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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.
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Epigenómica , Perfilación de la Expresión Génica , Corteza Motora/citología , Neuronas/clasificación , Análisis de la Célula Individual , Transcriptoma , Animales , Atlas como Asunto , Conjuntos de Datos como Asunto , Epigénesis Genética , Femenino , Masculino , Ratones , Corteza Motora/anatomía & histología , Neuronas/citología , Neuronas/metabolismo , Especificidad de Órganos , Reproducibilidad de los ResultadosRESUMEN
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).
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Encéfalo , Bases de Datos Genéticas , Epigenómica , Multiómica , Transcriptoma , Animales , Ratones , Genómica , Mamíferos , Primates , Encéfalo/citología , Encéfalo/metabolismoRESUMEN
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.
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Transcriptoma , Simulación por Computador , HumanosRESUMEN
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.
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Estudio de Asociación del Genoma Completo , Herencia Multifactorial , Elementos de Facilitación Genéticos/genética , Polimorfismo de Nucleótido Simple/genética , Secuencias Reguladoras de Ácidos Nucleicos , Factores de Transcripción/genética , Factores de Transcripción/metabolismoRESUMEN
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.
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Cuerpo Estriado/patología , Enfermedad de Huntington/patología , Neuronas/patología , Complejo Represivo Polycomb 2/metabolismo , Animales , Cuerpo Estriado/metabolismo , Enfermedad de Huntington/genética , Enfermedad de Huntington/metabolismo , Masculino , Ratones , Ratones Endogámicos C57BL , Mutación , Neuronas/metabolismo , RNA-SeqRESUMEN
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.
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Conectoma , Genoma , Artefactos , Mapeo Encefálico/métodos , Conjuntos de Datos como Asunto , Humanos , Reproducibilidad de los ResultadosRESUMEN
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.
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Genómica , Programas Informáticos , Análisis por Conglomerados , Aprendizaje Automático , Análisis de la Célula IndividualRESUMEN
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 (nâ¯=â¯32) and group II (nâ¯=â¯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.
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Traumatismos en Atletas/terapia , Terapia por Estimulación Eléctrica/métodos , Esguinces y Distensiones/terapia , Adolescente , Adulto , Antiinflamatorios no Esteroideos/administración & dosificación , Traumatismos en Atletas/clasificación , Método Doble Ciego , Impedancia Eléctrica , Etoricoxib/administración & dosificación , Femenino , Humanos , Masculino , Estudios Prospectivos , Volver al Deporte , Esguinces y Distensiones/clasificación , Escala Visual Analógica , Adulto JovenRESUMEN
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. CONTACT: gsteinobrien@jhmi.edu or ccolantu@jhmi.edu or ejfertig@jhmi.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Algoritmos , Perfilación de la Expresión Génica/métodos , Programas Informáticos , Teorema de Bayes , Biomarcadores , Humanos , Análisis de Secuencia de ARN/métodosRESUMEN
Previous investigations have combined transcriptional and genetic analyses in human cell lines, but few have applied these techniques to human neural tissue. To gain a global molecular perspective on the role of the human genome in cortical development, function and ageing, we explore the temporal dynamics and genetic control of transcription in human prefrontal cortex in an extensive series of post-mortem brains from fetal development through ageing. We discover a wave of gene expression changes occurring during fetal development which are reversed in early postnatal life. One half-century later in life, this pattern of reversals is mirrored in ageing and in neurodegeneration. Although we identify thousands of robust associations of individual genetic polymorphisms with gene expression, we also demonstrate that there is no association between the total extent of genetic differences between subjects and the global similarity of their transcriptional profiles. Hence, the human genome produces a consistent molecular architecture in the prefrontal cortex, despite millions of genetic differences across individuals and races. To enable further discovery, this entire data set is freely available (from Gene Expression Omnibus: accession GSE30272; and dbGaP: accession phs000417.v1.p1) and can also be interrogated via a biologist-friendly stand-alone application (http://www.libd.org/braincloud).
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Envejecimiento/genética , Perfilación de la Expresión Génica , Regulación del Desarrollo de la Expresión Génica/genética , Corteza Prefrontal/crecimiento & desarrollo , Corteza Prefrontal/metabolismo , Transcriptoma/genética , Autopsia , Feto/metabolismo , Genoma Humano/genética , Humanos , Polimorfismo de Nucleótido Simple/genética , Corteza Prefrontal/embriología , Grupos Raciales/genética , Factores de TiempoRESUMEN
BACKGROUND: Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of "batch" correction for biological discovery in two publicly available expression datasets. We consider this to include the estimation of and adjustment for wide-spread systematic heterogeneity in genomic measurements that is unrelated to the effects under study, whether it be technical or biological in nature. METHODS: We present three illustrative data analyses using surrogate variable analysis (SVA) and describe how to perform artifact discovery in light of natural heterogeneity within biological groups, secondary biological questions of interest, and non-linear treatment effects in a dataset profiling differentiating pluripotent cells (GSE32923) and another from human brain tissue (GSE30272). RESULTS: Careful specification of biological effects of interest is very important to factor-based approaches like SVA. We demonstrate greatly sharpened global and gene-specific differential expression across treatment groups in stem cell systems. Similarly, we demonstrate how to preserve major non-linear effects of age across the lifespan in the brain dataset. However, the gains in precisely defining known effects of interest come at the cost of much other information in the "cleaned" data, including sex, common copy number effects and sample or cell line-specific molecular behavior. CONCLUSIONS: Our analyses indicate that data "cleaning" can be an important component of high-throughput genomic data analysis when interrogating explicitly defined effects in the context of data affected by robust technical artifacts. However, caution should be exercised to avoid removing biological signal of interest. It is also important to note that open data exploration is not possible after such supervised "cleaning", because effects beyond those stipulated by the researcher may have been removed. With the goal of making these statistical algorithms more powerful and transparent to researchers in the biological sciences, we provide exploratory plots and accompanying R code for identifying and guiding "cleaning" process (https://github.com/andrewejaffe/StemCellSVA). The impact of these methods is significant enough that we have made newly processed data available for the brain data set at http://braincloud.jhmi.edu/plots/ and GSE30272.
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Algoritmos , Encéfalo/metabolismo , Biología Computacional/métodos , Genoma Humano , Genómica/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Células Madre Pluripotentes/metabolismo , Artefactos , Diferenciación Celular , Perfilación de la Expresión Génica , Humanos , Células Madre Pluripotentes/citología , Análisis de RegresiónRESUMEN
The human prefrontal cortex (PFC), a mastermind of the brain, is one of the last brain regions to mature. To investigate the role of epigenetics in the development of PFC, we examined DNA methylation in â¼14,500 genes at â¼27,000 CpG loci focused on 5' promoter regions in 108 subjects range in age from fetal to elderly. DNA methylation in the PFC shows unique temporal patterns across life. The fastest changes occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. At the genome level, the transition from fetal to postnatal life is typified by a reversal of direction, from demethylation prenatally to increased methylation postnatally. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes, including genes involved in brain development and in de novo DNA methylation. Our results indicate that promoter DNA methylation in the human PFC is a highly dynamic process modified by genetic variance and regulating gene transcription. Additional discovery is made possible with a stand-alone application, BrainCloudMethyl.
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Envejecimiento/genética , Metilación de ADN , Corteza Prefrontal/crecimiento & desarrollo , Corteza Prefrontal/metabolismo , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Islas de CpG/genética , Epigénesis Genética , Femenino , Feto/metabolismo , Variación Genética , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Corteza Prefrontal/embriología , Regiones Promotoras Genéticas , Sitios de Carácter Cuantitativo , Factores Sexuales , Adulto JovenRESUMEN
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.
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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.
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Neuroglía , Neuronas , Síndrome de Inmunodeficiencia Adquirida del Simio , Médula Espinal , Animales , Síndrome de Inmunodeficiencia Adquirida del Simio/metabolismo , Síndrome de Inmunodeficiencia Adquirida del Simio/genética , Síndrome de Inmunodeficiencia Adquirida del Simio/tratamiento farmacológico , Médula Espinal/metabolismo , Médula Espinal/efectos de los fármacos , Médula Espinal/virología , Neuroglía/metabolismo , Neuroglía/efectos de los fármacos , Neuroglía/virología , Neuronas/metabolismo , Neuronas/efectos de los fármacos , Neuronas/virología , Antirretrovirales/uso terapéutico , Antirretrovirales/farmacología , Virus de la Inmunodeficiencia de los Simios/efectos de los fármacos , Macaca mulatta , Expresión Génica/efectos de los fármacos , Masculino , Regulación de la Expresión Génica/efectos de los fármacosRESUMEN
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).
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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.
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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.
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COVID-19 , Fenotipo , Sepsis , Transcriptoma , Humanos , Sepsis/genética , Sepsis/sangre , Sepsis/inmunología , COVID-19/inmunología , COVID-19/genética , COVID-19/sangre , COVID-19/virología , Ghana/epidemiología , Masculino , Estudios de Cohortes , SARS-CoV-2/inmunología , SARS-CoV-2/genética , Femenino , Adulto , Persona de Mediana Edad , Perfilación de la Expresión Génica , Análisis de Secuencia de ARNRESUMEN
Schizophrenia is a common neuropsychiatric disorder that has a strong genetic component. MicroRNAs (miRNAs) have been implicated in neurodevelopmental and psychiatric disorders including schizophrenia, as indicated by their dysregulation in post-mortem brain tissues and in peripheral blood of schizophrenia patients. The olfactory epithelium (OE) is one of the few accessible neural tissues that contain neurons and their stem cells. Previous studies showed that OE-derived tissues and cells can be safely and easily collected from live human subjects and may provide a "window" into neuronal processes involved in disorders such as schizophrenia, while avoiding the limitations of using postmortem brain samples or non-neuronal tissues. In this study, we found that the brain-enriched miR-382 (miR-382-5p) expression was elevated in in vitro cultured olfactory cells, in a cohort of seven schizophrenia patients compared with seven non-schizophrenic controls. MiR-382 elevation was further confirmed in laser-capture microdissected OE neuronal tissue (LCM-OE), enriched for mature olfactory neurons, in a cohort of 18 schizophrenia patients and 18 non-schizophrenic controls. In sharp contrast, miR-382 expression could not be detected in lymphoblastoid cell lines generated from schizophrenic or non-schizophrenic individuals. We further found that miR-382 directly regulates the expression of two genes, FGFR1 and SPRY4, which are downregulated in both the cultured olfactory cells and LCM-OE derived from schizophrenia patients. These genes are involved in the fibroblast growth factor (FGF) signaling pathway, while impairment of this pathway may underlie abnormal brain development and function associated with schizophrenia. Our data suggest that miR-382 elevation detected in patients' OE-derived samples might serve to strengthen current biomarker studies in schizophrenia. This study also illustrates the potential utility of OE-derived tissues and cells as surrogate samples for the brain.
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MicroARNs/metabolismo , Neuronas/metabolismo , Mucosa Olfatoria/patología , Esquizofrenia/patología , Adolescente , Adulto , Células Cultivadas , Femenino , Factores de Crecimiento de Fibroblastos/metabolismo , Humanos , Péptidos y Proteínas de Señalización Intracelular/genética , Péptidos y Proteínas de Señalización Intracelular/metabolismo , Captura por Microdisección con Láser , Masculino , MicroARNs/genética , Análisis por Micromatrices , Persona de Mediana Edad , Proteínas del Tejido Nervioso/genética , Proteínas del Tejido Nervioso/metabolismo , Cambios Post Mortem , Transducción de Señal/genética , Transfección , Adulto JovenRESUMEN
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].