Journal: Nature biotechnology

Article Title: Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

doi: 10.1038/s41587-021-00911-w

Figure Lengend Snippet: (a) Genotype profiles for six example regions for cells in scATAC-seq data. The regions are taken from segmentation of matched whole exome sequencing (WES) data. Each dot represents a cell-specific (ρ^ir,θ^ir) pair. Cells are colored by annotation derived from peak signals25, Tumor: tumor cells, Fibro: fibroblasts, Endo: Endothelial cells]. Density contours are computed for each cell type (tumor, fibroblasts, endothelial) separately and shown by color on the plot. The lower-case letters following the chromosome number in the titles denote the ordered genomic segments. (b) Pipeline for multi-omics analysis integrating allele-specific copy number estimates and chromatin accessibility peak signals on ATAC-seq data. (c) Hierarchical clustering of cells by major haplotype proportion (θ^) allows the separation of tumor cells from normal cells, as well as the differentiation of a subclone within the tumor cells. The marker region on chr4a separating the two tumor subclones is highlighted. (d) Integrated visualization of chr4a major haplotype proportion (θ^ir) and genome-wide peak profile. Left: UMAP projection of the 788 cells in the dataset by their genome-wide peak profile, colored by θ^ir. The cell type annotation (endothelial, fibroblasts, and tumor cells) is labeled in the plot. Middle: UMAP projection of only the 308 tumor cells by their genome-wide peak profile shows two well-separated clusters: peaks1 and peaks2. Right: Density of θ^ir values for the peaks1 and peaks2 subpopulations. (e) Intratumor heterogeneity of SU008 is shaped by a subclonal LOH of chr4a followed by subsequent genome-wide chromatin remodeling leading to three subpopulations: Clone 1 which does not carry the chr4a LOH (peaks cluster 1), Clone 2 carrying the chr4a LOH (peaks cluster 1), and remodeled clone 2 (peaks cluster 2).

Article Snippet: Raw sequencing reads of the SNU601 scATAC-seq sample was de-multiplexed with the 10x Genomics Cell Ranger ATAC Software (v.1.2.0; https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/algorithms/overview ) and aligned to the GRCh38 reference genome.

Techniques: Sequencing, Derivative Assay, Biomarker Discovery, Marker, Genome Wide, Labeling

Journal: Cell Genomics

Article Title: Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

doi: 10.1016/j.xgen.2022.100164

Figure Lengend Snippet: Transcriptional profiles from joint scRNA- and ATAC-seq identify major cell types of the human retina (A) Schematic of the human retina, depicting the cell types analyzed in this study. (B) Uniform manifold approximation and projection (UMAP) plot of the 51,645 human retinal cells detected by scRNA-seq after quality control filtering and removal of putative doublets. Eight postmortem retinas from four donors were profiled. A total of 22 clusters were resolved and assigned to 13 cell types. (C) Frequency of different cell types in the human retina as determined by scRNA-seq. Numbers above each bar denote absolute counts out of 51,645. (D) Dot plot visualizing the normalized RNA expression of selected marker genes by cell type. The color and size of each dot correspond to the average expression level and fraction of expressing cells, respectively.

Article Snippet: Joint scRNA- and scATAC-seq libraries were prepared using the 10x Genomics Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s instructions.

Techniques: Control, RNA Expression, Marker, Expressing

Journal: Cell Genomics

Article Title: Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

doi: 10.1016/j.xgen.2022.100164

Figure Lengend Snippet: Chromatin accessibility profiles from joint scRNA- and ATAC-seq of the human retina reveal cell-type-specific epigenetic landscapes (A) Number of chromatin accessibility peaks for each cell type as determined by scATAC-seq. Peaks were required to be present in a least two pseudo-bulk ATAC replicates (n = 2 for astrocyte and microglia, n = 5 for all other cell types). (B) Overlap of scATAC peaks with peaks from published human retina bulk ATAC-seq data. Overlapping was defined as peaks with any overlapping bases. (C) Heatmap of scATAC marker peaks enriched in each cell type. Each column represents a marker peak. (D) Sequencing tracks of chromatin accessibility near selected marker genes by cell type. Each track represents the aggregate scATAC signal of all cells from the given cell type normalized by the total number of reads in TSS regions. Genes in the sense direction (TSS on the left) are shown in red, and genes in the antisense direction (TSS on the right) are shown in blue. Coordinates for each region are as follows: PDE6A (chr5:149924792–149964793), GRIK1 (chr21:29905031−29955033), RLBP1 (chr15:89201750−89241751), GRM6 (chr5:178975297−179015298), NIF3L1 (chr2:200874325−200914327), ARR3 (chrX:70248304−70288305), GAD2 (chr10:26186306−26246307), ONECUT1 (chr15:52781076−52821078), SLC6A9 (chr1:44005465−44035467), NEFL (chr8:24937109−24977110), CALB2 (chr16:71323711−71368713), PAX2 (chr10:100715602−100755603), and HLA-DRA (chr6:32419841−32459842).

Article Snippet: Joint scRNA- and scATAC-seq libraries were prepared using the 10x Genomics Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s instructions.

Techniques: Marker, Sequencing

Journal: Cell Genomics

Article Title: Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

doi: 10.1016/j.xgen.2022.100164

Figure Lengend Snippet: Integration of the single-cell multiome with HiChIP and eQTL data prioritizes functional noncoding polymorphisms in the human retina (A) Overlap of H3K27ac HiChIP loop anchors (n = 2 biological replicates) with scATAC peaks. (B) Percentage of SNPs in scATAC peaks for each disease with available retina eQTL data. (C) Sequencing tracks of chromatin accessibility near rs9966620 (chr18:24100771), rs2730260 (chr7:159054238), and rs66475830 (chr6:116087639). Genes in the sense and antisense directions are shown in red and blue, respectively. The location of each SNP is depicted by a vertical gray line. Gray arcs indicate predicted target genes for the scATAC peak containing the SNP of interest. The black arc overlapping with rs9966620 indicates a H3K27ac HiChIP loop with the region encompassed by the opposite anchor, highlighted in purple. (D) Significance of SNP-gene associations for rs2730260 or rs66475830 and their nearby genes, as determined by retina eQTL analysis. Adjusted p values for each gene were calculated by multiplying the nominal p value listed in the EyeGEx database by the number of SNP-gene pairs tested for that SNP.

Article Snippet: Joint scRNA- and scATAC-seq libraries were prepared using the 10x Genomics Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s instructions.

Techniques: HiChIP, Functional Assay, Sequencing

Journal: Cell Genomics

Article Title: Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

doi: 10.1016/j.xgen.2022.100164

Figure Lengend Snippet: Integration of the single-cell multiome with base-resolution deep learning nominates functional mechanisms for disease-associated SNPs (A) Schematic of the CNN-based deep learning pipeline. (B) Percentage of noncoding index SNPs (n = 1,284), LD expanded SNPs (n = 7,034), LD expanded SNPs in scATAC peaks (n = 1,152), randomly selected GC-matched SNPs (n = 9,984), and randomly selected SNPs in scATAC peaks (n = 1,160) that were categorized as high-effect. (C) Top: predicted per-base accessibility for rs1532278 (chr8:27608798) and rs1874459 (chr16:65041801) in Müller glia and rod bipolar cells, respectively, as determined by deep learning models. A 100-bp window depicts the importance of each base to predicted accessibility at the SNP, and a 1,000-bp window depicts predicted per-base counts for the reference (blue) and alternate (orange) alleles. SNP bases are highlighted in purple. For rs1874459, similar changes in accessibility were predicted for OFF-cone bipolar, ON-cone bipolar, gly-amacrine, and AII-amacrine cells. Bottom: sequencing tracks of chromatin accessibility near rs1532278 and rs1874459. Genes in the sense and antisense directions are shown in red and blue, respectively. The location of each SNP is depicted by a vertical gray line. Gray arcs indicate predicted target genes for the scATAC peak containing the SNP of interest. (D and F) Significance of SNP-gene associations for rs1532278 (D) or rs1874459 (F) and their nearby genes, as determined by retina eQTL analysis. Adjusted p values for each gene were calculated by multiplying the nominal p value listed in the EyeGEx database by the number of SNP-gene pairs tested for that SNP. (E) Dot plot visualizing the normalized RNA expression of 40 different homeodomain TFs in Müller glia. The selected TFs correspond to the 40 homeodomain factors whose binding motifs were most significantly enriched in Müller glia, as determined by motif analysis . (G) Dot plot visualizing the normalized RNA expression of neuroD and neurogenin family members by cell type.

Article Snippet: Joint scRNA- and scATAC-seq libraries were prepared using the 10x Genomics Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s instructions.

Techniques: Functional Assay, Sequencing, RNA Expression, Binding Assay

Journal: Cell Genomics

Article Title: Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

doi: 10.1016/j.xgen.2022.100164

Figure Lengend Snippet:

Article Snippet: Joint scRNA- and scATAC-seq libraries were prepared using the 10x Genomics Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s instructions.

Techniques: Recombinant, Protease Inhibitor, Electron Microscopy, Gene Expression, HiChIP, Software