ClearLight Biotechnologies LLC, Sunnyvale, Calif, has recently presented study data focused on applications of the company’s automated next-generation tissue processing and 3-D image analysis platform, currently under development.1

ClearLight is developing an automated instrumentation platform based on the Clarity lipid-clearing technique developed by company founder Karl Deisseroth, MD, PhD, and his colleagues at Stanford University. The technique enables the transformation of tissue into a nanoporous, hydrogel-hybridized form that is crosslinked to a 3-D network of hydrophilic polymers. The process produces a fully assembled, intact tissue that is permeable to macromolecules and optically transparent, thus allowing for robust 3-D imaging of subcellular components (DNA, RNA, and proteins) and analysis of heterogeneous cellular interactions within the microenvironment of a tissue. Paired with proprietary 3-D image analysis software, the technology will enable more-accurate analysis and assessment of normal and diseased tissue.

Technologies currently in use for preclinical and clinical cancer drug development are largely dependent upon the 2-D analysis of thin, formalin-fixed, paraffin-embedded (FFPE) tissue sections (5–10 µm). In recent years, however, the importance of understanding cellular phenotypic information combined with 3-D spatial analysis of tissues has become increasingly apparent. Several clearing techniques, such as Clarity, have been developed and modified as a means to image and evaluate such volumetric tissues. Most such techniques have employed chemical approaches to improve tissue clearing, inadvertently affecting the tissue integrity on a macroscopic or microscopic level.

Previous work with Clarity demonstrated that the tissue-hydrogel matrix is able to maintain its overall structural integrity. However, researchers noted that employing the technique required lengthy processing times, and they also remarked on the system’s lack of robust 3-D spatial analysis software. The company’s latest research sought to address these issues through the development of an automated clearing and staining platform for Clarity processed tissues, together with a proprietary 3-D image analysis platform employing artificial intelligence and machine learning techniques.

All experiments were performed with the Clarity technique using hydrogel matrix-embedded tissues that were cleared with an SDS/borate clearing buffer. Evaluation of the clearing module was performed 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 were then uploaded into proprietary 3-D software for segmentation, classification, and quantitative spatial analysis, demonstrating successful clearing and staining in both normal and cancerous tissue samples in a total time of less than 1 day.

This was a significant reduction in the time associated with the standard passive clearing and staining procedure, and it produced consistent results for both fresh and formalin-fixed tissues. In short, the development of an end-to-end multisample clearing and staining platform not only removes the need for laborious sectioning and registration for sample reconstruction, but also maintains the benefits of multiple interrogation of a single sample.

Although volumetric clearing and 3-D 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.

For further information, visit ClearLight Biotechnologies.


  1. White SL, Chen Y, Shen Q, Goodman LJ. Multisample automation of the Clarity technology for the processing of 3-D volumes of tissue [abstract 4690, online]. Presentation at the annual meeting of the American Association for Cancer Research, Atlanta, March 29–April 3, 2019. Available at: Accessed May 1, 2019.