Lysophosphatidic acid (LPA) is a small, ubiquitous phospholipid that acts as an extracellular signaling molecule through at least six cognate G protein-coupled receptors LPA₁₋₆. These receptors mediate diverse biological responses, including developmental, physiological, and pathophysiological effects. The embryonic nervous system is a major site of LPA presence and action through its receptors, although the precise cellular and mechanistic roles are not well understood. To study the role of LPA signaling in an intact developing cortex, I developed an in utero injection paradigm targeting mouse brain and used histology and immunochemistry in combination with pharmacological perturbation and LPA receptor null mutants to study this system. Overactivation of LPA₁ via a single LPA bolus exposure causes disruption of neuroprogenitor cells (NPCs) residing in the ventricular zone of the fetal brain, numerous accompanying histological changes - including ventricular dilation, formation of neurorosettes and heterotopias, mitotic displacement, loss of ependymal cells, ciliary defects, and 3rd ventricular occlusion - and subsequently fetal hydrocephalus (FH). In addition, exposure to blood derivatives and components such as serum and plasma through hemorrhage, two major sources of LPA, also cause FH. This modeled disorder can be prevented using both LPA₁ null mutants and pharmacological antagonism. This novel role of LPA in the etiology of FH is consistent with multiple, independent hydrocephalic models and clinical observations, suggesting common mechanistic pathways and, importantly, potential therapeutic targets for the amelioration of this disorder

Significant heterogeneities in gene expression among individual cells are typically interrogated using single whole cell approaches. However, tissues that have highly interconnected processes, such as in the brain, present unique challenges. Single-nucleus RNA sequencing (SNS) has emerged as an alternative method of assessing a cell's transcriptome through the use of isolated nuclei. However, studies directly comparing expression data between nuclei and whole cells are lacking. Here, we have characterized nuclear and whole cell transcriptomes in mouse single neurons and provided a normalization strategy to reduce method-specific differences related to the length of genic regions. We confirmed a high concordance between nuclear and whole cell transcriptomes in the expression of cell type and metabolic modeling markers, but less so for a subset of genes associated with mitochondrial respiration. Therefore, our results indicate that single-nucleus transcriptome sequencing provides an effective means to profile cell type expression dynamics in previously inaccessible tissues.

Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, as well as computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for the study of complex processes in the brain, such as genetic programs that coordinate adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, which provided insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains challenging. We developed pathway and gene set overdispersion analysis (PAGODA) to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.