Landau Theory and the Emergence of Chirality in Viral Capsids

We present a generalized Landau-Brazovskii theory for the solidification of chiral molecules on a spherical surface. With increasing sphere radius one encounters first intervals where robust achiral density modulations appear with icosahedral symmetry via first-order transitions. Next, one en- counters intervals where fragile but stable icosahedral structures still can be constructed but only by superposition of multiple irreducible representations. Chiral icoshedral structures appear via continuous or very weakly first-order transitions. Outside these parameter intervals, icosahedral symmetry is broken along a three-fold axis or a five-fold axis. The predictions of the theory are compared with recent numerical simulations

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brai

A Guide to the Brain Initiative Cell Census Network Data Ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis

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 mammals1. 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

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

Integrated computational analysis of brain cell transcriptomes and epigenomes

The mammalian brain consists of a vast network of neurons and non-neuronal cells with diverse morphology, anatomy, physiology and behavioral roles. These cellular phenotypes are enacted and maintained by a complex molecular program, including the abundance of gene transcripts, i.e. the transcriptome, and epigenetic modifications of DNA, i.e., the epigenome. Single cell sequencing assays, capable of measuring the entire transcriptome or epigenome for hundreds of thousands of single cells, have enabled the systematic characterization of brain cell types at unprecedented scale and with fine granularity. However, it is challenging to integrate diverse datasets, which differ in sample and library preparation, sequencing platforms, and assay modalities, for a consistent biological understanding of cell type organization. This thesis presents novel computational algorithms to integrate brain cell transcriptomes and epigenomes. We developed SingleCellFusion, which integrates disparate datasets into a common feature space based on a constrained k-nearest-neighbor graph algorithm. Using SingleCellFusion, we integrated 8 datasets with >400,000 cells from the mouse primary motor cortex (MOp). This analysis identified 56 neuronal cell types with consistent cell type specific patterns of gene expression, chromatin accessibility and DNA methylation. To validate the accuracy of SingleCellFusion, we helped to develop a novel multimodal sequencing assay, snmCAT-seq, that simultaneously measures methylCytosine (mC), chromatin Accessibility (A), and Transcriptome (T) from the same cells. Applying snmCAT-seq to 3,898 human frontal cortex cells, we identified fine grained neuronal cell types. SingleCellFusion integrated single-cell transcriptomes and DNA methylomes from the same cell types with 62.6~87.3% accuracy, recapitulating snmCAT-seq results at the cell type level. Cell type specific gene expression is in part regulated by epigenetic modifications of DNA at cis-regulatory elements (CREs), which are typically located thousands of base pairs away from the gene they regulate. We took advantage of co-variations in gene expression and epigenetic activity at candidate CREs across cell types to identify brain cell-type-specific gene-CRE associations. We developed a method that identified more than 10,000 robust gene-CRE associations from mouse MOp, using an empirical data shuffling procedure to control for false positives due to gene co-expression. Our results highlight the power of integrating transcriptomes and epigenomes to uncover the complex molecular regulation of brain cell types, and will directly enable design of reagents to target specific cell types for functional analysis. It also demonstrates that robust and efficient computational analysis methods are imperative to distill biological understandings from disparate large-scale single cell sequencing data