Adipose (fat) tissue is important for energy balance regulation, acting as a metabolic sensor [1,2]. It controls the storage and utilization of energy as well as food intake [1,2]. The significant increase in obesity and the related chronic disorders, such as cancer, diabetes, and heart disease, has motivated metabolism researchers to investigate in more depth the molecular and developmental mechanisms controlling adipose (fat) tissue development and expansion in relation to obesity [1,2]. Adipose tissue requires neural innervation to regulate metabolic functions . However, much about the innervation is still unknown, in part due to the inability to properly visualize the nerve types within these tissues . Adipose tissue is often studied with fluorescence microscopy, but certain challenges can arise when examining whole mount fat specimens which are unsectioned [3,4]. Recent advances in microscopy has reinvigorated imaging of 3D adipose tissue specimens [3,4].
For transgenic adipose tissue expressing fluorescent proteins, whole mount specimens of the fat tissue is usually difficult to image with widefield, camera-based fluorescence microscopy due to the large background and highly curved surface [3,4].
The study of whole mount tissue specimens, which are round and thick, requires an imaging solution allowing fast screening over a large area, but also enabling sharp images of the entire curved surface to be acquired with good contrast. Widefield microscopy offers ease of use, speed, and detection sensitivity. However, imaging of round, thick specimens often produces images showing only a portion of the surface in clear focus, as well as a “haze” appearing due to detected fluorescence signals emitted from out-of-focus planes within the specimen.
Thick specimens of transgenic mouse adipose tissue expressing red fluorescent protein (RFP) were imaged with a THUNDER Imager Model Organism, a fluorescence stereo microscope platform equipped with the opto-digital Computational Clearing technology developed by Leica Microsystems. The images were cleared with the method Large Volume Computational Clearing (LVCC) . In addition, Extended Depth of Field (EDoF) was used to combine the z stack images into one single image showing all the in-focus objects on the curved surface.
Images of adipose (fat) tissue are shown below in figure 1 where a raw widefield image (1A) is compared with a THUNDER image (1B). The cells of the adipose tissue (adipocytes) are seen more clearly over the entire field of view in the THUNDER image with much less background. LVCC enables users to obtain a clear view of details even deep within an intact sample, in real time without out-of-focus blur. The THUNDER Imager delivers specimen images which are clearer and sharper than those from conventional widefield microscopes.
A THUNDER Imager Model Organism using Large Volume Computational Clearing (LVCC)  was capable of producing sharp images over large areas of highly curved adipose tissue specimens and also clearing the out-of-focus blur or haze. The adipocytes are observed with higher contrast and sharper details throughout the z-stack used for 3D image reconstruction.
- Q.A. Wang, P.E. Scherer, R.K. Gupta, Improved methodologies for the study of adipose biology: insights gained and opportunities ahead, The Journal of Lipid Research (2014) vol. 55, iss. 4, pp. 605-624, DOI: 10.1194/jlr.R046441.
- R.K. Gupta, Adipocytes, Current Biology (2014) vol. 24, iss. 20, pp. R988-R993, DOI: 10.1016/j.cub.2014.09.003.
- J.W. Willows, M. Blaszkiewicz, A. Lamore, S. Borer, A.L. Dubois, E. Garner, W.P. Breeding, K.B. Tilbury, A. Khalil, K.L. Townsend, Visualization and Analysis 2 of Whole Depot Adipose Tissue Innervation, bioRxiv preprint (2019) DOI: 10.1101/788885.
- R. Berry, C.D. Church, M.T. Gericke, E. Jeffery, L. Colman, M.S. Rodeheffer, Imaging of Adipose Tissue, Ch. 4 in Methods in Enzymology, Ed. O.A. Macdougald (Elsevier, Oxford, UK, 2014) vol. 537, pp. 47-73, DOI: 10.1016/B978-0-12-411619-1.00004-5.
- J. Schumacher, L. Bertrand, THUNDER Technology Note: THUNDER Imagers: How Do They Really Work? Science Lab (2019) Leica Microsystems.
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