X-100 (Sigma-Aldrich, T8787) in ${\mathrm{dH}}_2\mathrm{O}$. CUBIC-R is prepared by mixing 45% (wt/wt) antipyrine (Tokyo Chemical Industry, D1876) with 30% (wt/wt) nicotinamide (Tokyo Chemical Industry, N0078) in ${\mathrm{dH}}_2\mathrm{O}$. The samples are incubated in CUBIC-L for delipidation for 1.5 d, washed with PBS for 6 h, and then exposed to CUBIC-R for RI matching for 0.5 d. All steps are performed at 37°C, with gentle shaking.

MACS.¹⁹ The fixed samples are serially incubated in MACS-R0, MACS-R1, and MACS-R2 solutions at room temperature while shaking gently. MACS-R0 is prepared by mixing 20% (vol/vol) MXDA (Tokyo chemical industry, D0127), 15% (wt/vol) sorbitol (Sigma, 85529), and ${\mathrm{dH}}_2\mathrm{O}$; MACS-R1 is prepared by mixing 40% (vol/vol) MXDA with 30% (wt/vol) sorbitol dissolved in 1× PBS. MACS-R2 is prepared by mixing 40% (vol/vol) MXDA with 50% (wt/vol) sorbitol in ${\mathrm{dH}}_2\mathrm{O}$. The incubation time for MACS-R0, MACS-R1, and MACS-R2 is 6, 2, and 1 h (tissue slices)/36, 12, and 12 h (whole organs), respectively. All steps are performed at 30°C, with gentle shaking.

PACT.²⁵ The PACT clearing method is comprised of three steps: hydrogel embedding, delipidation, and RI matching. First, the samples are incubated in A4P0 hydrogel solution (4% acrylamide in 0.01 M PBS), doused with 0.25% photoinitiator 2,20-Azobis [2-(2-imidazolin-2-yl) propane] dihydrochloride (Wako Chemicals, Osaka, Japan) at 4°C overnight, and incubated at 37°C for 6 h to achieve polymerization. Next, the embedded samples are washed with 0.01 M PBS and subjected to delipidation in 8% sodium dodecyl sulfate (SDS) (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) in 0.01 M PBS. The samples are again washed with PBS and immersed in a sorbitol-based refractive index matching solution (sRIMS) (75% (wt/vol) sorbitol solution) until the tissues become transparent. The time for delipidation with SDS is 96 h, and the time for RI matching is 24 h. All steps are performed at 37°C, with gentle shaking.

2.5.

Imaging

Fluorescent images of the cleared samples were captured using a light sheet microscope (LiToneXL, Light Innovation Technology, China) equipped with a $4\times$ objective lens [$\mathrm{NA}=0.28$, working distance $(\mathrm{WD})=20\mathrm{mm}$]. Thin light sheets were illuminated from all four sides of the sample, and a merged image was obtained. For light sheet imaging, the laser power was set to 10%, and the exposure time was 100 to 200 ms. An inverted laser-scanning confocal fluorescence microscope (LSM710, Zeiss, Germany) was used for the fluorescence imaging of tissue sections, employing the $5\times$ objective lens (FLUAR, $\mathrm{NA}=0.25$, $\mathrm{WD}=12.5\mathrm{mm}$) and $10\times$ objective lens (FLUAR, $\mathrm{NA}=0.5$, $\mathrm{WD}=2\mathrm{mm}$). Data were collected using the Zen 2011 SP2 (Version 8.0.0.273, Carl Zeiss GmbH, Germany) software. For confocal imaging, the laser power of 488 and 561 channels was set to 5%, and the laser power of 633 channels was set to 15% because the total power of 633 lasers is only a third of that of the 488/561 lasers. The gains were set to 500 to 600 to make sure that the image was not overexposure. The scan speed was set to 8. The image pre- and post-clearing for each labeling method was imaged under the same conditions.

2.6.

Image Data Processing

All raw image data were collected in a lossless TIFF format (8-bit images for confocal microscopy and 16-bit images for light-sheet microscopy). The 16-bit images were transformed into 8-bit images by ImageJ to enable fast processing by other software, such as Imaris. Processing and 3D rendering were executed on a Dell workstation with an 8-core Xeon processor, 256 GB RAM, and an Nvidia Quadro P2000 graphics card. 3D and 2D image visualizations were conducted using Imaris (Version 7.6, Bitplane AG) and ImageJ (Version 1.51n), respectively. Tile scans from light-sheet microscopy were stitched utilizing MATLAB (Version 2014a, Mathworks).

2.7.

Quantifications

To evaluate the efficiency of the different vessel labeling methods, as well as the fluorescence compatibility of each vessel labeling technique with different clearing procedures, signal-to-background ratios were calculated. First, the image stacks (50-μm-thickness) were imported in ImageJ. The “threshold” function in ImageJ was used to extract the vascular signals for each image in an image stack. Then, the “plot $Z$-axis profile” function in ImageJ was applied to calculate the mean signal intensities for each image. Next, three areas without blood vessels were manually selected as background regions for each image in the image stack. The mean values of these areas were determined as the background intensities of each image. The signal-to-background ratios for each image in the stack were defined as the mean signal intensities divided by the mean background intensities. The final signal-to-background ratio for an image stack was determined as the mean value of signal-to-background ratios for each image in the stack. Additionally, given the background heterogeneity in different brain regions, we calculated the signal-to-background ratio for three different brain regions and used the mean value as the final signal-to-background ratio for each labeling method to reduce the influence caused by the background heterogeneity.

3D vascular reconstruction and quantification by Imaris was performed using the pipeline described in previous studies. In brief, the “Threshold (loops)” algorithm in the filaments module was used to trace and quantify blood vessels. The parameter for the filament diameter was automatically set by the algorithm according to the voxel size. The voxel size of our data is $0.83\mu \mathrm{m}\times 0.83\mu \mathrm{m}\times 2\mu \mathrm{m}$. The threshold for extracting vascular information was manually adjust to ensure the complete recognition of all blood vessels. After reconstruction of the vascular network, the local vessel density, average radius, and vessel segment length were obtained using the “statistics-.selection” in the filaments module.

2.8.

Statistical Analysis

Data are presented as the mean ± SD and were analyzed using the SPSS software (Version 22, IBM, USA), with 95% confidence intervals. The sample sizes are indicated in the figure legends. For the analysis of statistical significance, the normality of the data distribution in each experiment was checked using the Shapiro–Wilk test. The variance homogeneity for each group was evaluated employing Levene’s test. $P$ values were calculated using an independent sample $t$-test (two-sided) to compare data between two groups, as shown in Figs. 4(a)–4(d) and Figs. 6(e)–6(f). $P$ values were determined using one-way ANOVA, followed by the Bonferroni post hoc test to compare data, as shown in Figs. 1(d) and 2(c). $P<0.05$ was considered significant (*, $P<0.05$; **, $P<0.01$; ***, $P<0.001$).

Fig. 1

Comparison of the labeling efficiency of four selected vessel labeling methods in the mouse brain. (a) Experimental workflow for vessel labeling, imaging, and analysis. (b) Fluorescence images of overall vessel information in sagittal mouse brain slices labeled with Gel-Dex-FITC, DiI, LEL-D649, and CD31-A647 antibodies, respectively. (c) Fluorescence images of vessel information in three typical brain regions at high magnification, including the cortex, hippocampus, and cerebellum. (d) Signal-to-noise ratios of labeled vasculatures using different methods ($n=6$). All values are presented as the mean±SD. Statistical significance in (d) (***, $P<0.001$) was assessed with one-way ANOVA, followed by the Bonferroni post hoc test.

Fig. 2

Analysis of vascular networks in the brain labeled using four vessel-labeling methods. (a) Representative images of the reconstruction and analysis of brain vessels. (b) Statistical analyses of the distributions of vessel radii and lengths of individual vessel segments, respectively ($n=6$). (c) Quantification of local vessel densities of specific brain regions in the brain, including the hippocampus, cortex, and cerebellum ($n=6$). All values are presented as the mean ± SD. Statistical significance in (c) (n.s., $P>0.05$) was assessed using one-way ANOVA, followed by the Bonferroni post hoc test.

3.1.

Comparing the Labeling Efficiency of the Four Selected Vessel Labeling Methods

Efficiently labeling entire vasculatures is fundamental to understanding the organization and structure of vascular networks. Generally, there are two types of widely employed fluorescent vascular labeling strategies in tissue clearing-related research for obtaining vascular information: vessel-specific marker vessel labeling and fluorescent dye filling vessel labeling. Vessel-specific markers, such as CD31 antibodies and lectins, are often directly injected into live mice through the tail vein for sufficient circulation to the entire vascular system. Another common strategy for labeling vessels is to fill the vessel lumen with fluorescent dyes, such as fluorescence-conjugated dextran mixed with gelatin and lipophilic dyes. Based on a comprehensive literature review, we selected four commonly used vessel labeling methods: the injection of two widely-used vessel-specific markers [DyLight 649 conjugated L. esculentum (Tomato) lectin (LEL-D649)], an Alexa Fluor 647 conjugated anti-CD31 antibody (CD31-A647), and common fluorescent dye filling (Gel-Dex-FITC and DiI).

Vascular structures in mouse brains were labeled using the four aforementioned methods based on protocols described in the literature.^15,19,22,26 The labeled mouse brains were sagittally sliced into sections and imaged with a confocal microscope. The entire pipeline is shown in Fig. 1(a). Figure 1(b) displays fluorescence images of the vascular structures labeled with Gel-Dex-FITC, DiI, LEL-D649, and CD31-A647 antibodies, respectively. All of the assessed techniques revealed similar and uniform labeling effects on the brain vasculature, both in the entire collection of sagittal slices and in three typical brain regions, including the cortex, hippocampus, and cerebellum, at high magnification [Figs. 1(b) and 1(c)].

We calculated the signal-to-noise ratios of the fluorescence images from samples labeled using different approaches to evaluate the vessel labeling efficiency of each method. As shown in Fig. 1(d), the signal-to-noise ratio of the lipophilic dye DiI-obtained vasculature was higher than those of vasculatures stained using other protocols. The signal-to-noise ratio of the intravenous injection of LEL-D649-labeled vasculature labeled by intravenous injection of LEL-D649 was similar to the vasculature labeled by transcardial perfusion by Gel-Dex-FITC. Vasculature labeled with the intravenous injection of the CD31-A647 antibody demonstrated a relatively low signal-to-noise ratio compared with other evaluated labeling procedures.

3.2.

Evaluating Vessel Distribution Patterns Created by Different Labeling Methods

Vascular networks in different organs often span several scales from micron-sized capillaries to large vessels extending over several millimeters.¹² It can sometimes be challenging to achieve the complete labeling of entire vasculatures in various organs using a single approach. Therefore, evaluating the distribution patterns of vessels in different organs labeled using different methods is necessary.

We first assessed the labeling patterns created by the four labeling techniques on microvessels in mouse brain tissues by calculating the distribution of the vessel radius and vessel segment length, which are usually used to evaluate the vessel distribution pattern [Fig. 2(a)]. As shown in Fig. 2(b), vascular structures labeled using the four methods exhibited a similar distribution pattern both in vascular diameter and vessel segment length, which are similar to the results in recent publications using perfusion-based vessel labeling and the same quantification procedures,^28,29 namely the diameters of over 95% of the labeled vessels were $<12\mu \mathrm{m}$, and about 70% of vessel segment length were shorter than $50\mu \mathrm{m}$. These results indicate that microvessels accounted for the majority of the entire vascular composition in the brain. Additionally, we also calculated the vessel density in three typical brain regions labeled by the four methods: the cortex, hippocampus, and cerebellum [Fig. 2(c)]. The density of labeled vascular networks is similar among the four labeling methods in the three brain regions, consistent with the quantitative results displayed in previous studies.^10,28 In general, all four examined labeling techniques are capable of realizing the fine labeling of brain microvessels in a similar pattern.

In addition to the brain, we also labeled and imaged vascular networks in different mouse organs. For mouse liver, the capillary networks could be efficiently labeled by all of the tested four methods; however, the uniformity of labeled vasculature by perfusion with DiI is obviously weaker than the other three methods, probably due to the potential dye leakage [Fig. 3(a)]. Additionally, we discovered that large blood vessels were not marked by intravenous injection of CD31-A647 antibody and LEL-D649, leaving unlabeled cavities within vessel lumens [Fig. 3(b)]. In contrast, large vessels could be effectively casted by transcardial perfusion with Gel-Dex-FITC or DiI [Fig. 3(b)]. As for mouse kidney, we also found that vasculature labeled intravenously via the injection of the CD31-A647 antibody and LEL-D649 showed only the glomeruli information, with most of the information for the surrounding vessels being lost [Fig. 3(c)]. For other microvessel-dominated organs, such as the stomach and muscles, the four labeling techniques all achieved better labeling performance [Figs. 3(d) and 3(e)]. Based on these results, we speculated that vessel labeling using intravenous injections of specific markers efficiently labeled microvessels but not some large vessels, whereas vessel labeling via perfusion of dye solution tended to label both large vessels and capillaries.

Fig. 3

Comparison of the labeling efficiency of four selected vessel labeling methods in typical mouse organs. (a) Fluorescence images of overall vessel information and magnification of boxed regions of mouse liver slices labeled using Gel-Dex-FITC, DiI, LEL-D649, and CD31-A647, respectively. (b) Signal profiles of large vessels marked in the magnified images in (a). (e) Fluorescence images of the overall vasculature and magnification of boxed regions of mouse kidney slices labeled using the four methods. (d) Fluorescence images of labeled vessels in the mouse stomach characterized using the four methods. (e) Fluorescence images of labeled vessels in the mouse muscle characterized using the four methods.

3.3.

Assessing the Fluorescence Compatibility of Vessel Labeling with Current Tissue Clearing Methods

The compatibility of the vessel labeling with current tissue-clearing techniques is another important factor in realizing the fine reconstruction of entire vascular networks in different organs. Here, we selected six typical tissue clearing methods: solvent-based methods, such as uDISCO,²¹ FDISCO,²² and PEGASOS,²³ and aqueous-based approaches, such as CUBIC,²⁴ PACT,²⁵ and MACS.¹⁹ We examined the compatibility of each clearing procedure with vessel labeling using the four vessel-labeling methods.

Labeled vascular information in brain samples was imaged before and after clearing with each tissue-clearing method. Signal-to-background ratios pre- and post-clearing were also calculated to quantitatively assess fluorescence preserving capabilities. As shown, all assessed clearing methods decreased the signal-to-background ratio of vessel information labeled using Gel-Dex-FITC perfusion to different degrees after clearing, with uDISCO and FDISCO performing better than the other procedures [Fig. 4(a)]. For vessel information labeled using DiI solution perfusion, the compatibility of most of the clearing methods with DiI was weak; only the MACS clearing approach preserved the fluorescence signals well [Fig. 4(b)]. As for vasculature labeled through intravenous injections of LEL-D649 and CD31-A647 antibodies, the uDISCO and FDISCO methods showed excellent protections for the labeled signals, resulting in no obvious loss of signal-to-background ratios; by contrast, other evaluated clearing methods, especially for aqueous based clearing methods, noticeably decreased the signal-to-background ratios to different degrees after clearing [Figs. 4(c) and 4(d)]. These results suggest that each vessel labeling technique has preferable applicability for certain types of tissue clearing. For instance, Gel-Dex-FITC, LEL-D649, and CD31-A647 antibodies are preferable for solvent-based clearing methods (except PEGASOS, which uses Quadrol solution), such as uDISCO and FDISCO, and DiI is more suitable for detergent- and solvent-free clearing methods, such as MACS.

Fig. 4

Evaluation of the fluorescence compatibilities of four vessel-labeling methods with tissue clearing. (a) Fluorescence images and quantification of signal-to-background ratios in mouse brain slices labeled using Gel-Dex-FITC before and after clearing with each clearing method. (b) Fluorescence images and quantification of signal-to-background ratios in mouse brain slices labeled using DiI before and after clearing with each clearing method. (c) Fluorescence images and quantification of signal-to-background ratios of mouse brain slices labeled using LEL-D649 before and after clearing with each clearing method. (d) Fluorescence images and quantification of signal-to-background ratios of mouse brain slices labeled using CD31-A647 before and after clearing with each clearing method. All values are presented as the mean ± SD. Statistical significance in (a) to (d) (***, $P<0.001$; **, $P<0.01$; *, $P<0.05$; n.s., $P>0.05$) was assessed using an independent-sample t-test.

These quantitative comparison results for evaluating the labeling performance for each labeling method, as well as the compatibilities of each labeling method with different clearing methods, are summarized in Table 1.

Table 1

Comparison of the overall performance of different vessel labeling methods.

3.4.

3D visualization of Vascular Networks in Typical Organs for Health and Disease

Combining vessel labeling with tissue clearing techniques enables the visualization of the 3D morphology details of vasculature using optical imaging procedures, therefore, allowing researchers to examine pathological changes in overall vessel networks during the occurrences of different diseases. Here, based on results from comprehensive evaluations, we selected the optimal combination of fluorescent vessel labeling and tissue-clearing methods to construct vascular networks for several typical organs.

The mouse brain hemisphere labeled using LEL-D649 was cleared by uDISCO to obtain imaging datasets for 3D reconstruction [Fig. 5(a), Video 1]. The overall vascular information at different depths was acquired, and microvessel structures were well observed in different brain regions, including the cortex, hippocampus, and cerebellum [Figs. 5(b) and 5(c)]. As for the mouse liver, the Gel-Dex-FITC solution perfusion procedure was chosen for vascular labeling and FDISCO for clearing; the 3D reconstruction of liver vasculature is shown in Fig. 5(d) and Video 2. Both the large vessels and capillaries were well defined [Fig. 5(e)]. Finally, for clearing DiI-labeled mouse kidneys, the MACS method was selected because it is the only clearing approach capable of preserving the DiI-labeled fluorescent signals. As shown in Figs. 5(f) and 5(g) and Video 3, both the glomerulus trees and branches in kidneys were clearly visible in DiI-labeled MACS-cleared kidneys.

Fig. 5

3D reconstruction of vascular networks in typical organs using the selected combination of vessel labeling and clearing methods. (a) 3D reconstruction of microvascular architecture in the mouse brain hemisphere labeled using LEL-D649 and cleared using uDISCO (Video 1, .mp4, 28.8 MB [URL: https://doi.org/10.1117/1.NPh.9.4.045008.s1]). (b) Acquired optical section images at different imaging depths. (c) High-magnification images of vascular structures in different brain regions. (d) 3D rendering of vascular networks in the mouse liver labeled using Gel-Dex-FITC and cleared using FDISCO (Video 2, .mp4, 29.5 MB [URL: https://doi.org/10.1117/1.NPh.9.4.045008.s2]), (e) Magnification of the boxed regions in (d). (f) 3D reconstruction of glomerular tufts and vessels in the mouse kidney labeled using DiI and cleared using MACS. (g) Magnification of the boxed regions in (f) (Video 3, .mp4, 29.6 MB [URL: https://doi.org/10.1117/1.NPh.9.4.045008.s3]).

In addition to healthy tissues, 3D visualization and quantification of microvascular remodeling under certain pathological conditions are also essential for biomedical studies. For example, TBI is a kind of serious brain injury induced by an external force and is a major cause of death and disability for human beings. TBI will cause severe neurovascular injury to brains, leading to chronic global neurological impairments.^30,31 Here, we used the selected optimal vessel labeling/tissue clearing combinations to investigate the cerebrovascular damage from TBI.

Figure 6(a) shows the experimental workflow for the construction of the TBI mouse model, vascular labeling, tissue clearing, imaging and data analysis. As expected, the selected vessel labeling/tissue clearing combinations enabled 3D visualization of the vascular networks in TBI mouse brain tissue [Fig. 6(b)]. The 3D reconstruction images revealed substantial loss of vascular structures in the injured region compared with the normal contralateral regions [Figs. 6(c) and 6(d)]. The quantitative data also showed that the vessel length densities decreased obviously in the injured regions [Fig. 6(e)], and the major form of vessel damage occurred in microvessels [Fig. 6(f)]. These results revealed that the selected optimal combinations are also appropriate for studying vasculature under specific pathophysiological conditions.

Fig. 6

3D visualization and analysis of changes of cerebrovascular structures in TBI mouse brain. (a) Experimental workflow. (b) 3D Reconstruction of the vascular architecture in the brain reveals an obvious vessel information loss in injury area compared with the contralateral normal area. (c) Magnification of region marked in (b). (d) Magnification of region marked in (b). (e) Quantification of vessel densities in normal and injury regions ($n=4$). (f) Statistical analysis of the distribution of vessel radius in normal and injury regions ($n=4$). All values are presented as the mean ± SD. Statistical significance in (e) and (f) (***, $P<0.001$; **, $P<0.01$) was assessed using an independent-sample t-test.