Abstract
Quantitative and qualitative evaluation of synapses is crucial to understand neural connectivity. This is particularly relevant now, in view of the recent advances in regenerative biology and medicine. There is an urgent need to evaluate synapses to access the extent and functionality of reconstructed neural network. Most of the currently used synapse evaluation methods provide only all-or-none assessments. However, very often synapses appear in a wide spectrum of transient states such as during synaptogenesis or neural degeneration. Robust evaluation of synapse quantity and quality is therefore highly sought after. In this paper we introduce QUANTOS, a new method that can evaluate the number, likelihood, and maturity of photoreceptor ribbon synapses based on graphical properties of immunohistochemistry images. QUANTOS is composed of ImageJ Fiji macros, and R scripts which are both open-source and free software. We used QUANTOS to evaluate synaptogenesis in developing and degenerating retinas, as well as de novo synaptogenesis of mouse iPSC-retinas after transplantation to a retinal degeneration mouse model. Our analysis shows that while mouse iPSC-retinas are largely incapable of forming synapses in vitro, they can form extensive synapses following transplantation. The de novo synapses detected after transplantation seem to be in an intermediate state between mature and immature compared to wildtype retina. Furthermore, using QUANTOS we tested whether environmental light can affect photoreceptor synaptogenesis. We found that the onset of synaptogenesis was earlier under cyclic light (LD) condition when compared to constant dark (DD), resulting in more synapses at earlier developmental stages. The effect of light was also supported by micro electroretinography showing larger responses under LD condition. The number of synapses was also increased after transplantation of mouse iPSC-retinas to rd1 mice under LD condition. Our new probabilistic assessment of synapses may prove to be a valuable tool to gain critical insights into neural-network reconstruction and help develop treatments for neurodegenerative disorders.