https://www.delmic.com/en/products/clem-solutions/secomCurrently affecting over 9% of the world's population, diabetes mellitus is an autoimmune disease with life-threatening consequences . One of the two most familiar forms, Type 1 diabetes, transpires when the insulin-producing beta cells are attacked and destroyed.
In the human body, these cells are situated in a broader conglomeration of cells in the pancreas, known as the islet of Langerhans. Beta cells monitor the blood sugar level and distribute insulin when that level has increased, such as after a meal. This sequentially acts as a stimulant for the cellular uptake of glucose and its transformation into energy.
Patients with Type 1 diabetes possess beta cells which lack the capacity to execute this function, and therefore keep sugar in their blood. The consequent glucose deficiency can cause cell death, while consistently high blood sugar levels can bring about multiple organ failure. The cause of this condition still remains a mystery. This research constitutes one component of wider academic investigation towards establishing a more comprehensive understanding of the condition.
In order to examine the permutations occurring during diabetes, the islets of Langerhans are represented using electron microscopy (EM). The advantage of this technology is its exceptionally powerful spatial resolution, which permits the identification of subcellular characteristics. Nevertheless, it is an innately labored technique, which necessitates scanning of the electron beam with high pixel dwell times to attain images with an adequate signal-to-noise ratio. Furthermore, the identification of the cells on a morphological basis is exceptionally labor intensive and prone to bias.
Conversely, fluorescence microscopy (FM) is a methodology enabling the identification of cells on the basis of their function over a broad field of view. Antibodies tagged with fluorophores are implemented as probes that affix themselves to particular areas of the cell. The use of various fluorophores consequently authorizes multicolor imaging of different functionalities.
Simultaneous Correlative Light and Electron Microscopy (SCLEM) integrates these two methods, culminating in a robust imaging technique whereby the selected cells can be discerned using FM. From this hybrid methodology, a potent magnification EM image can be obtained to illuminate structural features of the desired location at a high resolution.
80 nm thick segments of healthy rat pancreas were assembled for correlative imaging using the procedure described in . Fresh pancreas segments, sectioned and selected for the occurrence of islets of Langerhans, were suspended in 4% paraformaldehyde and 0.1% glutaraldehyde. Post-fixing was enacted with 1% osmium tetroxide, followed by dehydration and embedding in EPON.
Ultrathin sections were subsequently incised and set on ITO-coated glass slides. After this, immuno-labeling was undertaken with three alternative fluorophores, and the sample was imaged on the Delmic SECOM platform integrated with an FEI Verios 460 Scanning Electron Microscope.
The figure shows a simultaneously obtained correlative image incorporating the islets, imaged on the SECOM using an automated overlay mechanism. The labeling of the insulin (beta cells) in orange with Alexa Fluor 594 is plainly detectable and the ultrastructure can be meticulously examined from the EM contrast. The guanine quadruplexes are labeled in green with Alexa Fluor 488, and the nucleus is labeled in blue with Hoechst.
Figure 1. Simultaneously acquired correlative image of the islets of Langerhans imaged on the Delmic SECOM integrated with an FEI Verios 460 Scanning Electron Microscope. The insulin (beta cells) are labelled in orange with Alexa Fluor 594, with the ultrastructure is visible in the EM contrast. The guanine quadruplexes are labelled in green with Alexa Fluor 488, and the nucleus is labelled in blue with Hoechst.
This study demonstrates the potential of correlative microscopy for investigations into type 1 diabetes and points to the next challenge: implementation of large scale imaging using automated data acquisition and stitching.
 G. Danaei et al., The Lancet 378, 9785, 31-40 (2011)
 J. Kuipers et al., Experimental Cell Research 337, 202–207 (2015)
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