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Quantitative Assessment of Pancreatic Islets Using Laser Scanning Cytometry
Publication Name:
Current Protocols in Cytometry
Unit Number:
Unit 6.32
DOI:
10.1002/0471142956.cy0632s56
Online Posting Date:
April, 2011
Abstract
Insulin-dependent (type II) diabetes is characterized by an inability to metabolize glucose, resulting from insufficient insulin function for glucose transport from the blood to tissues. One cause of insufficiency is malfunction of the insulin-producing beta cells within the pancreatic islets. Various compounds to stimulate and restore normal islet function are under development. Zucker diabetic fatty (ZDF) rat animal models are used to measure efficacy of drug candidates, as they show clinical effects similar to those in diabetic patients. Drug effects are evaluated by removing the pancreas from ZDF rats, processing the tissue with paraffin and sectioning it, and then analyzing the sections utilizing antibodies against targeted proteins to quantify morphology and metabolic activity. This protocol describes quantitative analysis of insulin, glucagon, mitochondria (all on a per-islet basis), and insulin-positive proliferating cells in ZDF and lean rat pancreatic tissue sections using the iCyte Imaging Cytometer. Curr. Protoc. Cytom. 56:6.32.1-6.32.17. © 2011 by John Wiley & Sons, Inc.
Keywords: imaging cytometry; laser scanning cytometry; multi-parameter tissue analysis; pancreatic islets biomarkers
Table of Contents
- Introduction
- Basic Protocol: Automated Multi-Parameter Staining of Beta and Alpha Islet Cells for Quantitative Analysis
- Support Protocol 1: Image Acquisition
- Support Protocol 2: Image Analysis
- Support Protocol 3: Data Analysis
- Commentary
- Literature Cited
- Figures
- Tables
Materials
Basic Protocol: Automated Multi-Parameter Staining of Beta and Alpha Islet Cells for Quantitative Analysis Materials
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Figures
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Figure 6.32.1 Structure of the pancreas (http://www.nanotechgalaxy.com). -
Figure 6.32.2 Four-laser iCyte Automated Imaging Cytometer. -
Figure 6.32.3 LSC scan system and representative images. -
Figure 6.32.4 Combination of high-resolution images into CompuColor mosaic images. -
Figure 6.32.5 Low- and high-resolution images. -
Figure 6.32.6 DAPI and AF532 compensated out of the green PMT. -
Figure 6.32.7 Four different event-based components for identifying different cell types. -
Figure 6.32.8 Peripheral contours allow measurement of expression outside the event contour. -
Figure 6.32.9 Random sampling elements for extracting quantitative data from the samples independent of event-based segmentation. -
Figure 6.32.10 Data analysis scattergrams for the nuclei component. First, single-cell events are isolated based on DNA content (gating region R1). Then glucagon+ (region R2) and insulin+ (region R3) cells are identified. Note that the insulin+ (beta) cells exclude the glucagon+ (alpha) cells. -
Figure 6.32.11 Data analysis scattergrams for the Ki67 component. First, single-cell events are isolated based on Ki67 expression (gating region R4), then this Ki67+ (R4) population is further divided to identify glucagon+ (region R5) and insulin+ (region R6) cells. Note that the insulin+ (beta) cells exclude the glucagon+ (alpha) cells. -
Figure 6.32.12 Relating gated subpopulations to the image data.
Videos
Literature Cited
| Literature Cited | |
| Chatzigeorgiou, A., Halapas, A., Kalafatakis, K., and Kamper, E.F. 2009. The use of animal models in the study of diabetes mellitus. In Vivo 23:245-258. | |
| Darzynkiewicz, Z., Bedner, E., Li, X., Gorczyca, W., and Melamed, M.R., 1999. Laser-scanning cytometry: A new instrumentation with many applications . Exp. Cell Res. 249:1-12. | |
| Gerstner, A.O.H., Trumpfheller, C., Racz, P., Osmancik, P., Tenner-Racz, K., and Tarnok, A. 2004. Quantitative histology by multicolor slide-based cytometry. Cytometry A 59:210-219. | |
| Harnett, M.M. 2007. Laser scanning cytometry: Understanding the immune system in situ. Nat. Rev. Immunol. 7:897-904. | |
| Luther, E., Kamentsky, L., Henriksen, M., and Holden, E. 2004. "Next-generation laser scanning cytometry". In Methods in Cell Biology, Vol.75 (D.M. Prescott, ed.) pp. 185-218. Elsevier Academic Press, San Diego, Calif. | |
| McCabe, E.R.B. 1994. Microcompartmentation of energy metabolism at the outer mitochondrial membrane: Role in diabetes mellitus and other diseases. J. Bioenerg. Biomembr. 26:317-325. | |
| Muhammed, S., Ahmed, M., Kessler, B., and Salehi, A. 2010. Mitochondrial proteome analysis reveals changes in expression of multiple proteins in pancreatic beta cells exposed to high glucose. Diabetologia 53:S200. | |
| Nugent, D.A., Smith, D.M., and Jones, H.B. 2008. A review of islet of Langerhans degeneration in rodent models of type 2 diabetes. Toxicol. Pathol. 36:529-551. | |
| Peterson, R.A., Krull, D.L., and Butler, L. 2008. Applications of laser scanning cytometry in immunohistochemistry and routine histopathology. Toxicol. Pathol. 36:117-132. | |
| Pozarowski, P., Holden, E., and Darzynkiewicz, Z. 2006. "Laser scanning cytometry: Principles and applications". In Methods in Molecular Biology, Vol. 319 (J.M. Walker, ed.) pp. 165-192. Humana Press, New York. | |
| Wright, E.E., Stonehouse, A.H., and Cuddihy, R.M., 2010. In support of an early polypharmacy approach to the treatment of type 2 diabetes. Diabetes Obesity Metab. 12:929-940. | |
