Excited to release the first preprint from my lab: a new framework to convert existing NGS tube-reactions into spatial techniques using compressed sensing principles and a probabilistic algorithm that we call #Tomographer biorxiv.org/cgi/content/sh…
In the lab we are thrilled about possibilities offered by #spatialomics methods, and a bit impatient... it will take a bit until methods for the different omics are available. That's when we had the idea of using already available low-input protocols to obtain spatial information
The core idea is sampling a tissue by cutting thin slices at several orientations so as to obtain a high throughput readout from each of the strips. The measurements are then used to reconstruct the original localization of the signal by a maximum a posteriori estimation 
So, how do the reconstructions look? Turns out, pretty good! If you think about the sparse raw read counts we use as starting point… well, at least to me, the results looked like magic when @HSchede and @SchneiderChrisG showed them to me for the first time.
If all this reminds you of Tomo-seq, yes, we took inspiration from the pioneering work of Jan Philipp Junker and @AlexandervanOu1. Our generic framework uses a different sampling method and math & resolves highly complex patterns. Also, no need for multiple identical specimens!
We test the method by profiling the brain of a mouse and of a… #dragon! The brain of P. vitticeps is very interesting, we use Tomographer to determine de novo the molecular anatomy of this lizard and resolve an evolutionary dilemma. Check out our preprint for more!
Thanks to all co-authors and everybody involved @JStergiadou, @LarsBorm, Anurag Ranjak, Tracy M. Yamawaki, Fabrice P.A. David, Peter Lönnerberg, Gilles Laurent, @matosches, @SimoneCodeluppi and to @snsf_ch and #ELISIR @epflSV for supporting this effort.
