Joshua Orvis

Single-cell and spatial transcriptomic datasets provide tremendous potential to identify the cellular states and communication networks associated with senescence. Annotating senescent cells in these datasets is hindered by the lack of universal markers for senescent cells and their context-dependent variability. To overcome this, several groups have developed gene signatures to annotate cells with distinct senotypes. However, comparing these signatures and their biological contexts to determine the specificity of senescence signatures remains complicated by the separate data platforms for signatures and datasets. We present SENgEAR, a no-code web platform for senescent cell annotation, visualization of cell-cell communication networks, and comparison of senescence signatures across studies, tissues, species, and data modalities. SENgEAR provides a unified framework to explore contextual signature specificity, cellular communication pathways, and senotype heterogeneity. We demonstrate the use of SENgEAR for applications spanning aging, fibrosis, and wound healing and its ability to interrelate these states.

Fibrotic conditions contribute to significant global morbidity and mortality. Yet the underlying processes that orchestrate fibrosis remain poorly understood due to the cellular and spatial complexity of the stromal, immune, and vascular compartments that regulate fibrotic disease progression. Senescent cells (SnCs) have been implicated in fibrosis, but their roles are unclear, as evidence indicates that they serve both pathogenic and reparative functions. Here, we show that fibrosis-associated SnCs contain functionally divergent senotypes that are organized into distinct spatial niches. Using integrated single-cell and spatial transcriptomics analyses and hierarchical factorization in a murine fibrosis model, we identify fibroblast and perivascular SnC subpopulations that upregulate diverse programs related to extracellular matrix (ECM) production, immune signaling, and vascular remodeling. Fibroblast senotypes localize to discrete microenvironments with distinct tissue architectures, including niches associated with fibrotic signaling, immune activity, and cartilage development. Perivascular SnCs occupy interfaces between fibrotic signaling and immune-active niches and upregulate vascular and fibrotic remodeling pathways. Depletion of pericyte-lineage SnCs increases vascular maturation and fibrotic ECM deposition, providing mechanistic validation of the beneficial role these SnCs play in vascular remodeling and fibrosis modulation. In addition, using a new web-based infrastructure to query our senotype gene signatures in public datasets, we demonstrate that these senotypes are conserved across different murine and human fibrotic conditions. These findings establish senescence as a spatially organized regulator of fibrosis and identify perivascular senescence as a link between vascular remodeling and fibrotic outcomes.

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required 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 and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its 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 by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

The gastropod molluskAplysiais an important model for cellular and molecular neurobiological studies, particularly for investigations of molecular mechanisms of learning and memory. We developed an optimized assembly pipeline to generate an improvedAplysianervous system transcriptome. This improved transcriptome enabled us to explore the evolution of cognitive capacity at the molecular level. Were there evolutionary expansions of neuronal genes between this relatively simple gastropodAplysia(20,000 neurons) andOctopus(500 million neurons), the invertebrate with the most elaborate neuronal circuitry and greatest behavioral complexity? Are the tremendous advances in cognitive power in vertebrates explained by expansion of the synaptic proteome that resulted from multiple rounds of whole genome duplication in this clade? Overall, the complement of genes linked to neuronal function is similar betweenOctopusandAplysia.As expected, a number of synaptic scaffold proteins have more isoforms in humans than inAplysiaorOctopus. However, several scaffold families present in mollusks and other protostomes are absent in vertebrates, including the Fifes, Lev10s, SOLs, and a NETO family. Thus, whereas vertebrates have more scaffold isoforms from select families, invertebrates have additional scaffold protein families not found in vertebrates. This analysis provides insights into the evolution of the synaptic proteome. Both synaptic proteins and synaptic plasticity evolved gradually, yet the last deuterostome-protostome common ancestor already possessed an elaborate suite of genes associated with synaptic function, and critical for synaptic plasticity.

Theileria parva is an economically important, intracellular, tick-transmitted parasite of cattle. A live vaccine against the parasite is effective against challenge from cattle-transmissible T. parva but not against genotypes originating from the African Cape buffalo, a major wildlife reservoir, prompting the need to characterize genome-wide variation within and between cattle- and buffalo-associated T. parva populations. Here, we describe a capture-based target enrichment approach that enables, for the first time, de novo assembly of nearly complete T. parva genomes derived from infected host cell lines. This approach has exceptionally high specificity and sensitivity and is successful for both cattle- and buffalo-derived T. parva parasites. De novo genome assemblies generated for cattle genotypes differ from the reference by ~54K single nucleotide polymorphisms (SNPs) throughout the 8.31 Mb genome, an average of 6.5 SNPs/kb. We report the first buffalo-derived T. parva genome, which is ~20 kb larger than the genome from the reference, cattle-derived, Muguga strain, and contains 25 new potential genes. The average non-synonymous nucleotide diversity (π_N) per gene, between buffalo-derived T. parva and the Muguga strain, was 1.3%. This remarkably high level of genetic divergence is supported by an average Wright’s fixation index (F_ST), genome-wide, of 0.44, reflecting a degree of genetic differentiation between cattle- and buffalo-derived T. parva parasites more commonly seen between, rather than within, species. These findings present clear implications for vaccine development, further demonstrated by the ability to assemble nearly all known antigens in the buffalo-derived strain, which will be critical in design of next generation vaccines. The DNA capture approach used provides a clear advantage in specificity over alternative T. parva DNA enrichment methods used previously, such as those that utilize schizont purification, is less labor intensive, and enables in-depth comparative genomics in this apicomplexan parasite.

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.

The gEAR portal (gene Expression Analysis Resource, umgear.org) is an open access community-driven tool for multi-omic and multi-species data visualization, analysis and sharing. The gEAR supports visualization of multiple RNA-seq data types (bulk, sorted, single cell/nucleus) and epigenomics data, from multiple species, time points and tissues in a single-page, user-friendly browsable format. An integrated scRNA-seq workbench provides access to raw data of scRNA-seq datasets for de novo analysis, as well as marker-gene and cluster comparisons of pre-assigned clusters. Users can upload, view, analyze and privately share their own data in the context of previously published datasets. Short, permanent URLs can be generated for dissemination of individual or collections of datasets in published manuscripts. While the gEAR is currently curated for auditory research with over 90 high-value datasets organized in thematic profiles, the gEAR also supports the BRAIN initiative (via nemoanalytics.org) and is easily adaptable for other research domains.

Theileria parva is an economically important, intracellular, tick-transmitted parasite of cattle. A live vaccine against the parasite is effective against challenge from cattle-transmissible T. parva but not against genotypes originating from the African Cape buffalo, a major wildlife reservoir, prompting the need to characterize genome-wide variation within and between cattle- and buffalo-associated T. parva populations. Here, we describe a capture-based target enrichment approach that enables, for the first time, de novo assembly of nearly complete T. parva genomes derived from infected host cell lines. This approach has exceptionally high specificity and sensitivity and is successful for both cattle- and buffalo-derived T. parva parasites. De novo genome assemblies generated for cattle genotypes differ from the reference by ∼54K single nucleotide polymorphisms (SNPs) throughout the 8.31 Mb genome, an average of 6.5 SNPs/kb. We report the first buffalo-derived T. parva genome, which is larger than the genome from the reference, cattle-derived, Muguga strain. The average non-synonymous nucleotide diversity ( π _N ) per gene, between buffalo-derived T. parva and the Muguga strain, was 1.3%. This remarkably high level of genetic divergence is supported by an average F _ST , genome-wide, of 0.44, reflecting a degree of genetic differentiation between cattle- and buffalo-derived T. parva parasites more commonly seen between, rather than within, species, with clear implications for vaccine development. The DNA capture approach used provides clear advantages over alternative T. parva DNA enrichment methods used previously and enables in-depth comparative genomics in this apicomplexan parasite.

The primary motor cortex (M1) is essential for voluntary fine motor control and is functionally conserved across mammals. Using high-throughput transcriptomic and epigenomic profiling of over 450,000 single nuclei in human, marmoset monkey, and mouse, we demonstrate a broadly conserved cellular makeup of this region, whose similarity mirrors evolutionary distance and is consistent between the transcriptome and epigenome. The core conserved molecular identity of neuronal and non-neuronal types allowed the generation of a cross-species consensus cell type classification and inference of conserved cell type properties across species. Despite overall conservation, many species specializations were apparent, including differences in cell type proportions, gene expression, DNA methylation, and chromatin state. Few cell type marker genes were conserved across species, providing a short list of candidate genes and regulatory mechanisms responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allowed the Patch-seq identification of layer 5 (L5) corticospinal Betz cells in non-human primate and human and characterization of 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.

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.