The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

The single-cell opioid responses in the context of HIV (SCORCH) consortium

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Multiomic foundations of human prefrontal cortex tissue function

The prefrontal cortex (PFC) is a region of the brain that in humans is involved in the production of higher-order functions such as cognition, emotion, perception, and behavior. Neurotransmission in the PFC produces higher-order functions by integrating information from other areas of the brain. At the foundation of neurotransmission, and by extension at the foundation of higher-order brain functions, are an untold number of coordinated molecular processes involving the DNA sequence variants in the genome, RNA transcripts in the transcriptome, and proteins in the proteome. These "multiomic" foundations are poorly understood in humans, perhaps in part because most modern studies that characterize the molecular state of the human PFC use tissue obtained when neurotransmission and higher-order brain functions have ceased (i.e., the postmortem state). Here, analyses are presented on data generated for the Living Brain Project (LBP) to investigate whether PFC tissue from individuals with intact higher-order brain function has characteristic multiomic foundations. Two complementary strategies were employed towards this end. The first strategy was to identify in PFC samples obtained from living study participants a signature of RNA transcript expression associated with neurotransmission measured intracranially at the time of PFC sampling, in some cases while participants performed a task engaging higher-order brain functions. The second strategy was to perform multiomic comparisons between PFC samples obtained from individuals with intact higher-order brain function at the time of sampling (i.e., living study participants) and PFC samples obtained in the postmortem state. RNA transcript expression within multiple PFC cell types was associated with fluctuations of dopaminergic, serotonergic, and/or noradrenergic neurotransmission in the substantia nigra measured while participants played a computer game that engaged higher-order brain functions. A subset of these associations - termed the "transcriptional program associated with neurotransmission" (TPAWN) - were reproduced in analyses of brain RNA transcript expression and intracranial neurotransmission data obtained from a second LBP cohort and from a cohort in an independent study. RNA transcripts involved in TPAWN were found to be (1) enriched for RNA transcripts associated with measures of neurotransmission in rodent and cell models, (2) enriched for RNA transcripts encoded by evolutionarily constrained genes, (3) depleted of RNA transcripts regulated by common DNA sequence variants, and (4) enriched for RNA transcripts implicated in higher-order brain functions by human population genetic studies. In PFC excitatory neurons of living study participants, higher expression of the genes in TPAWN tracked with higher expression of RNA transcripts that in rodent PFC samples are markers of a class of excitatory neurons that connect the PFC to deep brain structures. TPAWN was further reproduced by RNA transcript expression patterns differentiating living PFC samples from postmortem PFC samples, and significant differences between living and postmortem PFC samples were additionally observed with respect to (1) the expression of most primary RNA transcripts, mature RNA transcripts, and proteins, (2) the splicing of most primary RNA transcripts into mature RNA transcripts, (3) the patterns of co-expression between RNA transcripts and proteins, and (4) the effects of some DNA sequence variants on RNA transcript and protein expression. Taken together, this report highlights that studies of brain tissue obtained in a safe and ethical manner from large cohorts of living individuals can help advance understanding of the multiomic foundations of brain function.

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Insights for disease modeling from single-cell transcriptomics of iPSC-derived Ngn2-induced neurons and astrocytes across differentiation time and co-culture

BACKGROUND

Trans-differentiation of human-induced pluripotent stem cells into neurons via Ngn2-induction (hiPSC-N) has become an efficient system to quickly generate neurons a likely significant advance for disease modeling and in vitro assay development. Recent single-cell interrogation of Ngn2-induced neurons, however, has revealed some similarities to unexpected neuronal lineages. Similarly, a straightforward method to generate hiPSC-derived astrocytes (hiPSC-A) for the study of neuropsychiatric disorders has also been described.

RESULTS

Here, we examine the homogeneity and similarity of hiPSC-N and hiPSC-A to their in vivo counterparts, the impact of different lengths of time post Ngn2 induction on hiPSC-N (15 or 21 days), and the impact of hiPSC-N/hiPSC-A co-culture. Leveraging the wealth of existing public single-cell RNA-seq (scRNA-seq) data in Ngn2-induced neurons and in vivo data from the developing brain, we provide perspectives on the lineage origins and maturation of hiPSC-N and hiPSC-A. While induction protocols in different labs produce consistent cell type profiles, both hiPSC-N and hiPSC-A show significant heterogeneity and similarity to multiple in vivo cell fates, and both more precisely approximate their in vivo counterparts when co-cultured. Gene expression data from the hiPSC-N show enrichment of genes linked to schizophrenia (SZ) and autism spectrum disorders (ASD) as has been previously shown for neural stem cells and neurons. These overrepresentations of disease genes are strongest in our system at early times (day 15) in Ngn2-induction/maturation of neurons, when we also observe the greatest similarity to early in vivo excitatory neurons. We have assembled this new scRNA-seq data along with the public data explored here as an integrated biologist-friendly web-resource for researchers seeking to understand this system more deeply: https://nemoanalytics.org/p?l=DasEtAlNGN2&g=NES .

CONCLUSIONS

While overall we support the use of the investigated cellular models for the study of neuropsychiatric disease, we also identify important limitations. We hope that this work will contribute to understanding and optimizing cellular modeling for complex brain disorders.

High-resolution omics of vascular ageing and inflammatory pathways in neurodegeneration

High-resolution omics, particularly single-cell and spatial transcriptomic profiling, are rapidly enhancing our comprehension of the normal molecular diversity of gliovascular cells, as well as their age-related changes that contribute to neurodegeneration. With more omic profiling studies being conducted, it is becoming increasingly essential to synthesise valuable information from the rapidly accumulating findings. In this review, we present an overview of the molecular features of neurovascular and glial cells that have been recently discovered through omic profiling, with a focus on those that have potentially significant functional implications and/or show cross-species differences between human and mouse, and that are linked to vascular deficits and inflammatory pathways in ageing and neurodegenerative disorders. Additionally, we highlight the translational applications of omic profiling, and discuss omic-based strategies to accelerate biomarker discovery and facilitate disease course-modifying therapeutics development for neurodegenerative conditions.

Cell and tissue-specific glycosylation pathways informed by single-cell transcriptomics

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ~40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ~200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

Moving CNS axon growth and regeneration research into human model systems

Axon regeneration is limited in the adult mammalian central nervous system (CNS) due to both intrinsic and extrinsic factors. Rodent studies have shown that developmental age can drive differences in intrinsic axon growth ability, such that embryonic rodent CNS neurons extend long axons while postnatal and adult CNS neurons do not. In recent decades, scientists have identified several intrinsic developmental regulators in rodents that modulate growth. However, whether this developmentally programmed decline in CNS axon growth is conserved in humans is not yet known. Until recently, there have been limited human neuronal model systems, and even fewer age-specific human models. Human in vitro models range from pluripotent stem cell-derived neurons to directly reprogrammed (transdifferentiated) neurons derived from human somatic cells. In this review, we discuss the advantages and disadvantages of each system, and how studying axon growth in human neurons can provide species-specific knowledge in the field of CNS axon regeneration with the goal of bridging basic science studies to clinical trials. Additionally, with the increased availability and quality of 'omics datasets of human cortical tissue across development and lifespan, scientists can mine these datasets for developmentally regulated pathways and genes. As there has been little research performed in human neurons to study modulators of axon growth, here we provide a summary of approaches to begin to shift the field of CNS axon growth and regeneration into human model systems to uncover novel drivers of axon growth.

CellCover Defines Conserved Cell Types and Temporal Progression in scRNA-seq Data across Mammalian Neocortical Development

Accurate identification of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Such analyses are often based on the existence of highly discriminating "marker genes" for specific cell classes which enables a deeper functional understanding of these classes as well as their identification in new, related datasets. Currently, marker genes are defined by methods that serially assess the level of differential expression (DE) of individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes, that can only be captured by analyzing several genes at the same time. We wish to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization which can be solved with integer programming. In this formulation, the covering elements are genes, and the objects to be covered are cells of a particular class, where a cell is covered by a gene if that gene is expressed in that cell. Our method, CellCover, identifies a panel of marker genes in scRNA-seq data that covers one class of cells within a population. We apply this method to generate covering marker gene panels which characterize cells of the developing mouse neocortex as postmitotic neurons are generated from neural progenitor cells (NPCs). We show that CellCover captures cell class-specific signals distinct from those defined by DE methods and that CellCover's compact gene panels can be expanded to explore cell type specific function.Transfer learning experiments exploring these covering panels across in vivo mouse, primate, and human scRNA-seq datasets demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as markers of temporal progression in the molecular identity of these cell types across development of the mammalian neocortex. The gene covering panels we identify across cell types and developmental time can be freely explored in visualizations across all the public data we use in this report at with NeMo Analytics [1] through https://nemoanalytics.org/p?l=CellCover . The code for CellCover is written in R and the Gurobi R interface and is available at [2].

Dissecting the tumor microenvironment of epigenetically driven gliomas: Opportunities for single-cell and spatial multiomics

Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.