The current technologies utilized for preclinical and clinical drug development in cancer is largely dependent upon the 2-dimensional (2D) analysis of thin formalin-fixed paraffin embedded (FFPE) tissue sections (5-10 µm). However, the importance of understanding cellular phenotypic information combined with three-dimensional (3D) spatial analysis of tissues has recently evolved. In recent years, several clearing techniques, such as CLARITY, have been developed and modified as a means to image and evaluate these volumetric tissues. Most of these techniques have employed chemical approaches to improve tissue clearing, while inadvertently affecting the tissue integrity on a macroscopic or microscopic level. Our previous work with CLARITY has demonstrated how the tissue-hydrogel matrix (HM) is able to maintain its structural integrity overall. Yet, some of the most noted caveats to employing this technique has been the lengthy processing times, and the lack of robust 3D spatial analysis software. We sought to address these issues through the development of an automated clearing and staining platform for CLARITY processed tissues with a proprietary 3D image analysis employing artificial intelligence and machine learning techniques. All experiments were performed with the CLARITY technique using HM-embedded tissues that were clearing with a SDS/borate clearing buffer. Evaluation of the clearing module was assessed using a passive staining (diffusion-based) approach before sample imaging. The effectiveness of the staining module was assessed using passively cleared tissues that were “actively” stained using the developed respective module, followed by standard imaging. The imaging data was then uploaded into our proprietary 3D software for segmentation, classification, and quantitative spatial analysis. We were able to demonstrate successful clearing and staining in both normal and cancerous tissue samples in a total time of less than one day. Consistent results are obtained for both fresh and formalin-fixed tissues. This was a significant reduction in the time associated with the standard passive clearing and staining procedure. In short, the development of our end-to-end multi-sample clearing and staining platform not only removes the laborious sectioning and sample registration for sample reconstruction, but also maintains the benefits of multiple interrogation of a single sample. Although volumetric clearing and 3D analysis are still in their infancy from a technology perspective, one tissue sample using these novel approaches provides as much volumetric information as 200 FFPE sections, while also maintaining key spatial information.

Citation Format: Sharla L. White, Yi Chen, Qi Shen, Laurie J. Goodman. Multi-sample automation of the CLARITY technology for the processing of 3D volumes of tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4690.