Hi all, in my upcoming project, I will be using lipid nanoparticles to deliver a drug to certain cells in the brain (in mice). I have a list of the surface receptors on those cells, but I am still deciding which receptor to use to deliver my drug.
My drug's target is present in nearly all of the cells I am most interested in, but is generally lowly expressed elsewhere in the brain. However, I am not sure if there are small populations of cells in the brain that express my drug's target, and if there are these small populations of cell, using one receptor as opposed to another could cause off target effects.
One way I thought of to try to find if there are these small populations is by looking at single-cell RNA-seq datasets for the whole (adult) mouse or human brains, to see if there are populations of cells that co-express my drug's target and each receptor, but it is my understanding that single-cell RNA-seq datasets for adult mouse brains or human brains must be massive and almost unanalyzable, which would make this task very difficult to do. One solution to this would be a dataset that took a random sample of e.g. each cell cluster - this would likely give an accurate description of most cell clusters and would be much smaller than a whole or multiple brains, but I am not aware of any dataset like this.
Therefore, my question - what are some of the best ways to go about choosing a receptor to target a drug-carrying LNP to, given that analyzing the expression of all receptors at a single-cell level would likely be computationally infeasible?
Have you checked the Allen Brain Atlas reference data? They host these massive data sets for both human and mouse that allow you to explore the expression patterns of individual genes of interest throughout all anatomical structures of the brains (and throughout development, too).