BRAIN CONNECTS: Light microscopy for cell-type specific mesoscale to nanoscale dense connectomics - PROJECT SUMMARY In recent years, there has been remarkable progress in mapping the connectivity of cell types in the brain. We now have complete connectomes for insects and worms, and in mammals, we can map synapse connectivity across millimeter-scale regions. As the BRAIN CONNECTS program aims to extend this capability to entire mammalian brains across multiple scales, it is essential to link these connectivity breakthroughs with the brain's molecular architecture. Until now, the connection between fine connectivity obtained through electron microscopy (EM) and molecular cell types has been established through indirect methods such as Patch-seq. Recent developments in light-microscopy-based connectomics (LICONN) have shown promising results for dense connectivity mapping. LICONN has the potential to register connectomics results to molecular subclasses at the single-neuron level and at scale. This project leverages LICONN and our expertise in creating and analyzing EM data to address three main objectives of the BRAIN CONNECTS program.The first objective is to demonstrate that LICONN can be used to create molecularly informed cell type connectivity patterns by integrating its results with large-scale EM data. This approach can provide a powerful tool to establish a ground truth link between molecular and connectivity types across datasets planned with the CONNECTS portfolio. The second objective is to combine light-microscopy-based connectomics with Mesoscale Expansion Microscopy in a multiscale fashion. This integration allows mapping microscale connectivity in gray matter and linking it with white matter connections between brain regions. The third objective is to test the feasibility of expanding LICONN to larger volumes and scale nanoscale expansion microscopy. The goal is to demonstrate that regions larger than a cubic millimeter can be expanded 16 times to achieve LICONN resolution, which is about ten times greater than the current state-of-the-art.
