Abstract
Pancreatic islets are endocrine micro-organs scattered throughout the exocrine pancreas. Islets are surrounded by a network of vasculature, ducts, neurons, and extracellular matrix. Three-dimensional imaging is critical for such structural analyses. We have adapted transparent tissue tomography to develop a method to image thick pancreatic tissue slices (1 mm) with multifluorescent channels. This method takes only 2 to 3 days from specimen preparation and immunohistochemical staining to clearing tissues and imaging. Reconstruction of the intact pancreas visualizes islets with β, α, and δ cells together with their surrounding networks. Capturing several hundred islets at once ensures sufficient power for statistical analyses. Further surface rendering provides clear views of the anatomical relationship between islets and their microenvironment as well as the basis for volumetric quantification. As a proof-of-principle demonstration, we show an islet size–dependent increase of intraislet capillary density and an inverse decrease in sphericity.
We have developed a simple and rapid method to image thick human pancreatic tissue slices (∼1 mm) with multifluorescent channels, including spatial visualization and quantification of three-dimensional structures.
Diabetes is one of the most prevalent chronic metabolic diseases worldwide. Insulin-secreting pancreatic β cells play a central role in maintaining glucose homeostasis. With an increased demand for insulin under insulin-resistant states such as puberty, pregnancy, and obesity, diabetes can develop when the overall β-cell compensation fails. A number of studies have also implicated both α cells (1–7) and somatostatin-secreting δ cells (8) in the pathogenesis of diabetes. In addition, the exocrine pancreas has been shown to affect disease progression (9, 10). With many of the aforementioned findings being discovered in mice, it is necessary to validate them with human studies, as there are notable species differences, including gene expression and islet architecture, among others (11–17).
Pancreatic islets have a unique architecture mainly composed of β, α, and δ cells organized in spherical structures throughout the pancreas. Due to their primary role of regulating blood glucose levels, islets are highly vascularized relative to the surrounding exocrine tissue. Previous studies have used traditional two-dimensional (2D) imaging approaches to quantify the degree of vascularization in islets (14). Although these 2D approaches provide important data in islet analysis, three-dimensional (3D) imaging approaches can further elucidate the organization of endocrine cells and capillaries within the islet while also providing insight into the surrounding microenvironment. Here, we describe, to our knowledge, a new approach to immunostain optically clear and image thick sections from the human pancreas. By modifying transparent tissue tomography (T3) (18), we generated 3D reconstructions of the intact pancreas to visualize the natural pancreatic microenvironment, including three different endocrine cell types, capillaries, ducts, and the extracellular matrix (ECM). In addition to 3D visualization, we demonstrate the volumetric-based quantification of intraislet capillary density, sphericity, and islet size frequency.
Materials and Methods
Human pancreas specimens
Human pancreata were generously provided by the Gift of Hope Organ Procurement Organization in Chicago. Written informed consent from a donor or the next of kin was obtained for use of a sample in research. The use of deidentified human tissues in the study was approved by the Institutional Review Board at the University of Chicago. Over the course of developing a 3D imaging method, several specimens were randomly used unless otherwise specified.
Antibodies
The following primary antibodies were used: mouse monoclonal anti-insulin [Research Resource Identifier (RRID): AB_2126399; Abcam, Cambridge, MA], mouse monoclonal antihuman glucagon (RRID: AB_1841774; Sigma-Aldrich, St. Louis, MO), mouse monoclonal antisomatostatin (RRID: AB_778010; Abcam), mouse monoclonal anti–pan-endocrine (RRID: AB_11157008, HPi1; Thermo Fisher Scientific, Waltham, MA), rabbit polyclonal anti-CD31 (RRID: AB_726362; Abcam), mouse monoclonal anti-CK19 (RRID: AB_306048; Abcam), rabbit polyclonal anti–collagen IV (RRID: AB_305584; Abcam), rabbit anti–α smooth muscle actin (α-SMA) (RRID: AB_2223021; Abcam), and 4′,6-diamidino-2-phenylindole (Invitrogen, Carlsbad, CA). The primary antibodies were conjugated with a combination of amine-reactive fluorophores (N-hydroxysuccinimide esters; Thermo Fisher Scientific, Kalamazoo, MI).
T3 for human pancreas imaging
A frozen human pancreas tissue block (∼5 mm in thickness) was fixed in 4% paraformaldehyde, embedded in 2% agarose gel, and mounted on a vibrating microtome. Sections (600 μm to 1 mm in thickness) were collected in cold phosphate-buffered saline. These macrosections were then immunohistochemically stained overnight. Optical clearing was carried out by sequential incubation with 20%, 50%, 80%, and 100% (weight-to-volume) solutions of d-fructose and 0.3% (volume-to-volume) α-thioglycerol (Sigma-Aldrich) for 1 to 2 hours each at 30°C with gentle agitation. A Leica SP8 laser scanning confocal microscope (Leica Microsystems, Buffalo Grove, IL) was used to image tissue slices mounted between coverslips. The 3D reconstruction and analysis were carried out using Fiji and Imaris software (Bitplane, Zurich, Switzerland).
Results
3D imaging of the human pancreas tissue slices
By adapting T3 (18), we have developed a method to 3D image thick human pancreas tissue slices (600 μm to 1 mm) with multifluorescent channels (up to eight channels, where currently five channels are more optimal). Our method takes 2 to 3 days from specimen preparation and immunohistochemistry staining to clearing tissues and imaging. We demonstrate that sufficient tissue clearing of the human pancreas can simply be achieved by sequential incubation with 20%, 50%, 80%, and 100% (weight-to-volume) d-fructose solution in 1 day. The refractive index of high-concentration d-fructose is higher than other water-based clearing reagents. In addition, d-fructose does not cause tissue shrinkage, which occurs in sucrose (19). Furthermore, the use of primary antibodies conjugated with secondary fluorescent dyes for immunohistochemistry staining of pancreas tissue slices makes the choice of primary antibodies free from consideration of their species-specific affinities. Resulting tissue preparations can be stored at −20°C for several months prior to imaging or even for reimaging the same tissue slices, based on our experience. Pancreas tissue blocks do not have to be fresh, but snap-frozen tissues kept at −80°C for several years can be used. In summary, the tissue preparation method for 3D imaging that we have optimized is a simple and rapid procedure that allows flexibility in experimental design.
Islet cell imaging to study islet cellular composition and architecture
The combination of islet cell hormones enables us to examine endocrine cellular composition in individual islets with reference to islet size. Spatial organization of islets can be examined by measuring XYZ-coordinates of each cell type. A full scan of a 600-μm slice immunostained for insulin (green), glucagon (yellow), somatostatin (magenta), and CD31 (red) is shown in Fig. 1A, which label β cells, α cells, δ cells, and blood vessels, respectively. Insets display the same tissue slice from different angles to highlight the depth of the slice as well as the ability to focus in on any islet. A closer view of a cluster of islets in the center of the sample (also shown as an inset in the upper left corner) is shown in Fig. 1B with individual fluorescent channel images and merged ones. Supplemental Video 1 provides a complete 360-degree view of the pancreatic section while also zooming into the tissue for a closer look at the cluster of islets previously mentioned.
Figure 1.
Three-dimensional imaging of the human pancreas tissue slices. (A) Full scan of a 600-μm pancreatic slice immunostained for insulin (green), glucagon (yellow), somatostatin (magenta), and CD31 (red). Three insets for detailed islet resolution and displaying depth with different angles. Scale bar: 1000 μm. (B) Cluster of islets in the sample with individual fluorescent channel images and merged ones. Scale bars included in each image for reference. Scale bars: all 40 μm.
Surface rendering for structural visualization and quantification
Surface rendering is critical to take advantage of 3D imaging, which is a useful tool to visualize the morphology of a structure. This reconstruction provides insight on its spatial structure as well as its interaction with the surrounding microenvironment. A cluster of islets with fluorescent signals in Fig. 2A was converted to a computer-generated surface rendering in Fig. 2B. The 3D rendered images were captured using both a sagittal and coronal clipping plane to reveal the composition and structure of the islet and vasculature (Fig. 2C). The 3D views of the surface rendering are shown in Supplemental Videos 2 and 3. Supplemental Video 2 reports an in situ view of islets with surrounding blood vessels. Supplemental Video 3 shows a top-to-bottom view of the whole stack of rendered images so that the inside structure of individual islet is displayed.
Figure 2.
Islet cell imaging to study islet cellular composition and architecture. (A) Cluster of islets in the sample with individual fluorescent channel images and merged ones. Islets are immunostained for insulin (green), glucagon (yellow), somatostatin (magenta), and CD31 (red). (B) Same cluster of islets converted to a computer-generated surface rendering with multiple angles displayed for 3D viewing. (C) Coronal slice of all islets in the cluster and a sagittal slice of the leftmost islet visualized by use of a computer-generated clipping plane to reveal the intraislet blood vessels. Scale bars included in each image for reference. Scale bars: all 50 μm.
Figure 3.
Islet microenvironment. (A) (a) Selected region of a full scan of a 600-μm pancreatic slice showing staining for collagen type IV (green), a pan-endocrine cell marker (yellow), and CD31 (red). Scale bar: 300 μm. (b) Collagen type IV staining. (c) Merged. Scale bar: 70 μm. (B) (a) The same slice showing staining for CK19 (cyan), a pan-endocrine cell marker (yellow), and CD31 (red). Scale bar: 300 μm. (b) CK19 staining. (c) Merged. Scale bar: 50 μm. (C) (a) A 3D surface rendering of an islet with the ECM and vasculature. Scale bar: 50 μm. (b) Islet and the ECM. (c) Merged. Scale bar: 50 μm. (D) A 3D surface rendering of an islet with ducts, the ECM, and vasculature. Scale bar: 50 μm. (b) Islet and ducts. Scale bar: 30 μm. (c) Islet, ducts, and vasculature. Scale bar: 30 μm.
Islet microenvironment
To study the islet microenvironment, we immunostained islets with a pan-endocrine cell marker antibody (HPi1), which makes two additional channels available for other markers. Here we used them to image the duct cells and the ECM of human pancreas. A pancreatic tissue slice in Fig. 3A.a includes the ECM (collagen IV; green) in addition to islets (yellow) and vasculature (CD31; red). Islets occupied a space that was only partially infiltrated by the ECM (Fig. 3A.b and 3A.c). The same slice is shown with ducts (CK19; cyan) in Fig. 3B.a. Islets were surrounded by duct cells and little infiltration was observed (Fig. 3B.b and 3B.c). The 3D rendered surfaces of merged images of islets, vasculature, and the ECM are shown in Fig. 3C.a. Intraislet collagen and capillary were exposed in the center of the islet (Fig. 3C.b and 3C.c). The 3D rendered surfaces for the duct cells showed that they were thicker than the collagen ECM (Fig. 3D.a) and that they surrounded the islet with little infiltration (Fig. 3D.b and 3D.c). Supplemental Video 4 further illustrates the relationship between endocrine cells and the surrounding ductal network.
Islet vascularization
Spatial relationship of islets to the vasculature is visualized by merging signals from the pan-endocrine cell marker and CD31 staining (Fig. 4). It is apparent that larger capillaries were localized around clusters of islets. The insets in Fig. 4A highlight the branching of these capillaries into a groove within each individual islet. This groove was captured in detail in Fig. 4B.a and 4B.b, where a single islet was depicted with a large capillary protruding from the surface. Using the 3D surface rendering, this vessel-groove dynamic within the islet became more apparent (Fig. 4B.c and Supplemental Video 5). When we sliced in the z-direction into the center of the islet, the large central vessel was still intact and branched out throughout the interior of the islet (Fig. 4B.d). Supplemental Video 6 follows a stepwise progression in the z-direction for tracking the route of the large central vessel as it enters the islet and branches off into smaller capillaries. Fig. 4C depicts another islet (β cells in green and α cells in yellow) with a large branching vessel that protruded from the surface of the islet (also see Supplemental Video 7). Slicing in the z-direction into the center of the islet reveals the extensive branching that the large vessel undergoes upon entering the islet (Fig. 4C.c and Supplemental Video 8).
Figure 4.
Surface rendering for structural visualization and quantification. (A) Full scan of a 600-μm pancreatic slice immunostained for a pan-endocrine cell marker (yellow) and CD31 (red). Insets show large branching capillaries entering various islets. (B) (a–b) A single islet with a large capillary branching from the surface. (c) A 3D surface rendering of the previous islet for enhanced visualization. (d) A 3D surface rendering of the previous islet after slicing in the z-direction into the center of the islet. Scale bars: all 50 μm. (C) A 3D surface rendering of a single islet immunostained for insulin (green), glucagon (yellow), and CD31 (red). (a–c) Images depict a large capillary entering the islet and branching into many smaller capillaries throughout the center and out the back of the islet. Scale bars: all 50 μm.
Afferent islet feeding arterioles
It is known that arterioles are larger than venules in diameter (20). We further confirmed afferent islet feeding arterioles by immunostaining α-SMA, which is expressed in large arteries and veins, as well as arterioles, but not in venules and capillaries (Fig. 5A) (20). Clusters of intermediate, large, and small islets are shown in boxes in Fig. 5A from left to right, with enlarged views shown in Fig. 5B. The intermediate islets are surface rendered to clearly show α-SMA bearing afferent arterioles feeding into islets (Fig. 5C). Blood vessels are made transparent in Fig. 5C.a and removed in Fig. 5C.b. Further removing β cells in Fig. 5C.c reveals the extent of arterioles within islets.
Figure 5.
Afferent islet feeding arterioles. (A) Vascular smooth muscle immunostained with α-SMA (yellow). Shown are blood vessels (red), β cells (green), α cells (cyan), and δ cells (magenta). Scale bar: 500 μm. (B) (a) Intermediate islets. (b) Large islets. (c) Small islets. All correspond to boxes in (A) from left to right. Scale bar: 100 μm. (C) (a) Panel in (B.a) including intermediated islets is surface rendered. Note that blood vessels are made transparent to reveal underlying structures. (b) Blood vessels removed. (c) β cells further removed. Scale bar: 30 μm.
We quantified the number of afferent arterioles in five nondiabetic donors (72 ± 5 islets/donor; 35.2 ± 7.8 years of age; two men and three women). Most islets received one single arteriole (85.0% ± 3.5%). Interestingly, there was no islet size dependency in receiving multiple afferent arterioles observed from small islets to large islets (in a range of diameter from 40 to 300 μm).
Quantification of intraislet capillary density in relation to the islet size
The degree of islet vascularization can be examined by measuring the intraislet capillary volume relative to total islet volume. This quantification process is depicted in Fig. 6A. We first use an image filtering and thresholding macro in ImageJ/Fiji that converts the original fluorescent signal (Fig. 6A.a and 6A.b) into a binary mask for both the islet channel and capillary channel (Fig. 6A.c and 6A.d). Next, the binary channels are merged together using the merge channel feature (Fig. 6A.e). The merged composite image is then imported into Imaris, and 3D surface rendering is applied to all of the islets present in the image (Fig. 6A.f). We then use a masking feature that automatically removes any vasculature signal that falls outside of the identified islet surfaces so that only intraislet capillaries remain (Fig. 6A.g). This last step is shown in the final panel, highlighting the degree of vascularization as only the intraislet capillaries are displayed (Fig. 6A.h). Every islet is automatically given an identification number that is linked to the specific islet within the image with a variety of measurements, including intraislet capillary volume, total islet volume, and sphericity, among others. Using these data, the intraislet capillary density was calculated for several hundred islets per donor. For each measurement, the islets were organized into bins based on their size to see how the degree of vascularization varied based on the size of the islet. Fig. 6B shows intraislet capillary density and islet sphericity in the tail region of five donor pancreata, indicating the type of data that can be obtained using our approach.
Figure 6.
Islet vascularization. (A) Step-by-step processing of images for quantification. (a–b) Islet and capillary original fluorescent channels. (c–d) Binary map of each channel after thresholding. (e) Red-green-blue (RGB) image after merging the two channels. (f) The 3D rendered islets after computer-generated surface rendering. (g) The 3D rendered islets after masking (removal) of interislet capillaries. (h) Remaining intraislet capillaries after hiding islet channel. Scale bars: all 400 μm. (B) Quantification of intraislet capillary density (capillary volume/islet volume) as well as sphericity for islets in the tail region of five donor pancreata; means ± standard error of the mean. Note that islet size on the x-axis is presented as a logarithmic scale considering the high number of small islets and the low number of large islets. Linear regression line plotted on each graph with R2 values presented as well. F, female; M, male; n, the number of islets examined; yo, year old.
Discussion
A key factor for successful 3D imaging has been optical tissue clearing. Since the pioneering work presented as “CLARITY,” one of the most popular methods for tissue clearing (21), a number of different methods have been developed (22–33). Although many of them often require special devices and a lengthy procedure of several days and weeks, we adapted the T3 method (18), which clears thick human pancreatic tissues in 1 day. The versatility of our method further stems from the use of primary antibodies conjugated with fluorescent dyes, which allows any combinations of primary antibodies regardless of their species-specific affinities.
We have recently shown a stereological approach to quantify human β-cell/islet mass based on 2D imaging and quantification (34, 35). One of the major sources of biased measurement is selection of islet-rich areas with inappropriate normalization, which leads to marked overestimation in 2D analyses (34). In contrast, a network structure such as blood vessels may be underestimated in 2D imaging (14). Cohrs et al. (36) reported no islet size–dependent changes and an intraislet capillary density as low as 9% in all four humans studied. It may also be due to a thin-slice preparation (120 μm) and assessment of “large islets” that are “>225 μm in diameter,” where these islets may not have been measured sufficiently in a 3D context. As we have shown, blood vessels and other network structures such as ducts and the ECM are best examined in 3D.
Our large-scale imaging of the whole tissue slice enables us to capture a large number of islets, over several hundred within a tissue slice, which increases the power of statistical analysis. Surface rendering is particularly useful for accurate quantification of volume and distance in 3D imaging. It also helps to better understand the connection between structural and functional interactions. Combining this 3D technique with established 2D imaging techniques and applying them both to the entire human pancreas should allow us to obtain a more complete picture of the changes that occur in the pancreas in health and disease.
Supplementary Material
Acknowledgments
We thank Drs. Piotr Witkowski and J. Michael Millis at the University of Chicago and Dr. Martin Jendrisak and the entire team of the Gift of Hope Organ & Tissue Donor Network in Chicago for providing the human pancreas tissues used in the current study.
Financial Support: The study is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (DK-020595 and DK-072473, to M.H.); the National Institute on Aging (AG-042151, to M.H.); Kovler Family Foundation (to M.H.); and the National Institute of Biomedical Imaging and Bioengineering (K99EB022636, to S.S.-Y.L.).
Author Contributions: Study design: M.H. Method development: J.L.F., S.S.-Y.L., Z.C.W., S.K.O., S.J.K., and M.H. Data collection and analyses: J.L.F., Z.C.W., S.K.O., and M.H. Preparation of the manuscript: J.L.F. and M.H. All authors discussed results and commented on the manuscript.
Disclosure Summary:
S.S.-Y.L. and S.J.K. are founders of TransNostics LLC to commercialize T3. The remaining authors have nothing to disclose.
Glossary
Abbreviations:
- 2D
two-dimensional
- 3D
three-dimensional
- ECM
extracellular matrix
- RRID
Research Resource Identifier
- T3
transparent tissue tomography
- α-SMA
α smooth muscle actin
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