Discovering the molecular genetic principles of cell type organization through neurobiology-guided computational analysis of single cell multi-omics data sets

摘要

ABSTRACT Understanding the biological principles of cell type diversity and organization is necessary for deciphering neural circuits underlying brain function. The recent rapid accumulation of single cell transcriptomic and epigenomic data sets provides unprecedented opportunity to explore the molecular genetic basis of cell type identity, diversity, and organization. However, analysis of multi-omics datasets have been largely driven by statistic methods that typically do not engage the deep knowledge of neurobiology and developmental biology. As such, most statistic methods do not distinguish technical noise and methodological biases from biologically relevant signals and relationships, and have limited power in achieving biological discovery and insight. Neurobiology guided feature selection based on inherent physiological and developmental processes is essential to move beyond simple statistical clustering of molecular types towards achieving multi-modal definition of neuron types and revealing their inherent relationships as a taxonomy. We have discovered that transcriptional architectures of synaptic input/output (I/O) communication may underlie the essence of cortical GABAergic neuron identity. We hypothesize that transcriptional architectures of synaptic communication is a general defining feature for brain neuron types. We will test this hypothesis by performing a series of supervised learning and feature selection analyses of publically available and emerging single sc-transcriptomic data sets across brain areas and systems from the BRIAN Initiative Cell Census Network (BICCN). We further hypothesize that cell type transcriptional signatures of synaptic communication is orchestrated by well-defined gene regulatory programs rooted in epignomic landscape. We will test this hypothesis by joint analysis of sc-transcriptome, ATACseq, and DNA methylome dataset of the same cortical cell populations from BICCN to identify co-expressed gene signatures that reliably define cell identity, focusing on signatures of synaptic communication. Based on transcriptomic and epigenomic architecture of synaptic communication, we will further develop neurobiology guided feature selection algorithms to improve and refine the current statistical clustering the cortical transcriptomic types. In addition, we will generate web portal tools for automated classification of transcriptomic cell types. Our study will establish a unified paradigm of neuronal cell type organization in which epignomic landscape configures core gene regulatory programs to shape synaptic communication properties that define cardinal neuron types. Together, this work will establish a molecular genetic framework for understanding neuronal diversity and achieving a biological classification across brain areas and mammalian species.