UC Davis

Abstract: Neurodevelopmental Disorders (NDDs), including Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) are disorders that are affected by the developing human brain. The brain has hundreds to thousands of unique cell types within it, and by studying the cell types that are critical in NDDs, this will lead to a greater understanding of the mechanisms of NDDs in the developing brain. Single-cell RNA-seq (scRNA-seq) can shed light on the importance of the many cell types involved in NDDs and the normal brain development process, as it can provide more fine-grained details on individual cells in comparison to bulk RNA sequencing. This thesis project proposes a mathematical objective function that identifies critical cell types for a set of genes and a scRNA-seq dataset. Our objective function was able to identify critical cell types previously identified in literature. A set of ASD risk genes were used as module genes, as well as scRNA-seq data taken from the developing human neocortex for input. Excitatory deep layer neurons (glutamatergic neurons) and microglial cells were found to be of interest with these module genes. A second and third set of module genes were tested, with one being composed of genes indicated in ASD and ID, and the other being composed of genes indicated in both disorders but that are also in synaptic function, long-term potentiation, and calcium signaling that are found to be more highly expressed at postnatal time points. For these sets of module genes, excitatory deep layer neurons (glutamatergic neurons), and cycling progenitors in the G2/M and S phases were found to be critical. Additionally, we show that cells within the same defined cell type (here done using tSNE) have a higher average maximum similarity with each other than with cells outside of their cell type. In contrast, for a random selection of cells, there is a higher average maximum similarity of cells between groupings, rather than in the same grouping. This indicates that the cell types utilized in this project are in fact clustered properly together. In conclusion, utilizing scRNA-seq in conjunction with module genes enables us to identify critical cell types applicable to NDDs.