Over the last decade, the importance of the stem cells for cell replacement therapy, human disease modeling and drug toxicity/therapy screenings has been greatly appreciated by both the general public and the scientific community. In our application utilizing human embryonic stem cells and induced pluripotent stem cells (iPSCs), we proposed to use Rett syndrome (RTT) as a proof of principle to establish a human cell xenografting paradigm (i.e., transplanting human cells into mouse/rat brains) to study the function of normal and diseased human neurons in vivo. While we increased our knowledge about the electrophysiological characteristics of RTT neurons during Year 2 funding, we mainly focused on the transplantation of the normal and diseased cells, as well as the molecular signatures of transplanted cells at a single cell level, during Year 3 of the funding period. Following our initial transplantation experiments, we observed clustering of the transplanted cells at the injection site, even though there were number of cells integrating into the host brains. In order to circumvent this problem and answer our original questions, we developed an alternative approach. Specifically, we adopted the “transparent brain” methodology to better visualize the integration and the projections of the transplanted cells, as well as the circuitries that they participate, in the host environment to reveal the connectivity of wild type and RTT neurons with the host circuits. With this method, we’re able to follow the transplanted RTT neurons at a higher resolution -without the limitations of the conventional approaches- for studying human iPSC-derived RTT neurons integrated into mouse brains. As part of our last Specific Aim, we’ve performed single neuron gene expression profiling coupled with electrophysiological recordings both in vitro and in vivo. Specifically, we implemented electrophysiological recordings from the transplanted RTT iPSC-derived neurons and isolated the genomic material from the same cell to perform transcriptome analyses. We collected significant amount of data from RNA sequencing experiments and have been performing relevant bioinformatic analyses. In order to complete the gene expression profiling analysis, we obtained a no-cost-extension of the project, and upon completion of the no-cost-extension period, the relevant report will be filed outlining the outcomes of the single neuron transcriptome analysis coupled with electrophysiology. Collectively, our findings provide mechanistic insights into RTT disease pathophysiology, which will facilitate the development of novel therapies for RTT. Lastly, our approach is applicable for studying other neurological disorders in addition to RTT.