Cell-Type-Specific Analysis of Molecular Pathology in Autism Identifies Common Genes and Pathways Affected Across Neocortical Regions

Velmeshev, Dmitry; Magistri, Marco; Mazza, Emilia Maria Cristina; Lally, Patrick; Khoury, Nathalie; D’Elia, Evan Ross; Bicciato, Silvio; Faghihi, Mohammad Ali

doi:10.1007/s12035-020-01879-5

Cited by 24 publications

(27 citation statements)

References 33 publications

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“…Studies of single-nucleus RNA sequencing in cortical tissues of patients with ASD have found that malfunctioning genes expressed in specific cell types of the neocortex are ideal candidates for the development of ASD-targeted therapies [10]. In a recent study [11], researchers attempted to identify the cell-type-specific changes of genes shared across cortical areas of ASD patients. They found that in three groups of ASD patients, the same 30 genes were upregulated in four cortical regions.…”

Section: Introductionmentioning

confidence: 99%

“…These genes are expressed in adult microglia, astrocytes, and brain endothelial cells. The results showed that activation of astrocytes, microglia, and endothelial cells in the neocortex is a common feature of ASD pathology across multiple cortical regions, and astrocytes and microglia have been shown to regulate the formation and pruning of synapses during development [11]. Although the studies [9,11] have pointed out the cell specificity in the pathogenic factors of ASD, only the relationship between a single gene and ASD has been analyzed in the previous research process.…”

Section: Introductionmentioning

confidence: 99%

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Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis

et al. 2021

Biomedicines

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Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a network module. The identification of these modules is the basis for the systematic understanding of the pathogenesis of ASD. Moreover, ASD is characterized by highly pathogenic heterogeneity, and gene modules associated with ASD are cell-type-specific. In this study, based on the single-nucleus RNA sequencing data of 41 post-mortem tissue samples from the prefrontal cortex and anterior cingulate cortex of 19 ASD patients and 16 control individuals, we applied sparse module activity factorization, a matrix decomposition method consistent with the multi-factor and heterogeneous characteristics of ASD pathogenesis, to identify cell-type-specific gene modules. Then, statistical procedures were performed to detect highly reproducible cell-type-specific ASD-associated gene modules. Through the enrichment analysis of cell markers, 31 cell-type-specific gene modules related to ASD were further screened out. These 31 gene modules are all enriched with curated ASD risk genes. Finally, we utilized the expression patterns of these cell-type-specific ASD-associated gene modules to build predictive models for ASD. The excellent predictive performance also proved the associations between these gene modules and ASD. Our study confirmed the multifactorial and cell-type-specific characteristics of ASD pathogeneses. The results showed that excitatory neurons such as L2/3, L4, and L5/6-CC play essential roles in ASD’s pathogenic processes. We identified the potential ASD target genes that act together in cell-type-specific modules, such as NRG3, KCNIP4, BAI3, PTPRD, LRRTM4, and LINGO2 in the L2/3 gene modules. Our study offers new potential genomic targets for ASD and provides a novel method to study gene modules involved in the pathogenesis of ASD.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis

et al. 2021

Biomedicines

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“…Whilst much progress has recently been made characterizing the convergent transcriptomic mechanisms and signatures which are characteristic of ASDs at the bulk RNA-seq level [ 80 , 81 ], these studies have been limited in their ability to determine the precise cell types affected with high resolution. In an attempt to address this, snRNA-seq of the healthy adult neocortex has recently been used to identify those cell-types that are enriched in the differential gene expression and alternative splicing programmes that are consistently detected by bulk RNA-seq of ASD tissue [ 55 , 82 ]. Specifically, snRNA-seq identified endothelial cells, microglia and astrocytes to be most highly enriched for a set of 27 genes related to immune system response that are consistently differentially expressed across four cortical (frontal and temporal) regions in bulk RNA-seq.…”

Section: Lessons Learnt From Single-cell Transcriptomics In the Mammalian Cnsmentioning

confidence: 99%

“…Applying the similar methodology to that used in aforementioned ASD studies [ 55 , 82 , 84 , 86 ] these snRNA-seq datasets of human AD tissue have also facilitated important interrogation of cell-subtype specific expression patterns of ~1000 AD-associated GWAS genes [ 112 ]. This unexpectedly revealed that many AD-associated genes display specific expression in microglia to further emphasize the central role of these cells in AD (e.g.…”

Section: Lessons Learnt From Single-cell Transcriptomics In the Mammalian Cnsmentioning

confidence: 99%

Elucidating the cellular dynamics of the brain with single-cell RNA sequencing

et al. 2021

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Single-cell RNA-sequencing (scRNA-seq) has emerged in recent years as a breakthrough technology to understand RNA metabolism at cellular resolution. In addition to allowing new cell types and states to be identified, scRNA-seq can permit cell-type specific differential gene expression changes, pre-mRNA processing events, gene regulatory networks and single-cell developmental trajectories to be uncovered. More recently, a new wave of multi-omic adaptations and complementary spatial transcriptomics workflows have been developed that facilitate the collection of even more holistic information from individual cells. These developments have unprecedented potential to provide penetrating new insights into the basic neural cell dynamics and molecular mechanisms relevant to the nervous system in both health and disease. In this review we discuss this maturation of single-cell RNA-sequencing over the past decade, and review the different adaptations of the technology that can now be applied both at different scales and for different purposes. We conclude by highlighting how these methods have already led to many exciting discoveries across neuroscience that have furthered our cellular understanding of the neurological disease.

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“…Examples include the autism GWAS, of which the original signal is weak (no SNP passing genome-wide significance threshold) [26]. Sherlock-II identified 10 genes with FDR<0.1; four of them (ENPP4, MAPT, LEPR, MTHFR) have strong literature support [27][28][29][30] (Figure S1).…”

Section: Translate Snp-phenotype Association Profile To Gene-phenotypmentioning

confidence: 99%

Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

Zheng

et al. 2020

Preprint

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Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. The recent accumulation of GWAS data has made it possible to analyze the genetic similarity between human traits through comparative analysis. Here we developed a gene-based approach to measure genetic similarity between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data; 2) measuring the similarity between a pair of traits by a normalized distance between the two gene-phenotype association profiles; 3) delineating genes/pathways supporting the similarity. Application of this approach to a set of GWAS data covering 59 human traits detected significant similarity between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based similarity measures. Examples include Height and Schizophrenia, Cancer and Alzheimer’s Disease, and Rheumatoid Arthritis and Crohn’s disease. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Height and Schizophrenia are co-associated with genes involved in neural development, skeletal muscle regeneration, protein synthesis, magnesium homeostasis, and immune response, suggesting growth and development as a common theme underlying both traits. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses, and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.

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Cell-Type-Specific Analysis of Molecular Pathology in Autism Identifies Common Genes and Pathways Affected Across Neocortical Regions

Cited by 24 publications

References 33 publications

Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis

Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis

Elucidating the cellular dynamics of the brain with single-cell RNA sequencing

Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

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