When Visikol was first founded, we were very focused on providing researchers services and products which would enable them to image tissues in 3D. We had initially launched the company as a products and kits company but soon added 3D tissue imaging as a service through a contract research service model to meet the demand from researchers who were interested in the technology but did not want to apply it themselves. However, we always had an eye on the larger applications of tissue clearing and 3D tissue imaging such as applying the approach to the clinic and potentially helping improve the diagnosis of disease and/or helping to determine which treatment would be most effective for a given patient. Over the last six years, we led various research projects in this space and were funded by the NIH as well as the NSF to evaluate the application of our Visikol® HISTO™ tissue clearing technology in the clinic, which presented a few key challenges:
Challenge 1: Defining an Application for Tissue Clearing in the Clinic
The principal problem which we evaluated was where could tissue clearing and 3D imaging actually add value in the clinic? While the practice of histology has been largely unchanged for a century despite some more recent improvements (e.g., ISH, IHC, slide scanning), the practice works quite well and is highly efficacious in diagnosing most forms of cancer while being quite affordable and high throughput. Therefore, a much more complicated and expensive modality such as tissue clearing would need to be applied to an application where the current practices are insufficient and result in poorer than desired outcomes. In evaluating the space, we determined that the only applications that could benefit were the ones in which diagnosis was poor (i.e., high false negative or high false positive rate) and an improved characterization paradigm could improve outcomes. The second point is interesting as it is possible that while tissue clearing could be “better” (i.e., more accurate, precise, robust) than the current methodology, the patient outcome might be unchanged if there are minimal treatments available. We see this phenomenon with many researchers and companies in the digital pathology space such as with PAPNET decades ago, or its contemporary descendants today who are able to improve the sensitivity and/or accuracy of diagnosis. However, if these tools do not result in a change in clinical outcome such as how a patient is treated as there are limited options, then the tool doesn’t actually change clinical outcomes and wont be adopted despite it being “better.”
Challenge 2: Deploying a Tissue Clearing Approach
In 2012, researchers marveled at the first mainstream tissue clearing work that appeared in Nature and featured the CLARITY approach. However, if you ask these researchers how many of them successfully reproduced the approach in their lab, you will have very few researchers respond positively. This isn’t to knock the CLARITY researchers, but instead is to highlight that tissue clearing and 3D microscopy is very challenging and requires the combination of several disparate and highly complex fields. To successfully image a tissue in 3D requires data science, image analysis, IHC expertise, chemistry expertise and of course advanced microscopy expertise.
As such, its challenging to get tissue clearing with any approach to work consistently as there are so many factors at play (e.g., fixation, antibody lot, tissue quality, laser life) which effect its performance. From our experience, we struggled with dropping tissue clearing into clinical workflows, as the tissue preparation process varied so widely from facility to facility, and validating the approach in a decentralized manner (I.e., at local hospitals) or at a centralized facility with multiple different sites was very challenging and inconsistent. For example, the antibody concentration and incubation time is dependent upon the volume and shape of a tissue and slight variations can have a significant impact on data outputs which greatly reduces the sensitivity of the approach.
Ultimately, our work in developing clinical applications for tissue clearing has been unsuccessful to date at Visikol and we were not able to meet the high bar required to develop a clinical diagnostic tool despite significant effort. While we have shown the ability to improve the detection of tumor cells in the context of metastatic melanoma and potentially reduce false negatives in the clinic and improve patient outcomes, we have been unable to implement such an approach in the clinic due to the challenges described above. We found that the variation in samples and the consistency of the approach reduced any added sensitivity gained in comparison to traditional 2D histology and thus the approach added marginal value while adding significant cost and complexity. However, this does not mean that the field is a dead end. Instead, we see 3D microscopy as potentially yet bearing fruit but with other approaches, such as in conjugation with non-label based imaging modalities and using complex image analysis approaches to reduce noise and improve sensitivity.
If you are interested in learning more about tissue clearing, download our Tissue Clearing EBook, check our tissue clearing overview page, or watch our tissue clearing webinar.
Be sure to reach out to a member of our team if you’re interested in discussing your latest project!