Blind demixing methods for recovering dense neuronal morphology from barcode imaging data

Shuonan Chen, Jackson Loper, Pengcheng Zhou, Liam Paninski

Abstract: Author summary In situ barcode sequencing allows us to simultaneously locate many neurons in intact brain tissues, albeit at modest spatial resolution. By increasing the barcode density, high-resolution neuronal morphology reconstruction from such data might be possible. Here we use simulations to study this possibility, while addressing the computational challenges in analyzing such data. We developed a novel blind demixing method that uses fluorescent images and identifies the unknown barcodes used to label the neurons with high accuracy. Further, we developed a neural network which can reconstruct the morphology for these labeled neurons from the observed ‘pointilistic’ imaging data. We show that under both high- and low-resolution optical settings, our methods can successfully extract the morphologies for many labeled neurons. The results from this theoretical study suggest that it may be feasible to map the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.