Dataset Information

Single-cell genomics identifies cell type-specific molecular changes in autism.

ABSTRACT: Despite the clinical and genetic heterogeneity of autism, bulk gene expression studies show that changes in the neocortex of autism patients converge on common genes and pathways. However, direct assessment of specific cell types in the brain affected by autism has not been feasible until recently. We used single-nucleus RNA sequencing of cortical tissue from patients with autism to identify autism-associated transcriptomic changes in specific cell types. We found that synaptic signaling of upper-layer excitatory neurons and the molecular state of microglia are preferentially affected in autism. Moreover, our results show that dysregulation of specific groups of genes in cortico-cortical projection neurons correlates with clinical severity of autism. These findings suggest that molecular changes in upper-layer cortical circuits are linked to behavioral manifestations of autism.

SUBMITTER: Velmeshev D

PROVIDER: S-EPMC7678724 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Single-cell genomics identifies cell type-specific molecular changes in autism.

Velmeshev Dmitry D Velmeshev Dmitry D Schirmer Lucas L Jung Diane D Haeussler Maximilian M Perez Yonatan Y Mayer Simone S Bhaduri Aparna A Goyal Nitasha N Rowitch David H DH Kriegstein Arnold R AR

Science (New York, N.Y.) 20190501 6441

Despite the clinical and genetic heterogeneity of autism, bulk gene expression studies show that changes in the neocortex of autism patients converge on common genes and pathways. However, direct assessment of specific cell types in the brain affected by autism has not been feasible until recently. We used single-nucleus RNA sequencing of cortical tissue from patients with autism to identify autism-associated transcriptomic changes in specific cell types. We found that synaptic signaling of uppe ...[more]

PMID: 31097668

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Project description:Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the dataset. Immunohistochemistry for subgroup-specific ‘signature’ genes was used to determine subgroup affiliation for 294 non-overlapping medulloblastomas on two independent tissue microarrays (TMAs). Multiple unsupervised analyses of transcriptional profiles identified four distinct, non-overlapping molecular variants: WNT, SHH, Group C, and Group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) could reliably and uniquely classify formalin fixed medulloblastomas in ~98% of cases. Group C patients (NPR3 +ve tumors) exhibited a significantly diminished progression free and overall survival irrespective of their metastatic status. Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma sub-classification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma. A total of 103 primary medulloblastoma specimens were profiled by Affymetrix exon array and gene-level analysis was performed.

Dataset Information

Single-cell genomics identifies cell type-specific molecular changes in autism.

Publications

Single-cell genomics identifies cell type-specific molecular changes in autism.

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