Honestly, this tech is still in its infancy, and is also _suuuuuper_ expensive. Most people are hard-pressed to make use of the gigabytes of sequence data they get from a single experiment, let alone take that forward to N=3 or more.

That isn't to say what you're proposing is impossible, or even impractical, but more to suggest that for such specific questions there are usually cheaper, more specific solutions. You absolutely can compare multiple RNAseq datasets, and adding a spatial component to this should also be possible provided your segmentation/tissue designation is solid enough (or you have cell-specific markers to aid comparisons), but really...I think you need to carefully formulate your question and work out exactly what this very, very expensive (but very, very neat) approach can do that other more conventional methods cannot.

Also, obviously I am jealous of your budget and resources, but that kinda goes without saying. ;-)

Hi John,

Thanks for your comments! Yes, I am aware that it is such an expensive technology (unfortunately...). It feels like that for this technology we are in a similar situation as for the single cell technology a few years back where it was super expensive to do multiple replicates and papers were published with an N = 1 or N = 2.

From a scientific standpoint it is probably good to have multiple biological replicates, but we are also really stretching our budget, so for maybe it is better to allocate funds differently. So instead of doing more replicates for the spatial transcriptomics, we can compare results from a single spatial transcriptomics experiment with immunohistochemistry or smFISH. Using the spatial transcriptomics as an unbiased approached to assess gene expression changes in different areas, which helps us selecting antibodies and probes for further experiments.