Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics

Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This da..., Please see Manuscript for detailed information. In Brief: we generated Pseudobulk expression ASD vs CTL analysis by cell type. We generated pseudobulk counts for each sample by adding counts from the same cell type. Then pseudobulk counts are normalized by variance stabilizing transformation method. To identify genes differentially expressed in ASD compared to control in each cell type, we examined covariates with top 5 PCs from normalized pseudo-bulk expression matrix. We identified the following covariates consistently correlated with top 5PCs for each cell type: age, PMI, BrainRegion, SeqBatch, Mito_perc, and ngenes. We then randomly selected subjects 500 times and calculated average beta to regress out effects of these covariates. Then we used limma-voom to identify differentially expressed genes for each cluster., , # Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics Raw Differential Gene Expression data, Data S1, from \"Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics\" ## Description of the data and file structure Excell document with raw differential gene expression ASD vs. CTL per cell-type cluster. The first column is a drop-down selection to select which cell-type to view the differential gene expression results. The second (ASDvCTL) and third (CTLvASD) columns are the LOGFC value for each differential gene for a given cell type, the fourth (ASDvCTL) and fifth (CTLvASD) column are the p-values for each gene for a given cell type, the sixth (ASDvCTL) and seventh (CTLvASD) columns are the FDR-values for each gene for a given cell type, and the last column is the gene name. ## Sharing/Access information Links to other publicly accessible locations of t...自闭症患者死后脑组织的基因组分析始终揭示出趋同的分子变化。目前尚不明确这些变化的驱动因素，以及它们与这一复杂疾病的遗传易感性之间的关联。本研究采用深度单细胞核RNA测序（single nuclear RNA sequencing, snRNAseq）分析细胞组成与转录组特征，鉴定出自闭症中细胞类型特异性基因调控网络（gene regulatory networks, GRNs）的失调，并通过单细胞核转座酶可及性测序（snATAC-seq）与空间转录组学验证了这一结果。转录组变化主要具有细胞类型特异性，涉及多种细胞类型，其中最显著的包括半球间投射神经元与胼胝体投射神经元、浅层神经元间神经元，以及少突胶质细胞、小胶质细胞和星形胶质细胞的不同胶质反应状态。与自闭症相关的GRN驱动因子及其靶基因在罕见和常见遗传风险变异中显著富集，将自闭症的遗传易感性与人类大脑的细胞和环路改变联系起来。本数据集……详细信息请参见手稿。 研究概况：我们针对各细胞类型开展了伪批量（pseudobulk）表达的自闭症谱系障碍（Autism Spectrum Disorder, ASD）与对照组（Control, CTL）差异分析。我们通过合并同一细胞类型的计数为每个样本生成伪批量计数，随后采用方差稳定转换方法对伪批量计数进行标准化。为鉴定各细胞类型中ASD相较于CTL的差异表达基因，我们基于标准化伪批量表达矩阵的前5个主成分（PCs）分析协变量。我们发现各细胞类型的前5个PCs均与以下协变量显著相关：年龄、死后间隔时间（Post-Mortem Interval, PMI）、脑区、测序批次（SeqBatch）、线粒体百分比（Mito_perc）以及基因数（ngenes）。随后我们随机抽样受试者500次，计算平均beta值以去除这些协变量的影响，最后采用limma-voom方法鉴定每个细胞簇的差异表达基因。 # 原始差异表达基因数据集，数据集S1，来源：《单细胞基因组学揭示的自闭症分子级联反应与细胞类型特异性特征》 原始差异表达基因数据集，数据集S1，来源：《单细胞基因组学揭示的自闭症分子级联反应与细胞类型特异性特征》 ## 数据与文件结构说明 本数据集为Excel格式文件，包含各细胞类型簇的ASD与CTL原始差异表达基因数据。第一列为下拉选择框，用于选择需查看差异表达基因结果的细胞类型；第二列（ASDvCTL）与第三列（CTlvASD）为对应细胞类型下各差异基因的对数倍变化（LOGFC）值；第四列（ASDvCTL）与第五列（CTlvASD）为对应细胞类型下各基因的p值；第六列（ASDvCTL）与第七列（CTlvASD）为对应细胞类型下各基因的错误发现率（FDR）值；最后一列为基因名称。 ## 共享与获取信息 其他公开可获取来源的链接……