Clarifying CLARITY: Quantitative Optimization of the Diffusion Based Delipidation Protocol for Genetically Labeled Tissue

Abstract

Tissue clarification has been recently proposed to allow deep tissue imaging without light scattering. The clarification parameters are somewhat arbitrary and dependent on tissue type, source and dimension: every laboratory has its own protocol, but a quantitative approach to determine the optimum clearing time is still lacking. Since the use of transgenic mouse lines that express fluorescent proteins to visualize specific cell populations is widespread, a quantitative approach to determine the optimum clearing time for genetically labeled neurons from thick murine brain slices using CLARITY2 is described. In particular, as the main objective of the delipidation treatment is to clarify tissues, while limiting loss of fluorescent signal, the "goodness" of clarification was evaluated by considering the bulk tissue clarification index (BTCi) and the fraction of the fluorescent marker retained in the slice as easily quantifiable macroscale parameters. Here we describe the approach, illustrating an example of how it can be used to determine the optimum clearing time for 1 mm-thick cerebellar slice from transgenic L7GFP mice, in which Purkinje neurons express the GFP (green fluorescent protein) tag. To validate the method, we evaluated confocal stacks of our samples using standard image processing indices (i.e., the mean pixel intensity of neurons and the contrast-to -noise ratio) as figures of merit for image quality. The results show that detergent-based delipidation for more than 5 days does not increase tissue clarity but the fraction of GFP in the tissue continues to diminish. The optimum clearing time for 1 mm-thick slices was thus identified as 5 days, which is the best compromise between the increase in light penetration depth due to removal of lipids and a decrease in fluorescent signal as a consequence of protein loss: further clearing does not improve tissue transparency, but only leads to more protein removal or degradation. The rigorous quantitative approach described can be generalized to any clarification method to identify the moment when the clearing process should be terminated to avoid useless protein loss.

Intraclonal Plasticity in Mammary Tumors Revealed through Large-Scale Single-Cell Resolution 3D Imaging

“…FUnGI was effective in clearing mammary tissue in 2 h at room temperature, and tissue transparency was maintained ( Figure 1A). Comparison of FUnGI with the clearing agent Ce3D and glycerol (60% or 80%) revealed that both FUnGI and Ce3D, but not glycerol, could efficiently clear the mammary gland, as verified through quantification using the bulk tissue clarity index (BTCi) (Magliaro et al, 2016) (Figures S1B and S1C). In addition, FUnGI appeared to have a faster and higher clearing capacity than Ce3D.…”

“…For tissue clearing assessment and measurement of mammary gland size, images were acquired on a stereomicroscope (Leica M205FA). For tissue clearing assessment, we used the Bulk Tissue Clarification index (Magliaro et al, 2016). For quantification of sample size changes, image thresholding was performed using FIJI and the pixel area was measured.…”

Advances and perspectives in tissue clearing using CLARITY

“…dopaminergic neurons or parva-albuminergic neurons in transgenic animals (figure 3b). In 1 mm brain slices with genetically expressed GFP, when cleared by passive CLARITY, tissue transparency reaches a plateau after 5 days, while the fraction of GFP remaining in the tissue decreases rapidly during clearing (Magliaro et al, 2016), demonstrating a trade-o↵ between transparency and protein retention. Antigens and fluorescent proteins are lost during tissue clearing.…”

Vasculature-staining with lipophilic dyes in tissue-cleared brains assessed by deep learning

“…Quantifying vessel staining using automated segmentation reduces the workload, especially compared to using tools like Fiji (Pan et al, 2016) or measuring fluorescent residue in clearing solution (Magliaro et al, 2016;Ke et al, 2013). Using the deep learning segmentation tool (Intellesis from Zeiss) we achieved reliable data collection with a further reduction in workload.…”