Individual variation in the emergence of anterior-to-posterior neural fates from human pluripotent stem cells.

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. In this study, hPSC lines from multiple donors were differentiated toward neuroectoderm and mesendoderm lineages. We revealed dynamic transcriptomic patterns that delineate the emergence of these lineages, which were conserved across lines, along with individual line-specific transcriptional signatures that were invariant throughout differentiation. These transcriptomic signatures predicted an antagonism between SOX21-driven forebrain fates and retinoic acid-induced hindbrain fates. Replicate lines and paired adult tissue demonstrated the stability of these line-specific transcriptomic traits. We show that this transcriptomic variation in lineage bias had both genetic and epigenetic origins, aligned with the anterior-to-posterior structure of early mammalian development, and was present across a large collection of hPSC lines. These findings contribute to developing systematic analyses of PSCs to define the origin and consequences of variation in the early events orchestrating individual human development.

A Switch from Glial to Neuronal Gene Expression Alterations in the Spinal Cord of SIV-infected Macaques on Antiretroviral Therapy.

Despite antiretroviral therapy (ART), HIV-associated peripheral neuropathy remains one of the most prevalent neurologic manifestations of HIV infection. The spinal cord is an essential component of sensory pathways, but spinal cord sampling and evaluation in people with HIV has been very limited, especially in those on ART. The SIV/macaque model allows for assessment of the spinal cord at key time points throughout infection with and without ART. In this study, RNA was isolated from the spinal cord of uninfected, SIV+, and SIV + ART animals to track alterations in gene expression using global RNA-seq. Next, the SeqSeek platform was used to map changes in gene expression to specific cell types. Pathway analysis of differentially expressed genes demonstrated that highly upregulated genes in SIV-infected spinal cord aligned with interferon and viral response pathways. Additionally, this upregulated gene set significantly overlapped with those expressed in myeloid-derived cells including microglia. Downregulated genes were involved in cholesterol and collagen biosynthesis, and TGF-b regulation of extracellular matrix. In contrast, enriched pathways identified in SIV + ART animals included neurotransmitter receptors and post synaptic signaling regulators, and transmission across chemical synapses. SeqSeek analysis showed that upregulated genes were primarily expressed by neurons rather than glia. These findings indicate that pathways activated in the spinal cord of SIV + ART macaques are predominantly involved in neuronal signaling rather than proinflammatory pathways. This study provides the basis for further evaluation of mechanisms of SIV infection + ART within the spinal cord with a focus on therapeutic interventions to maintain synaptodendritic homeostasis.

Early Developmental Origins of Cortical Disorders Modeled in Human Neural Stem Cells.

The implications of the early phases of human telencephalic development, involving neural stem cells (NSCs), in the etiology of cortical disorders remain elusive. Here, we explored the expression dynamics of cortical and neuropsychiatric disorder-associated genes in datasets generated from human NSCs across telencephalic fate transitions in vitro and in vivo. We identified risk genes expressed in brain organizers and sequential gene regulatory networks across corticogenesis revealing disease-specific critical phases, when NSCs are more vulnerable to gene dysfunctions, and converging signaling across multiple diseases. Moreover, we simulated the impact of risk transcription factor (TF) depletions on different neural cell types spanning the developing human neocortex and observed a spatiotemporal-dependent effect for each perturbation. Finally, single-cell transcriptomics of newly generated autism-affected patient-derived NSCs in vitro revealed recurrent alterations of TFs orchestrating brain patterning and NSC lineage commitment. This work opens new perspectives to explore human brain dysfunctions at the early phases of development.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes.

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

A Curated Compendium of Transcriptomic Data for the Exploration of Neocortical Development.

Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of ~30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).

Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus.

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.

Cochlear organoids reveal transcriptional programs of postnatal hair cell differentiation from supporting cells.

We explore the changes in chromatin accessibility and transcriptional programs for cochlear hair cell differentiation from postmitotic supporting cells using organoids from postnatal cochlea. The organoids contain cells with transcriptional signatures of differentiating vestibular and cochlear hair cells. Construction of trajectories identifies Lgr5+ cells as progenitors for hair cells, and the genomic data reveal gene regulatory networks leading to hair cells. We validate these networks, demonstrating dynamic changes both in expression and predicted binding sites of transcription factors (TFs) during organoid differentiation. We identify known regulators of hair cell development, Atoh1, Pou4f3, and Gfi1, and the analysis predicts the regulatory factors Tcf4, an E-protein and heterodimerization partner of Atoh1, and Ddit3, a CCAAT/enhancer-binding protein (C/EBP) that represses Hes1 and activates transcription of Wnt-signaling-related genes. Deciphering the signals for hair cell regeneration from mammalian cochlear supporting cells reveals candidates for hair cell (HC) regeneration, which is limited in the adult.

A single-cell genomic atlas for maturation of the human cerebellum during early childhood.

Inflammation early in life is a clinically established risk factor for autism spectrum disorders and schizophrenia, yet the impact of inflammation on human brain development is poorly understood. The cerebellum undergoes protracted postnatal maturation, making it especially susceptible to perturbations contributing to the risk of developing neurodevelopmental disorders. Here, using single-cell genomics of postmortem cerebellar brain samples, we characterized the postnatal development of cerebellar neurons and glia in 1- to 5-year-old children, comparing individuals who had died while experiencing inflammation with those who had died as a result of an accident. Our analyses revealed that inflammation and postnatal cerebellar maturation are associated with extensive, overlapping transcriptional changes primarily in two subtypes of inhibitory neurons: Purkinje neurons and Golgi neurons. Immunohistochemical analysis of a subset of these postmortem cerebellar samples revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those children who had experienced inflammation. Maturation-associated and inflammation-associated gene expression changes included genes implicated in neurodevelopmental disorders. A gene regulatory network model integrating cell type-specific gene expression and chromatin accessibility identified seven temporally specific gene networks in Purkinje neurons and suggested that inflammation may be associated with the premature down-regulation of developmental gene expression programs.

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].

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

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).

Identifying enhancer properties associated with genetic risk for complex traits using regulome-wide association studies.

Genetic risk for complex traits is strongly enriched in non-coding genomic regions involved in gene regulation, especially enhancers. However, we lack adequate tools to connect the characteristics of these disruptions to genetic risk. Here, we propose RWAS (Regulome Wide Association Study), a new application of the MAGMA software package to identify the characteristics of enhancers that contribute to genetic risk for disease. RWAS involves three steps: (i) assign genotyped SNPs to cell type- or tissue-specific regulatory features (e.g., enhancers); (ii) test associations of each regulatory feature with a trait of interest for which genome-wide association study (GWAS) summary statistics are available; (iii) perform enhancer-set enrichment analyses to identify quantitative or categorical features of regulatory elements that are associated with the trait. These steps are implemented as a novel application of MAGMA, a tool originally developed for gene-based GWAS analyses. Applying RWAS to interrogate genetic risk for schizophrenia, we discovered a class of risk-associated AT-rich enhancers that are active in the developing brain and harbor binding sites for multiple transcription factors with neurodevelopmental functions. RWAS utilizes open-source software, and we provide a comprehensive collection of annotations for tissue-specific enhancer locations and features, including their evolutionary conservation, AT content, and co-localization with binding sites for hundreds of TFs. RWAS will enable researchers to characterize properties of regulatory elements associated with any trait of interest for which GWAS summary statistics are available.

Peptidylarginine Deiminase 2 Autoantibodies Are Linked to Less Severe Disease in Multiple Sclerosis and Post-treatment Lyme Disease.

Background: Peptidylarginine deiminase 2 (PAD2) mediates the post-translational conversion of arginine residues in proteins to citrullines and is highly expressed in the central nervous system (CNS). Dysregulated PAD2 activity has been implicated in the pathogenesis of several neurologic diseases, including multiple sclerosis (MS). In this study, we sought to define the cellular and regional expression of the gene encoding for PAD2 (i.e. PADI2) in the human CNS using publicly available datasets and evaluate whether anti-PAD2 antibodies were present in patients with various neurologic diseases. Methods: A total of 491 study participants were included in this study: 91 people with MS, 32 people with neuromyelitis optica (NMO), 281 people with post-treatment Lyme disease (PTLD), and 87 healthy controls. To measure PADI2 expression in the CNS from healthy individuals, publicly available tissue and single cell RNA sequencing data was analyzed. Anti-PAD2 antibodies were measured in the serum of study participants using anti-PAD2 ELISA. Clinical and demographic variables were compared according to anti-PAD2 antibody positivity for the MS and PTLD groups and correlations between anti-PAD2 levels and disease severity were examined. Results: PADI2 expression was highest in oligodendrocytes (mean ± SD; 6.4 ± 2.2), followed closely by astrocytes (5.5 ± 2.6), microglia/macrophages (4.5 ± 3.5), and oligodendrocyte precursor cells (3.2 ± 3.3). There was an increased proportion of anti-PAD2 positivity in the MS (19.8%; p = 0.007) and PTLD groups (13.9%; p = 0.057) relative to the healthy controls (5.7%), and these antibodies were not detected in NMO patients. There was a modest inverse correlation between anti-PAD2 levels and disease severity in people with MS (τ = -0.145, p = 0.02), with levels being the highest in those with relapsing-remitting disease. Similarly, there was a modest inverse correlation between anti-PAD2 levels and neurocognitive score (τ = -0.10, p = 0.027) in people with PTLD, with difficulty focusing, memory changes, fatigue, and difficulty finding words contributing most strongly to the effect. Conclusion: PADI2 expression was observed in diverse regions and cells of the CNS, and anti-PAD2 autoantibodies were associated with less severe symptoms in subsets of patients with MS and PTLD. These data suggest that anti-PAD2 antibodies may attenuate inflammation in diseases of different etiologies, which are united by high PADI2 expression in the target tissue.

Two-stage linked component analysis for joint decomposition of multiple biologically related data sets.

Integrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets.

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.

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.

Plasma microRNAs are associated with domain-specific cognitive function in people with HIV.

OBJECTIVE: Cognitive impairment remains common in people with HIV (PWH) on antiretroviral therapy (ART). The clinical presentation and severity are highly variable in PWH suggesting that the pathophysiological mechanisms of cognitive complications are likely complex and multifactorial. MicroRNA (miRNA) expression changes may be linked to cognition as they are gene regulators involved in immune and stress responses as well as the development, plasticity, and differentiation of neurons. We examined plasma miRNA expression changes in relation to domain-specific and global cognitive function in PWH. DESIGN: Cross-sectional observational study. METHODS: Thirty-three PWH receiving care at the Southern Alberta Clinic, Canada completed neuropsychological (NP) testing and blood draw. Plasma miRNA extraction was followed by array hybridization. Random forest analysis was used to identify the top 10 miRNAs upregulated and downregulated in relation to cognition. RESULTS: Few miRNAs were identified across cognitive domains; however, when evident a miRNA was only associated with two or three domains. Notably, miR-127-3p was related to learning/memory and miR-485-5p to motor function, miRNAs previously identified in CSF or plasma in Alzheimer's and Parkinson's, respectively. Using miRNET 2.0, a software-platform for understanding the biological relevance of the miRNA-targets (genes) relating to cognition through a network-based approach, we identified genes involved in signaling, cell cycle, and transcription relating to executive function, learning/memory, and language. CONCLUSION: Findings support the idea that evaluating miRNA expression (or any molecular measure) in the context of global NP function might exclude miRNAs that could be important contributors to the domain-specific mechanisms leading to the variable neuropsychiatric outcomes seen in PWH.

Single-Nucleus RNA-Seq Reveals Dysregulation of Striatal Cell Identity Due to Huntington's Disease Mutations.

Huntington's disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (HTT) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/+, and from wild-type controls. We used 14- to 15-month-old male mice, a time point at which multiple behavioral, neuroanatomical, and neurophysiological changes are present but at which there is no known cell death. Thousands of differentially expressed genes (DEGs) were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell type-specific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the polycomb repressive complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum.SIGNIFICANCE STATEMENT Huntington's disease (HD) is a dominantly inherited neurodegenerative disorder characterized by specific loss of medium spiny neurons (MSNs) in the striatum, accompanied by more subtle changes in many other cell types. It is thought that changes in transcriptional regulation are an important underlying mechanism, but cell type-specific gene expression changes are not well understood, particularly at time points relevant to the onset of disease-related symptoms. Single-nucleus (sn)RNA-seq in a genetically precise mouse model enabled us to identify novel patterns of transcriptional dysregulation because of HD mutations, including bidirectional dysregulation of many cell type identity genes that may be driven by partial loss-of-function of the polycomb repressive complex (PRC). Identifying these regulators of transcriptional dysregulation in HD can be leveraged to design novel disease-modifying therapeutics.

Putative Autoantigen Leiomodin-1 Is Expressed in the Human Brain and in the Membrane Fraction of Newly Formed Neurons.

Nodding syndrome is a pediatric epilepsy disorder associated with Onchocerca volvulus infection, but the mechanism driving this relationship is unclear. One hypothesis proposes that parasite-induced immune responses cross-react with human leiomodin-1 resulting in immune-mediated central nervous system (CNS) damage. However, as leiomodin-1 expression and epitope availability in human neurons remains uncharacterized, the relevance of leiomodin-1 autoimmunity is unknown. Leiomodin-1 transcript expression was assessed in silico using publicly available ribonucleic acid (RNA) sequencing databases and in tissue by in situ hybridization and quantitative polymerase chain reaction. Abundance and subcellular localization were examined by cell fractionation and immunoblotting. Leiomodin-1 transcripts were expressed in cells of the CNS, including neurons and astrocytes. Protein was detectable from all brain regions examined as well as from representative cell lines and in vitro differentiated neurons and astrocytes. Leiomodin-1 was expressed on the membrane of newly formed neurons, but not neural progenitor cells or mature neurons. Importantly, leiomodin-1 antibodies were only toxic to cells expressing leiomodin-1 on the membrane. Our findings provide evidence that leiomodin-1 is expressed in human neurons and glia. Furthermore, we show membrane expression mediates leiomodin-1 antibody toxicity, suggesting these antibodies may play a role in pathogenesis.