Quantification of pancreatic islet distribution in situ in mice

Abstract

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. Precisely monitoring pancreatic beta-cell proliferation and islet growth is a challenging area of research. We have developed a method to capture the distribution of beta-cells in the intact pancreas of transgenic mice with fluorescence-tagged beta-cells with a macro written for ImageJ (rsb.info.nih.gov/ij/). Total beta-cell area and islet number and size distribution are quantified with reference to specific parameters and location for each islet and for small clusters of beta-cells. The entire distribution of islets can now be plotted in three dimensions, and the information from the distribution on the size and shape of each islet allows a quantitative and a qualitative comparison of changes in overall beta-cell area at a glance.

Fig. 1.

Quantitative optical image analysis of the entire distribution of pancreatic islets in the intact adult pancreas. A: a series of contiguous images of a specimen was taken using a ×2 objective (a male RIP-Tag mouse at 15 wk). Note a mosaic image covering the entire distribution of islets, including small clusters of β-cells (<10 cells). B: a virtual slice that combines all the images into a single image montage. The dorsal pancreas is on the right and the ventral pancreas is on the left. Scale bar, 5 mm. C: mask rendering of fluorescing particles. Color codes are as follows: blue, low-intensity fluorescence; green, intermediate-intensity fluorescence; red, high-intensity fluorescence. Scale bar, 5 mm. D: quantification of each islet/small cluster of β-cells. Note that each islet (including small clusters of β-cells) is identified and numbered, which corresponds to individual information listed in a tab-delimited text file by a programmed macro using ImageJ. a: 4 parameters were recorded on each structure: 1) area; 2) perimeter, distance surrounding an area; 3) circularity, degree of roundness where 1.0 depicts a perfect circle; 4) Feret's diameter, longest distance within an area. Note: #417 with area of 1,971 μm², for example, represents the resolution of the analysis, which is equivalent to only a few β-cells. b: islet shape, defined by a combination of parameters. Shape of an individual islet can be determined by using parameters of perimeter, circularity, and Feret's diameter. Three islets (#724, #1059, and #737) are similar in area; however, #1059 shows a distinct shape, which reflects a longer perimeter, longer Feret's diameter, and low number in circularity compared with those of #724 and #737.

Fig. 2.

Application of watershed segmentation. A: representative false capture of multiple objectives as one structure. a: closely residing islets are prone to be captured as a continuous structure in a large-scale optical image analysis. b: a corresponding fluorescent image shows that there are multiple islets within a single cluster recorded in a. Scale bar, 500 μm. c: watershed segmentation recognized each islet structure and divided it into 4 islets. B: representative false capture of low-intensity fluorescent light scattering. a: a low-intensity mask (blue) captures β-cell clusters and small islets accurately but falsely interconnects larger fluorescent particles prone to light scattering by surrounding tissue. b: original image of fluorescing islets and β-cell clusters. Scale bar, 500 μm. c: results of particle analysis after size exclusion filters correspond closely to captured fluorescent particles in b. C: outline rendering of particle analysis. Color codes are as follows: light blue, small islets and β-cell clusters (≤1 × 10⁵ μm² with circularity >0.7); dark blue, islets (≤1 × 10⁵ μm² with circularity >0.7) subject to watershed segmentation; light red, large islets (≥1 × 10⁵ μm² with circularity >0.7); dark red, islets (>1 × 10⁵ μm² with circularity ≤0.7) that were segmented; and light green, islets (≅1 × 10⁵ μm² with circularity ≤0.7) that were excluded, because an increased threshold decreases the measured area; dark green, islets (≅1 × 10⁵ μm² with circularity ≤0.7). Scale bar, 5 mm. D: Three-dimensional scatter plot showing size and shape distribution of each islet. a: comparison of pre-watershed (red) and post-watershed (blue) is shown. b: post-watershed data plotted alone.

Fig. 3.

Progressive development of insulinoma in RIP-Tag mice. A: representative stages of insulinoma development are shown in order of increasing β-cell area. Dorsal pancreas is on the right, and ventral pancreas is on the left. All males. #1: pancreas from a wild-type mouse at 12 wk as a control; #2–#10 RIP-Tag mice (#2: 4 wk; #3 and 4: 8 wk; #5: 10 wk; #6 and 7: 15 wk; #8–10: 20 wk. NB: #6 specimen was used as a model in Figs 1 and 2). A series of outline renderings of particle analyses (left) demonstrates progressive β-cell tumor growth in situ. Corresponding 3-D scatter plots of islet parameters depict distribution of islets with various sizes and shapes (right). B: histogram showing distribution of islets. a: distribution of total β-cell area is shown in increments of 1 × 10³ μm². Note that β-cell area >10 × 10³ μm² is compiled in the last column. b: β-cell area >10 × 10³ μm² shown in Aa is partitioned in increments of 10 × 10³ μm². Note difference in frequency (y-axis) from histogram shown in Ba.

Fig. 4.

Mathematical analysis of insulinoma progression. Log plots of distribution of β-cell area at each stage of developing insulinoma. The numerical value of each β-cell area is converted to an effective diameter s (a parameter that depicts the same area of a perfect circle) and is plotted as scattered dots. Note that overall growth of β-cells fit into a log normal function, where with substantial tumor development (at #6 and thereafter) the distribution of islets >1,000 μm in effective diameter falls off from the curve with a rightward shift and rather fits a power law distribution (dashed line), suggesting unregulated tumor growth.

Fig. 5.

Additional validation of the method. A: islet size distribution of virtual slice analysis (3-wk-old wild-type mice, n = 5). B: islet size distribution of immunohistochemical analysis (3-wk-old wild-type mice, n = 3). C: log-normal plot comparison of virtual slice (○) and immunohistochemical analysis (●). Both analyses show a similar range of islet size distribution. However, the virtual slice method provides an analysis that more precisely fits the log-normal distribution.