The change of microglia numbers within the mice retina, optic nerve and chiasm following intravitreal AAV2-GFP injection

Purpose To explore the optimized concentration of AAV2-GFP for sparse transfection of retinal ganglion cells (RGCs) and optic nerve (ON), and to examine the changes of microglial morphology and distribution in the retina, optic nerve and chiasm after injection. Methods We defined the optimal concentration of AAV2-GFP for sparse labeling of RGCs and axons in WT mice. We further explored the changes of microglial morphology and distribution in the retina, optic nerve and chiasm after intravitreal injection in CX3CR1+/GFP mice. Results 14 days after intravitreal injection of AAV2-GFP, live imaging of the retina showed that fundus fluorescence was very strong and dense at 2.16 × 1011 VG/retina, 2.16 × 1010 VG/retina, 2.16 × 109 VG/retina. RGCs were sparsely marked at a concentration 1:1000 (2.16 × 108 VG/retina) and fundus fluorescence was weak. The transfected RGCs and axons were unevenly distributed in the retina and significantly more RGCs were transfected near the injection site of AAV2-GFP compared to the other sites of the flat-mounted retina. Microglia density increased significantly in the retina and part of optic nerve, but not in the optic chiasm. The morphology of microglia was largely unchanged. Conclusions AAV2-GFP was highly efficient and the optimal concentration of sparsely labeled RGCs was 1:1000 (2.16 × 108 VG/retina). After intravitreal injection of AAV2-GFP, the number of microglia increased partly. The morphology of microglia was comparable.

Bitbow: a digital format of Brainbow enables highly efficient neuronal lineage tracing and morphology reconstruction in single brains

Identifying the cellular origins and mapping the dendritic and axonal arbors of neurons have been century old quests to understand the heterogeneity among these brain cells. Classical chemical and genetic methods take advantage of light microscopy and sparse labeling to unambiguously, albeit inefficiently, trace a few neuronal lineages or reconstruct their morphologies in each sampled brain. To improve the analysis throughput, we designed Bitbow, a digital format of Brainbow which exponentially expands the color palette to provide tens of thousands of spectrally resolved unique labels. We generated transgenic Bitbow Drosophila lines, established statistical tools, and streamlined sample preparation, image processing and data analysis pipelines to allow conveniently mapping neural lineages, studying neuronal morphology and revealing neural network patterns with an unprecedented speed, scale and resolution.

Sparse Labeling Augmentation for Dense Models Training

This work proposes and validates a simple but effective approach to train dense semantic segmentation models from sparsely labeled data. Data and labeling collection is most costly task of semantic segmentation. Our approach needs only a few pixels per image reducing the human interaction required.