Aim 3 (Rahul) : We will extend our analysis to integrate neuronal datasets across species, mapping human cell types to their mouse counterparts, and demonstrating a path towards functionally characterization of human cell states.
Here, we will demonstrate that cross-species comparisons can map human discoveries onto experimentally tractable models, analagous to the essential role of comparative genomics for interpreting the human genome project.
Aim 3 (John): We will extend the methods developed in Aims 1 and 2 to integrate data from sequential stages of development, thus providing the ability to stitch together datasets from multiple time points while controlling for confounding effects. This approach will help us understand cell fate decisions and provide insight into developmental processes. While initially using data from mouse development, the methods developed will be applicable to in vitro differentiation protocols that use organoid development as a basis on which to study human development.
Aim 3 (Oliver): We will extend these approaches derived in Aim 2 to integrate spatial single-cell readouts with conventional scRNA-seq collected from disassociated cell populations. This is a particular important instance of data integration that will be required for aligning spatial tissue contexts and single-cell RNA-seq, a critical analysis aspect of the HCA. The approaches we seek to derive will provide the computational foundation for the joint analysis of multiple spatial datasets coupled with conventional scRNA-seq.
3. Prior Contributions and Preliminary Data