Light-sheet microscopy for biomedical research: spatial statistics of retinal ganglion cells in intact mouse eyes

Session

Poster Session

Authors

Robert Lees (1), Benjamin Davis (1)

Affiliations

1. Science and Technology Facilities Council

Keywords

Light-sheet microscopy, retina, ophthalmology, clearing, spatial statistics, 

Abstract text

We have described a workflow from sample preparation to image analysis of whole mouse eyeballs to obtain the spatial statistics of retinal ganglion cells. 

The retina is gaining recognition for its significance in studying neurodegenerative and inflammatory processes, as it allows non-invasive assessment of the central nervous system. This is enabled by advancements in technologies like Adaptive Optics Scanning Laser Ophthalmoscopy that enables single-cell resolution imaging of the retina in vivo [1]. Neurodegenerative and inflammatory processes are pertinent in various systemic disorders critical to human health, such as Alzheimer’s disease, Parkinson’s disease, and neonatal hypoxia. Rodent models are crucial in enhancing our understanding of the mechanisms behind disease-associated neurodegenerative processes, paving the way for early diagnosis, and aiding in the development and evaluation of innovative therapeutic strategies.

Recent progress in spatial statistics has enabled the spatiotemporal modelling of cell populations and their interactions, offering new perspectives on the mechanisms driving neurodegenerative diseases. However, extracting maximum information from rodent ocular tissues remains challenging. Traditional methods like histological sectioning only assess a small portion of the tissue, while retinal wholemounting (Figure 1A, B), despite allowing the evaluation of almost the entire retinal cell population, is a skill-intensive and time-consuming procedure often restricted to specialized ophthalmic labs.

To address these challenges, we are developing comprehensive ophthalmic clearing, imaging, and analysis protocols. These aim to investigate spatial interactions of retinal cell populations over an extended timecourse of disease-relevant models, bypassing many of the limitations of wholemount preparations. Our approach involves intact tissue clearing techniques (Figure 1C,D) [2] combined with light-sheet microscopy and 3D spatial statistical analyses [3,4]. Here, we present a methodological and analytical workflow to extract and localize retinal point patterns from cleared ophthalmic tissues, suitable for subsequent spatial statistical modelling. Additionally, cells can be categorized into types based on spatial, morphological characteristics, or through incorporating secondary fluorescent labels.

In conclusion, methodologies such as these are expected to enable more quantitative and statistically robust biological research. They have the potential to reduce the overall suffering of animals in research by increasing the statistical power of studies, reuse of tissues and datasets, and when applied to other organs, allow for multi-organ correlations.

Figure 1. A) Confocal microscopy image of wholemounted pig retina immunostained for glial (magenta) and retinal pigmented epithelial cells (green) taking ~6 hours of acquisition. Yellow box: region for panel B. B) Zoom of inset in A showing glial and retinal pigmented epithelium at sub-micron resolution. C) Light-sheet microscopy image of wholemount eyeball taking ~30 minutes. Stained with non-specific Syto 17 nuclear dye showing retinal layers across one plane of the eyeball. Yellow box: region for panel D. D) Zoom of inset in B showing single retinal ganglion cell nuclei resolved.

References

[1] Gofas-Salas et al. Improvements to multi-offset adaptive optics scanning laser ophthalmoscopy for in vivo imaging of individual retinal ganglion cell layer neurons in humans. bioRxiv 2020.12.08.416826

[2] Gurdita A, et al. InVision: An optimized tissue clearing approach for three-dimensional imaging and analysis of intact rodent eyes. iScience. 2021, vol 24:8

[3] Davis B, et al. Characterizing microglia activation: a spatial statistics approach to maximize information extraction. Sci Rep. 2017, May; 8.

[4] Davis B, et al. Automatic quantitative analysis of experimental primary and secondary retinal neurodegeneration: implications for optic neuropathies. Cell Death Discovery. 2016; May, 23.