Comparative cellular analysis of motor cortex in human, marmoset and mouse

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals(1). Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.Cardiovascular Aspects of Radiolog

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

A Single-Nucleus RNA-Sequencing Pipeline to Decipher the Molecular Anatomy and Pathophysiology of Human Kidneys.

Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples

Characterization of genome-methylome interactions in 22 nuclear pedigrees

Genetic polymorphisms can shape the global landscape of DNA methylation, by either changing substrates for DNA methyltransferases or altering the DNA binding affinity of cis-regulatory proteins. The interactions between CpG methylation and genetic polymorphisms have been previously investigated by methylation quantitative trait loci (mQTL) and allele-specific methylation (ASM) analysis. However, it remains unclear whether these approaches can effectively and comprehensively identify all genetic variants that contribute to the inter-individual variation of DNA methylation levels. Here we used three independent approaches to systematically investigate the influence of genetic polymorphisms on variability in DNA methylation by characterizing the methylation state of 96 whole blood samples in 52 parent-child trios from 22 nuclear pedigrees. We performed targeted bisulfite sequencing with padlock probes to quantify the absolute DNA methylation levels at a set of 411,800 CpG sites in the human genome. With mid-parent offspring analysis (MPO), we identified 10,593 CpG sites that exhibited heritable methylation patterns, among which 70.1% were SNPs directly present in methylated CpG dinucleotides. We determined the mQTL analysis identified 49.9% of heritable CpG sites for which regulation occurred in a distal cis-regulatory manner, and that ASM analysis was only able to identify 5%. Finally, we identified hundreds of clusters in the human genome for which the degree of variation of CpG methylation, as opposed to whether or not CpG sites were methylated, was associated with genetic polymorphisms, supporting a recent hypothesis on the genetic influence of phenotypic plasticity. These results show that cis-regulatory SNPs identified by mQTL do not comprise the full extent of heritable CpG methylation, and that ASM appears overall unreliable. Overall, the extent of genome-methylome interactions is well beyond what is detectible with the commonly used mQTL and ASM approaches, and is likely to include effects on plasticity