To begin unraveling the mysteries of the brain we must first understand its fundamental unit: the neuron. Recent advances in single cell RNA-sequencing (scRNA-seq) technology have resulted in the rapid generation of high dimensional data for individual cells, revealing vast heterogeneity and enabling the prediction of novel cell subtypes based on expression patterns. In spite of this wealth of data, a theoretical framework to define subtype identity is lacking. Cross-laboratory expression analysis has emerged as a powerful approach to obtain robust and replicable results and we have previously shown that scRNA-seq data is readily amenable to gene co-expression meta-analysis. Here, we propose that these methods, in combination with cross-species analysis, will provide important validation of novel subtypes, and allow for the identification of new gene functional modules that drive neuronal diversity. Our overall hypothesis is that coordinated gene expression patterns underlie neuronal identity and function.
The specific aims to address this hypothesis combine both computational and experimental approaches. First, we will characterize cell-type specific co-expression using RNA-seq data from bulk tissue and from single cells, defining best practices for network construction and identifying and characterizing gene modules that are unique to single cell data. Second, we will define a data- driven cell type taxonomy for the mouse and human nervous systems, and use this to identify neuronal subtype marker genes for validation with co-in situ hybridization and follow up with gene targeting and further scRNA-seq experiments. Finally, we will elucidate functional pathways that are unique to or conserved across species by comparing mouse and human neuronal subtype co-expression networks, with a particular focus on characterizing the cell type specific connectivity of psychiatric disease genes.
These aims are consistent with the National Institute of Mental Health's strategic plan to describe the molecules, cells and neural circuits associated with complex behaviors. As a result, these studies will have a significant impact on our understanding of neuronal diversity and identity, leading to improved understanding of the molecular pathology of brain disorders.
It has long been known that there are many different kinds of neurons that are involved in controlling brain activity. However, it is unclear precisely how many different types of neurons exist, and to what degree human neurons resemble those of other animals. In this project we propose that by combining information about the gene activity in both human and mouse neurons we will begin to put together a final `parts list' of the nervous system which we can use to understand brain disorders.
| Crow, Megan; Paul, Anirban; Ballouz, Sara et al. (2018) Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat Commun 9:884 |
| Crow, Megan; Gillis, Jesse (2018) Co-expression in Single-Cell Analysis: Saving Grace or Original Sin? Trends Genet 34:823-831 |
| Kalish, Brian T; Cheadle, Lucas; Hrvatin, Sinisa et al. (2018) Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement. Proc Natl Acad Sci U S A 115:E1051-E1060 |
| Paul, Anirban; Crow, Megan; Raudales, Ricardo et al. (2017) Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity. Cell 171:522-539.e20 |
