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.

Keywords: CLARITY; GFP; image processing; mouse brain slices; quantitative protocol optimization.

Figure 1

Photographs of cerebellar slices at different clearing times. The images were used to calculate BTCi through Equation (1), comparing the intensity of the black line below the slice and in the region without the slice. (pixel size: 0.125 mm).

Figure 2

(A) BTCi as a function of clearing time for control cerebellar slices in PBS (n = 5, red) and in CLARITY clearing solution (n = 5, blue) slices. (B) Cumulative GFP measured in the clearing solution over time (n = 5 slices). (C) Fraction of GFP retained (GFP_fr), expressed as in equation 4, showing no significant differences between slices at the same time point. (D) BTCi and GFP_fr time series obtained grouping results from the 5 different slices together, showing the relationship between the two parameters.

Figure 3

(A) Mean pixel intensity (MPI) as a function of stack depth for tissue slices immersed in clearing solution for different times (n = 2 slices per line). (B) MPI for controls (n = 2 slices per line). (C) CNR (contrast to noise ratio) as a function of stack depth for tissue slices immersed in clearing solution for different times (n = 2 slices per line). (D) MPI for controls (n = 2 slices per line). For each sample acquired, the MPI and CNR were calculated over 200 μm thick regions from 100 different images spaced 2 μm apart, i.e., a total of 100 data points).

Figure 4

Volume view of a confocal stack acquired at day 5 (Ex/Em: 488/502, pixel-to micron ratio: 0.62 μm, z-resolution: 1.2 μm). Volume dimensions (w × l × h): 317 × 317 × 172 μm (numbers in the edges of the box represent distances in microns).