Islet transplantation has been used to treat diabetes, but is faced with various challenges such as the unavailability of donor organs and the requirement for immunosuppression. Islet encapsulation is a promising solution to some of these problems, helping to achieve insulin independence over longer time. Currently, the standard method to evaluate the encapsulation quality is based on manual microscopic screening of the sample, which is extremely low-throughput and laborious, due to the limited field-of-view (FOV) of standard optical microscopes. Here we report a high-throughput islet encapsulation quality screening system based on lens-free on-chip imaging, which can image/analyze over ~8,000 microcapsules in a single shot. The system utilizes a large format CMOS image sensor that has 60 megapixels, providing a sample FOV of 18.15cm2, which is >100-fold larger than the FOV of a lens-based optical microscope. The encapsulated islets are loaded into a custom-made chamber, which is placed onto the image sensor chip with <2mm gap between the sample and sensor planes. A blue LED provides illumination of the sample, casting in-line holograms of the islets onto the image sensor, which is then analyzed by a custom-written image reconstruction and processing software. The total count of the microcapsules, their size, intactness and whether they contain an islet or not, are analyzed, with the results provided to the user. Being high-throughput, low-cost and simple, this platform can be used for researchers to develop encapsulation protocols as well as a quality control tool before the actual transplantation into patients.
Yibo Zhang, Michael Alexander, Sam Yang, Rahul Krishnan, Yinxu Bian, Elliot Botvinick, Jonathan Lakey, and Aydogan Ozcan, "High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging (Conference Presentation)," Proc. SPIE 10497, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVI, 104970T (Presented at SPIE BiOS: January 30, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2291453.5751469779001.
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