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Image Search Results
Journal: Genome Biology
Article Title: Asymmetric gene expression and cell-type-specific regulatory networks in the root of bread wheat revealed by single-cell multiomics analysis
doi: 10.1186/s13059-023-02908-x
Figure Lengend Snippet: Cell-type-specific regulatory networks and key regulators for root hair differentiation. a Mapping the information of chromatin openness revealed by snATAC-seq data into the cell clusters classified by snRNA-seq. b UMAP visualization of 10 cell clusters were annotated by both snRNA-seq and snATAC-seq for wheat root. Each dot represents a single cell. c High correlation between chromatin accessibility and expression of marker genes for each corresponding clusters. Left part is the open chromatin scores calculated based on snATAC-seq, Right part is the expression level calculated based on snRNA-seq. d Top5 representative TF regulons for each cluster identified by SCENIC4. The abbreviated names of TF regulons were followed with the chromosome name to indicate their location in subgenomes. e Root transections of taspl14 knock-out line and wild type. The protoxylem pores and companion cells were marked with yellow arrow and pink arrow head, respectively. Bar is 100 μm. f taspl14 knock-out line showed reduced companion cells and increased protoxylem. g BAM1 and LOB were downregulated in root of taspl14 knock-out lines. h The accessible chromatin regions (ACRs) of BAM1 homolog ( TraesCS4D02G235800 ). i Differentiated trajectories of root hair. Colors of dots are corresponding to cell clusters. Start indicates the initiation of the pseudo-time trajectory. Terminal indicates the end of the pseudo-time trajectory. j Trajectory network for root hair differentiation identified the key regulators for cell identity transition
Article Snippet: The
Techniques: Expressing, Marker, Knock-Out
Journal: eBioMedicine
Article Title: Genomic profiling and associated B cell lineages delineate the efficacy of neoadjuvant anti-PD-1-based therapy in oesophageal squamous cell carcinoma
doi: 10.1016/j.ebiom.2024.104971
Figure Lengend Snippet: Immune infiltration heterogeneity of ESCC with distinct therapeutic responses. (a) UMAP plot showing clustering for immune cells from 10 patients with ESCC (left), expression of selected genes (middle) and distribution according to different therapeutic responses (right). (b) Dot plot showing the expression of PDCD1 and CD274 in immune cell clusters from responders and non-responders. (c) Lollipop chart showing difference of proportions of immune cell clusters in patients with different MMR status (left) and therapeutic responses (right) (scales of circles indicate the –log P value; Student's t test). (d) Expression heatmap of signature genes in distinct lymphocyte clusters. (e) UMAP plot showing the subclustering for CD4 + T cells from 10 patients with ESCC (left), expression of selected genes (middle) and distribution in different therapeutic responses (right). (f) Lollipop chart showing differences of proportions of CD4 + T cell subclusters from patients with different MMR status (left) and therapeutic responses (right) (scales of circles indicate the –log P value; Student's t test). (g) UMAP plot showing the subclustering for B cells from 10 patients with ESCC (left), expression of selected genes (middle) and distribution according to different therapeutic responses (right). (h) Lollipop and column chart showing difference of proportions of B cell subclusters from patients with different MMR status (scales of circles indicate the –log P value; Student's t test; left) and therapeutic responses (Chi-square test; right). P values are indicated in the figure.
Article Snippet: Immune infiltration heterogeneity of ESCC with distinct therapeutic responses. (a) UMAP plot showing clustering for immune cells from 10 patients with ESCC (left), expression of selected genes (middle) and distribution according to different therapeutic responses (right). (b) Dot plot showing the expression of PDCD1 and CD274 in immune cell clusters from responders and non-responders. (c) Lollipop chart showing difference of proportions of immune cell clusters in patients with different MMR status (left) and therapeutic responses (right) (scales of circles indicate the –log P value; Student's t test). (d) Expression heatmap of signature genes in distinct lymphocyte clusters. (e) UMAP plot showing the subclustering for CD4 + T cells from 10 patients with ESCC (left), expression of selected genes (middle) and distribution in different therapeutic responses (right). (f) Lollipop chart showing differences of proportions of CD4 + T cell subclusters from patients with different MMR status (left) and therapeutic responses (right) (scales of circles indicate the –log P value; Student's t test). (g)
Techniques: Expressing
Table 1 ) were normalized against the mean of the healthy control taken along within the same experiment and converted to percentages. Statistics were calculated using Welsch’s t test. The black line indicates the median value, the lower and upper hinges correspond to the 25th and 75th percentiles. The upper and lower whisker extend to 1.5∗IQR. (C) Showing the UMAP plots from Journal: iScience
Article Title: Imaging flow cytometry reveals divergent mitochondrial phenotypes in mitochondrial disease patients
doi: 10.1016/j.isci.2024.111496
Figure Lengend Snippet: Correlation of IFC features with phenotypic features (A) Correlation plot showing the correlation between IFC features and clinical phenotypes. All clinical features were converted to binary parameters (Yes = Present, No=Not present) and correlation was calculated using Spearman’s correlation. The color coding and dot size correlate with Spearman’s rho coefficient. (B) All significant correlations have been plotted as a boxplot. For the boxplots, the values of each of the five features (
Article Snippet: All graphs and
Techniques: Control, Whisker Assay