Enabling scalable multicolor connectomics through expansion microscopy

Abstract

Decades of work in computational power and high resolution imaging have made it possible to observe specific neurons and their connections at a microscopic level. This level of resolution is known as the connectomic level. While we have been able to observe the brain at such resolution for some time thanks to electron microscopy (EM), the analysis of the resulting data has proven to be a very challenging task, mainly because it relies only on membrane contrast to differentiate cells. Expansion microscopy (ExM) offers an exciting alternative to EM by expanding physical brain tissue, with the added benefits that regular optical microscopes can be used for image acquisition. This will enable a faster, cheaper, and multicolor approach to connectomics, and may have the potential to realistically tackle a nervous system as large and complex as a mammalian brain. Currently, the study and the development of Expansion Microscopy are limited by the rate at which experiments run, which can take weeks, and sometimes months. In a previous project, we described a pipeline to create synthetic ExM images (SimExM) and discussed the importance of ground truth data in experimenting with segmentation algorithms. One of the main bottlenecks in creating a full connectome is the need for these ground truth annotations which require a substantial amount of manual work. This work explores various reconstruction strategies based on ExM. We evaluate their dependency on manual annotations and their overall performance, and contribute, as such, to the development of a scalable connectomics pipeline.