Journal: PLOS ONE

Article Title: High throughput isolation of RNA from single-cells within an intact tissue for spatial and temporal sequencing a reality

doi: 10.1371/journal.pone.0289279

Figure Lengend Snippet: Bioanalyzer profiles of RNAseq libraries from as-made single-cell RNAseq libraries without purifying primers, adaptors, and dimers (Fig 3A & 3B). Fig 3C shows purified and pooled samples prior to cDNA sequencing.

Article Snippet: After cDNA synthesis, each sample went through NGS library preparation with the NVIGEN single-cell RNAseq library kit (Cat# K81005).

Techniques: Purification, Sequencing

Journal: Nucleic Acids Research

Article Title: A comprehensive analysis framework for evaluating commercial single-cell RNA sequencing technologies

doi: 10.1093/nar/gkae1186

Figure Lengend Snippet: Table of kit descriptions and requirements

Article Snippet: ASTERIA Single-cell RNASeq Kit (Scipio Bioscience) , Scipio , Hydrogel , Reverse Transcription , 3-prime end , no , [28, 75, 6, 0] , 15, 000 , $4, 240 , 4 , $1, 060.

Techniques: Sequencing, Emulsion, Reverse Transcription, Hybridization

Journal: Nature Cancer

Article Title: Two distinct epithelial-to-mesenchymal transition programs control invasion and inflammation in segregated tumor cell populations

doi: 10.1038/s43018-024-00839-5

Figure Lengend Snippet: a , Experimental design. Three single-cell RNAseq libraries were generated from one control (n = 1 SHAM operated mouse) and two obstructed kidneys (n = 2 UUO mice) obtained from 3 male mice. b , Violin plots showing gene number (detected genes), unique transcript counts and percentage of mitochondrial counts for the different 10xGenomics-based libraries. We applied filtering to remove putative cell doublets and to include only cells having number of detected genes in the range of 400–4000. 25424 cells passed this filter and were subjected to subsequent analysis, showing a mitochondrial proportion below 10% to include metabolically highly active tubular renal cells. c , Heatmap showing top 20 discriminative DEGs for the 26 clusters. d , UMAP plot showing the distribution of the 26 clusters and their assigned identities. e , Dot plot showing the proportion and expression levels of markers genes that identify different cell types as in (d). Markers (y-axis), cell types (x-axis). See Supplementary Table for full gene names. UMAP and dot plot in (d) and (e) were generated using n = 25424 cells. f , Cell populations changes after unilateral ureteral obstruction. Compositional data analysis (CoDA) (see ) was used to assess the statistical relevance of changes in cell populations taking the glomerulus as a reference. Positive and negative CoDA loadings (x-axis) correspond respectively to the increases and decreases of a cell population in UUO compared to SHAM. Cell populations as in Fig. . The boxplots depict the uncertainty of the loading coefficients obtained by resampling with 1000 bootstrapping. The uncertainty of the loading coefficients obtained by resampling with 1000 bootstrapping was represented using boxplot, where the boxes are IQRs split by the median (middle line) and the whiskers represent minimum and maximum loading coefficients. Corrected P values were determined using Benjamini Hochberg procedure (for more details, see ‘Compositional analysis for kidney cell populations’ in ). The red horizontal line separates cell types passing significance threshold.

Article Snippet: Three single-cell RNAseq libraries were generated from one control (n = 1 SHAM operated mouse) and two obstructed kidneys (n = 2 UUO mice) obtained from 3 male mice. b , Violin plots showing gene number (detected genes), unique transcript counts and percentage of mitochondrial counts for the different 10xGenomics-based libraries.

Techniques: Generated, Control, Metabolic Labelling, Expressing

Journal: Nature Cancer

Article Title: Two distinct epithelial-to-mesenchymal transition programs control invasion and inflammation in segregated tumor cell populations

doi: 10.1038/s43018-024-00839-5

Figure Lengend Snippet: a , Experimental design used to prepare single-cell barcoded cDNA libraries. Four single-cell RNAseq libraries (T1-T4) were generated from n = 4 independent samples obtained from three 12–14 weeks old female mice. Right panel shows the 3D reconstitution of one representative whole-mounted left lung lobe showing tdTomato-positive metastatic foci. Scale bars, 2 mm. b , Violin plots showing gene number (detected genes), unique transcript counts and percentage of mitochondrial counts for the different 10xGenomics-based libraries of the four PyMT primary tumor samples. We removed putative cell doublets and applied stringent filtering to include only cells having number of detected genes in the range of 400–4000. The majority of cells (n = 36091/36162) passed this filter and were subjected to subsequent analysis, showing a mitochondrial proportion below 2%, indicative of high-quality104. c , Heatmap showing discriminative genes of the five main PyMT tumour populations (see Fig. ). d , Dot-plot showing the expression levels for genes that identify the major cell types in the tumours. Symbols of cell types (y-axis) as shown in (c). e , UMAP visualization of cells expressing different markers for tumour cells (CC, tdTomato), myeloid cells (MC, Cd74), cancer-associated fibroblasts (CAF, Col3a1), endothelial cells (EC, Cdh5) and lymphoid cells (LC, Cd3g). See Supplementary Table for all full gene names. Data in (c), (d) and (c) were generated using n = 36162 tumour cells.

Article Snippet: Three single-cell RNAseq libraries were generated from one control (n = 1 SHAM operated mouse) and two obstructed kidneys (n = 2 UUO mice) obtained from 3 male mice. b , Violin plots showing gene number (detected genes), unique transcript counts and percentage of mitochondrial counts for the different 10xGenomics-based libraries.

Techniques: Generated, Expressing

Journal: Nature Cancer

Article Title: Two distinct epithelial-to-mesenchymal transition programs control invasion and inflammation in segregated tumor cell populations

doi: 10.1038/s43018-024-00839-5

Figure Lengend Snippet: a , UMAP visualization of cancer cells showing expression of the tdTomato reporter. b , UMAP plots and table depicting the distribution of cancer cell subclusters in each single-cell RNAseq data set derived from the 4 independent tumour samples. c , Expression of luminal (blue) and basal/myoepithelial (red) cancer cell lineage markers on the UMAP gene expression plot. d-f , Distribution of expression of epithelial (d), mesenchymal markers (e), and of EMT-TFs (f) in cancer cells on the UMAP plot. Markov affinity-based graph imputation of cells (MAGIC) was applied to improve EMT-Tfs representation over UMAP. See Supplementary Table for full gene names. All UMAPs were generated using n = 19001 cancer cells.

Article Snippet: Three single-cell RNAseq libraries were generated from one control (n = 1 SHAM operated mouse) and two obstructed kidneys (n = 2 UUO mice) obtained from 3 male mice. b , Violin plots showing gene number (detected genes), unique transcript counts and percentage of mitochondrial counts for the different 10xGenomics-based libraries.

Techniques: Expressing, Derivative Assay, Gene Expression, Generated