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1AbstractAccurate 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 discriminatingpanelsof 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 acrossin vivomouse, 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] throughhttps://nemoanalytics.org/p?l=CellCover. The code for CellCover is written in R and the Gurobi R interface and is available at [2].Graphical Abstract

MotivationIt is necessary to develop exploratory tools to learn from the unprecedented volume of high-dimensional multi-omic data currently being produced. We have developed an R package, SJD, which identifies components of variation that are shared across multiple matrices. The approach focuses specifically on variation across the samples/cells within each dataset while incorporating biologist-defined hierarchical structure among input experiments that can spanin vivoandin vitrosystems, multi-omic data modalities, and species.ResultsSJD enables the definition of molecular variation that is conserved across systems, those that are shared within subsets of studies, and elements unique to individual matrices. We have included functions to simplify the construction and visualization of highly complexin silicoexperiments involving many diverse multi-omic matrices from multiple species. Here we apply SJD to decompose four RNA-seq experiments focused on neurogenesis in the neocortex. The public datasets used in this analysis and the conserved transcriptomic dynamics in mammalian neurogenesis that we define here can view viewed and explored together athttps://nemoanalytics.org/p?l=ChenEtAlSJD2022&g=DCX.Availability and ImplementationThe SJD package can be found athttps://chuansite.github.io/SJD.Contacthzchenhuan@gmail.com;ccolant1@jhmi.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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).

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).KEY POINTSThe Neuroscience Multi-Omic Archive serves as the genomics data repository for the BRAIN Initiative.Genomic data from >50 million cells span all the major regions of the brains of humans and mice.We provide a searchable web portal, a cloud-computing interface, and a data visualization platform.

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, Htt(Q175/+), 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.

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 risk of neurodevelopmental disorders. Here, using single-cell genomics, we characterize the postnatal development of cerebellar neurons and glia in 1-5-year-old children, comparing those who died while experiencing inflammation vs. non-inflamed controls. Our analyses reveal that inflammation and postnatal 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 brains revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those subjects experiencing inflammation. Maturation- and inflammation-associated genes were strongly enriched for those 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 the effects of inflammation correspond to blunted cellular maturation.One Sentence SummaryPost-mortem cerebelli from children who perished under conditions that included inflammation exhibit transcriptomic changes consistent with blunted maturation of Purkinje neurons compared to those who succumbed to sudden accidental death.

HIV-associated neurocognitive disorders (HAND) remain prevalent despite implementation of antiretroviral therapy (ART). Development of HAND is linked to mitochondrial dysfunction and oxidative stress in the brain; therefore, upregulation of antioxidant defenses is critical to curtail neuronal damage. Superoxide dismutase 2 (SOD2) is a mitochondrial antioxidant enzyme essential for maintaining cellular viability. We hypothesized that SOD2 was upregulated during retroviral infection. Using a simian immunodeficiency virus (SIV)-infected macaque model of HIV, quantitative PCR showed elevated SOD2 mRNA in cortical gray (GM, 7.6-fold for SIV vs. uninfected) and white matter (WM, 77-fold for SIV vs. uninfected) during SIV infection. Further, SOD2 immunostaining was enhanced in GM and WM from SIV-infected animals. Double immunofluorescence labeling illustrated that SOD2 primarily co-localized with astrocyte marker glial fibrillary acidic protein (GFAP) in SIV-infected animals. Interestingly, in ART-treated SIV-infected animals, brain SOD2 RNA levels were similar to uninfected animals. Additionally, using principal component analysis in a transcriptomic approach, SOD2 and GFAP expression separated SIV-infected from uninfected brain tissue. Projection of these data into a HIV dataset revealed similar expression changes, thereby validating the clinical relevance. Together, our findings suggest that novel SOD2-enhancing therapies may delay the onset or reduce severity of HAND seen in ART-treated HIV-infected patients.

We explored the transcriptional and epigenetic programs underlying the differentiation of hair cells from postnatal progenitor cells in cochlear organoids. Heterogeneity in the cells including cells with the transcriptional signatures of mature hair cells allowed a full picture of possible cell fates. Construction of trajectories identified Lgr5+ cells as progenitors for hair cells and the genomic data revealed gene regulatory networks leading to hair cells. We validated these networks, demonstrating dynamic changes both in expression and predicted binding sites of these transcription factors during organoid differentiation. We identified known regulators of hair cell development, Atoh1, Pou4f3, and Gfi1, and predicted novel 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 HC regeneration which is limited in the adult.

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. We sequenced the transcriptomes of 40,927 cells from the prenatal human hypothalamus spanning from 6 to 25 gestational weeks and 25,424 mature neurons in regions of the adult human hypothalamus, revealing a temporal trajectory from proliferative stem cell populations to mature neurons and glia. Developing hypothalamic neurons followed branching trajectories leading to 170 transcriptionally distinct neuronal subtypes in ten hypothalamic nuclei in the adult. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence from the same individuals, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. Cross-species comparisons to the mouse hypothalamus identified human-specificPOMCpopulations expressing unique combinations of transcription factors and neuropeptides. These results provide the first comprehensive transcriptomic view of human hypothalamus development at cellular resolution.One-Sentence SummaryUsing single-cell genomics, we reconstructed the developmental lineages by which precursor populations give rise to 170 distinct neuronal subtypes in the human hypothalamus.

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.

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 framework to identify the characteristics of enhancers that contribute to genetic risk for disease. Applying our technique to interrogate genetic risk for schizophrenia, we found that risk-associated enhancers in this disease are predominantly active in the brain, evolutionarily conserved, and AT-rich. The association between AT percentage and risk corresponds to an overrepresentation in risk-associated enhancers for the binding sites of transcription factors that recognize AT-rich cis-regulatory motifs. Several of the TFs identified in our model as being overrepresented in risk-associated enhancers, including MEF2C, are master regulators of neuronal development. The genes that encode several of these TFs are themselves located at genetic risk loci for schizophrenia. This list also includes brain-expressed TFs that have not previously been linked to schizophrenia. In summary, we developed a generalizable approach that integrates GWAS summary statistics with enhancer characteristics to identify risk factors in tissue-specific regulatory regions.AUTHOR SUMMARYEnhancers are regulatory regions that influence gene expression via the binding of transcription factors. Risk for many heritable diseases is enriched in regulatory regions, including enhancers. In this study, we introduce a novel method of testing for association between enhancer attributes and risk and use this method to determine the enhancer characteristics that are associated with risk for schizophrenia. We found that enhancers associated with schizophrenia risk are both evolutionarily conserved and in physical contact with mutation-intolerant genes, many of which have neurodevelopmental functions. Risk-associated enhancers are also AT-rich and contain binding sites for neurodevelopmental transcription factors.

Summary 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. The code to conduct 2s-LCA has been complied into an R package “PJD”, which is available at https://github.com/CHuanSite/PJD.

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. We identify differences in the spontaneous self-organization of individual hPSC lines during self-renewal that lie along a fundamental axis of in vivo development. Distinct stable and dynamic transcriptional elements were revealed by decomposition of RNA-seq data in pluripotency and early lineage emergence. Stable differences within pluripotency predicted regional bias in the dynamics of neural differentiation that were also observed in large collections of hPSC lines. Using replicate human induced PSC (hiPSC) lines and paired adult tissue, we demonstrate that cells from individual humans expressed unique transcriptional signatures that were maintained throughout life. In addition, replicate hiPSC lines from one donor showed divergent expression phenotypes driven by distinct chromatin states. These stable transcriptional states are under both genetic and epigenetic control and predict bias in subsequent forebrain and hindbrain neural trajectories. These results define mechanisms controlling transcription in pluripotency to first establish human neural diversity. Data availability: GSE164055; https://nemoanalytics.org/p?l=KimEtAL2021&g=GBX2. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/435870v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1b5dc19org.highwire.dtl.DTLVardef@851d5forg.highwire.dtl.DTLVardef@14b703forg.highwire.dtl.DTLVardef@1b63ad0_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

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., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

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.

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.

Motivation Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset. Results We developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis. Availability and implementation projectR is available on Bioconductor and at https://github.com/genesofeve/projectR. Contact gsteinobrien@jhmi.edu or ejfertig@jhmi.edu Supplementary information Supplementary data are available at Bioinformatics online.

SUMMARY Better understanding of the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here, we use RNA sequencing, cell imaging, and lineage tracing of mouse and human in vitro NSCs and monkey brain sections to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs develop from pluripotency in vitro, they transition through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines vary in these early patterning states, leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analyses of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon., In Brief Micali et al. report that human telencephalic NSCs in vitro transition through the organizer states that pattern the neocortex. Human pluripotent lines vary in organizer formation, generating divergent neuronal differentiation trajectories biased toward dorsal or ventral telencephalic fates and opening further analysis of the earliest cortical specification events., Graphical Abstract

Summary Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g. time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel patternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with patternMarkers requires whole-genome data. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTEx data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data. Availability and Implementation PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license. Supplementary information Supplementary data are available at Bioinformatics online.

New approaches are urgently needed to glean biological insights from the vast amounts of single cell RNA sequencing (scRNA-Seq) data now being generated. To this end, we propose that cell identity should map to a reduced set of factors which will describe both exclusive and shared biology of individual cells, and that the dimensions which contain these factors reflect biologically meaningful relationships across different platforms, tissues and species. To find a robust set of dependent factors in large-scale scRNA- Seq data, we developed a Bayesian non-negative matrix factorization (NMF) algorithm, scCoGAPS. Application of scCoGAPS to scRNA-Seq data obtained over the course of mouse retinal development identified gene expression signatures for factors associated with specific cell types and continuous biological processes. To test whether these signatures are shared across diverse cellular contexts, we developed projectR to map biologically disparate datasets into the factors learned by scCoGAPS. Because projecting these dimensions preserve relative distances between samples, biologically meaningful relationships/factors will stratify new data consistent with their underlying processes, allowing labels or information from one dataset to be used for annotation of the other—a machine learning concept called transfer learning. Using projectR, data from multiple datasets was used to annotate latent spaces and reveal novel parallels between developmental programs in other tissues, species and cellular assays. Using this approach we are able to transfer cell type and state designations across datasets to rapidly annotate cellular features in a new dataset without a priori knowledge of their type, identify a species-specific signature of microglial cells, and identify a previously undescribed subpopulation of neurosecretory cells within the lung. Together, these algorithms define biologically meaningful dimensions of cellular identity, state, and trajectories that persist across technologies, molecular features, and species.GRAPHICAL ABSTRACT

Summary Transcriptional regulatory changes in the developing and adult brain are prominent features of brain diseases, but the involvement of specific transcription factors (TFs) remains poorly understood. We integrated brain-specific DNase footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain. We identified key regulator TFs whose predicted target genes were enriched for differentially expressed genes in the prefrontal cortex of individuals with psychiatric and neurodegenerative diseases. Many of these TFs were further implicated in the same diseases through disruption of their binding sites by disease-associated SNPs and associations of TF loci with disease risk. Using primary human neural stem cells, we validated network predictions that link the TF POU3F2 to schizophrenia and bipolar disorder via both cis- and trans-acting mechanisms. Our models of brain-specific TF binding sites and target genes provide a resource for network analysis of brain diseases.

Interest is great in the new molecular concepts that explain, at the level of signal transduction, the process of reprogramming. Usually, transcription factors with developmental importance are used, but these approaches give limited information on the signaling networks involved, which could reveal new therapeutic opportunities. Recent findings involving reprogramming by genetic means and soluble factors with well-studied downstream signaling mechanisms, including signal transducer and activator of transcription 3 (STAT3) and hairy and enhancer of split 3 (Hes3), shed new light into the molecular mechanisms that might be involved. We examine the appropriateness of common culture systems and their ability to reveal unusual (noncanonical) signal transduction pathways that actually operate in vivo. We then discuss such novel pathways and their importance in various plastic cell types, culminating in their emerging roles in reprogramming mechanisms. We also discuss a number of reprogramming paradigms (mouse induced pluripotent stem cells, direct conversion to neural stem cells, and in vivo conversion of acinar cells to β-like cells). Specifically for acinar-to-β-cell reprogramming paradigms, we discuss the common view of the underlying mechanism (involving the Janus kinase-STAT pathway that leads to STAT3-tyrosine phosphorylation) and present alternative interpretations that implicate STAT3-serine phosphorylation alone or serine and tyrosine phosphorylation occurring in sequential order. The implications for drug design and therapy are important given that different phosphorylation sites on STAT3 intercept different signaling pathways. We introduce a new molecular perspective in the field of reprogramming with broad implications in basic, biotechnological, and translational research. Significance Reprogramming is a powerful approach to change cell identity, with implications in both basic and applied biology. Most efforts involve the forced expression of key transcription factors, but recently, success has been reported with manipulating signal transduction pathways that might intercept them. It is important to start connecting the function of the classic reprogramming genes to signaling pathways that also mediate reprogramming, unifying the sciences of signal transduction, stem cell biology, and epigenetics. Neural stem cell studies have revealed the operation of noncanonical signaling pathways that are now appreciated to also operate during reprogramming, offering new mechanistic explanations.

Ankle injuries are very common between professional athletes and recreational sports. Lateral stable ligaments injury can be treated conservatively. Noninvasive interactive neurostimulation (NIN) is a form of electric therapy that works by locating areas of lower skin impedance. The objective of this prospective, double-blinded, randomized controlled trial was to compare the results in terms of improvement of a foot functional score, lower level of reported pain, and return to sports in 2 groups of contact sport athlete affected by a grade I or II lateral ankle sprain. Patients were randomized using random blocks to the NIN program (group I) or a sham device (group II). The outcome measurements were the use of a self-reported Inability Walking Scale, patient-reported subjective assessment of the level of pain using a standard visual analogue scale, and daily intake of nonsteroidal antiinflammatory drugs (etoricoxib 60 mg). Patients were also reached by telephone at 2 and 4 months of follow-up to register their return to sport activity. Beyond baseline evaluation, follow-ups were done after 5 (1 week) and 10 sessions (2 weeks) of treatment, and then at 30 days after the end of therapy. Of the 70 athletes admitted to the study, 61 eligible patients were randomized using random blocks to group I (n = 32) and group II (n = 29). Group I patients showed better improvement in terms of functional impairment (Inability Walking Scale), reported pain (visual analogue scale), and daily intake of etoricoxib 60 mg. Athletes of group I registered a faster resuming of sport activities. This prospective, randomized trial showed NIN can improve short-term outcomes in athletes with acute grade I or II ankle sprain and that it can hasten resuming of sport activities.

Summary: GWAS have identified 108 schizophrenia risk loci, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. Across the genome, we found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls which replicated in an independent dataset, implicated synaptic processes and were strongly regulated in early development. These findings together offer genetic- and diagnosis-related targets for better modeling schizophrenia risk. This publicly-available resource is available at: http://eqtl.brainseq.org/phase1

Genetic and genomic studies suggest an important role for transcriptional regulatory changes in brain diseases, but roles for specific transcription factors (TFs) remain poorly understood. We integrated human brain-specific DNase I footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain, predicting the brain-specific binding sites and target genes for 741 TFs. We used this model to predict core TFs involved in psychiatric and neurodegenerative diseases. Our results suggest that disease-related transcriptomic and genetic changes converge on small sets of disease-specific regulators, with distinct networks underlying neurodegenerative vs. psychiatric diseases. Core TFs were frequently implicated in a disease through multiple mechanisms, including differential expression of their target genes, disruption of their binding sites by disease-associated SNPs, and associations of the genetic loci encoding these TFs with disease risk. We validated our model’s predictions through systematic comparison to publicly available ChIP-seq and TF perturbation studies and through experimental studies in primary human neural stem cells. Combined genetic and transcriptional evidence supports roles for neuronal and microglia-enriched, MEF2C-regulated networks in Alzheimer’s disease; an oligodendrocyte-enriched, SREBF1-regulated network in schizophrenia; and a neural stem cell and astrocyte-enriched, POU3F2-regulated network in bipolar disorder. We provide our models of brain-specific TF binding sites and target genes as a resource for network analysis of brain diseases.

Defining the environmental context in which genes enhance disease susceptibility can provide insight into the pathogenesis of complex disorders. We report that the intra-uterine environment modulates the association of schizophrenia with genomic risk (in this study, genome-wide association study–derived polygenic risk scores (PRSs)). In independent samples from the United States, Italy, and Germany, the liability of schizophrenia explained by PRS is more than five times greater in the presence of early-life complications (ELCs) compared with their absence. Patients with ELC histories have significantly higher PRS than patients without ELC histories, which is confirmed in additional samples from Germany and Japan. The gene set composed of schizophrenia loci that interact with ELCs is highly expressed in placenta, is differentially expressed in placentae from complicated in comparison with normal pregnancies, and is differentially upregulated in placentae from male compared with female offspring. Pathway analyses reveal that genes driving the PRS-ELC interaction are involved in cellular stress response; genes that do not drive such interaction implicate orthogonal biological processes (for example, synaptic function). We conclude that a subset of the most significant genetic variants associated with schizophrenia converge on a developmental trajectory sensitive to events that affect the placental response to stress, which may offer insights into sex biases and primary prevention.