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      Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells

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          Abstract

          Background

          In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of ‘brain to behaviour’ pathogenic mechanisms, remains largely unknown.

          Methods

          We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases).

          Results

          We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes ( p < 5.23 × 10 –11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10 –11, oligodendrocyte, p < 1.31 × 10 –09).

          Conclusion

          Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40246-021-00368-7.

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          Most cited references81

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Analysis of protein-coding genetic variation in 60,706 humans

              Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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                Author and article information

                Contributors
                mohammed.uddin@mbru.ac.ae
                Journal
                Hum Genomics
                Hum Genomics
                Human Genomics
                BioMed Central (London )
                1473-9542
                1479-7364
                21 November 2021
                21 November 2021
                2021
                : 15
                : 68
                Affiliations
                [1 ]GRID grid.510259.a, ISNI 0000 0004 5950 6858, College of Medicine, , Mohammed Bin Rashid University of Medicine and Health Sciences, ; Dubai, UAE
                [2 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Biomedical Engineering Department, , McGill University, ; Montréal, QC Canada
                [3 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, The Centre for Applied Genomics (TCAG), , The Hospital for Sick Children, ; Toronto, ON Canada
                [4 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, Genetics and Genome Biology, , The Hospital for Sick Children, ; Toronto, ON Canada
                [5 ]GRID grid.510259.a, ISNI 0000 0004 5950 6858, Mohammed Bin Rashid University of Medicine and Health Sciences, ; Dubai, UAE
                [6 ]The Mental Health Center of Excellence, Al Jalila Children’s Speciality Hospital, Dubai, UAE
                [7 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Molecular Genetics, , University of Toronto, ; Toronto, ON Canada
                [8 ]GRID grid.1006.7, ISNI 0000 0001 0462 7212, Biosciences Institute, Newcastle University, ; Newcastle upon Tyne, UK
                [9 ]Cellular Intelligence (Ci) Lab, GenomeArc Inc., Toronto, ON Canada
                Article
                368
                10.1186/s40246-021-00368-7
                8607722
                34802461
                79cf7f74-5a9a-48c2-a1a3-89854c022e2c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 July 2021
                : 5 November 2021
                Funding
                Funded by: sandooq al watan
                Award ID: SWARD-2019
                Award Recipient :
                Categories
                Primary Research
                Custom metadata
                © The Author(s) 2021

                Genetics
                single-cell transcriptomics,autism spectrum disorder,de novo lof variant,glial cell type,brain tissue

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