Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing

BACKGROUND

Fine characterization of gene expression patterns is crucial to understand many aspects of embryonic development. The chicken embryo is a well-established and valuable animal model for developmental biology. The period spanning from the third to sixth embryonic days (E3 to E6) is critical for many organ developments. Hybridization chain reaction RNA fluorescent in situ hybridization (HCR RNA-FISH) enables multiplex RNA detection in thick samples including embryos of various animal models. However, its use is limited by tissue opacity.

RESULTS

We optimized HCR RNA-FISH protocol to efficiently label RNAs in whole mount chicken embryos from E3.5 to E5.5 and adapted it to ethyl cinnamate (ECi) tissue clearing. We show that light sheet imaging of HCR RNA-FISH after ECi clearing allows RNA expression analysis within embryonic tissues with good sensitivity and spatial resolution. Finally, whole mount immunofluorescence can be performed after HCR RNA-FISH enabling as exemplified to assay complex spatial relationships between axons and their environment or to monitor GFP electroporated neurons.

CONCLUSIONS

We could extend the use of HCR RNA-FISH to older chick embryos by optimizing HCR RNA-FISH and combining it with tissue clearing and 3D imaging. The integration of immunostaining makes possible to combine gene expression with classical cell markers, to correlate expressions with morphological differentiation and to depict gene expressions in gain or loss of function contexts. Altogether, this combined procedure further extends the potential of HCR RNA-FISH technique for chicken embryology.

Airy beam light sheet microscopy boosted by deep learning deconvolution

Common light sheet microscopy comes with a trade-off between light sheet width defining the optical sectioning and the usable field of view arising from the divergence of the illuminating Gaussian beam. To overcome this, low-diverging Airy beams have been introduced. Airy beams, however, exhibit side lobes degrading image contrast. Here, we constructed an Airy beam light sheet microscope, and developed a deep learning image deconvolution to remove the effects of the side lobes without knowledge of the point spread function. Using a generative adversarial network and high-quality training data, we significantly enhanced image contrast and improved the performance of a bicubic upscaling. We evaluated the performance with fluorescently labeled neurons in mouse brain tissue samples. We found that deep learning-based deconvolution was about 20-fold faster than the standard approach. The combination of Airy beam light sheet microscopy and deep learning deconvolution allows imaging large volumes rapidly and with high quality.