Journal: Cell

Article Title: Origins and Proliferative States of Human Oligodendrocyte Precursor Cells

doi: 10.1016/j.cell.2020.06.027

Figure Lengend Snippet: (A) Schematic of the workflow. Developing human cortex was dissociated and OPCs enriched by PDGFRA immunopanning. Single cells were isolated using the Fluidigim C1 microfluidic chip system, and pair-end single-cell RNA sequencing (scRNA-seq) was performed. Clustering was used to annotate cell types according to RNA profiles.

Article Snippet: Single-cell RNA-sequencing For Fluidigm C1 scRNA-seq, cDNA synthesis and pre-amplification were performed as described before ( Nowakowski et al., 2017 ) using Fluidigm C1 auto-prep system following manufacturer’s protocol.

Techniques: Isolation, RNA Sequencing Assay

Journal: Cell

Article Title: Origins and Proliferative States of Human Oligodendrocyte Precursor Cells

doi: 10.1016/j.cell.2020.06.027

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Single-cell RNA-sequencing For Fluidigm C1 scRNA-seq, cDNA synthesis and pre-amplification were performed as described before ( Nowakowski et al., 2017 ) using Fluidigm C1 auto-prep system following manufacturer’s protocol.

Techniques: Plasmid Preparation, Recombinant, Sample Prep

Journal: Nature Reviews. Cardiology

Article Title: Immune cell profiling in atherosclerosis: role in research and precision medicine

doi: 10.1038/s41569-021-00589-2

Figure Lengend Snippet: Single-cell studies of human and mouse atherosclerotic plaques

Article Snippet: Normal aorta and atherosclerotic aorta from Apoe −/− mice , Vascular smooth muscle cells , scRNA-seq (Fluidigm C1, Smart-Seq2, 10× Genomics) , .

Techniques: Injection, Western Blot

Journal: Developmental biology

Article Title: Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf

doi: 10.1016/j.ydbio.2017.11.006

Figure Lengend Snippet: Experimental design included dissociation of E14.5 mouse kidney cells, scRNA-Seq data generation with Chromium 10X Genomics, Drop-Seq and Fluidigm HT 800 cell IFC platforms, unsupervised bioinformatics classification of the resulting scRNA-Seq profiles, supervised harmonization of the three datasets and downstream cell-population level analyses.

Article Snippet: In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney.

Techniques:

Journal: Developmental biology

Article Title: Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf

doi: 10.1016/j.ydbio.2017.11.006

Figure Lengend Snippet: A) De novo identified cell populations from the software ICGS are shown for each scRNA-Seq platform. The displayed heatmaps were produced by the MarkerFinder algorithm, downstream of the ICGS population predictions, with yellow indicating high relative gene expression and blue or black, low or no gene expression in the associated genes (rows). Prior established embryonic kidney marker genes corresponding to compartments are shown in panel C. Text to the left of each heatmap indicates the statistical enrichment of genes from the Drop-Seq ICGS analysis for the 16 identified populations (MarkerFinder) using the embedded gene-set enrichment analysis tool GO-Elite in AltAnalyze. B–C) t-SNE plot derived from the ICGS heatmaps in panel A, where each dot represents individual cells colored according to its B) ICGS cluster annotation or C) prior established population specific genes. CD: Collecting duct, UT: Ureteric Tip, LOH: Loop of Henle, RV: Renal vesicle, DCSB: Distal comma shaped body, Pod: podocyte, PT: Proximal Tubule, PA: Pre-tubular aggregate, CM: Cap mesenchyme, Endo: Endothelium, NZS: Nephrogenic Stroma, CS: Cortical Stroma.

Article Snippet: In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney.

Techniques: Software, Produced, Expressing, Marker, Derivative Assay

Journal: Developmental biology

Article Title: Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf

doi: 10.1016/j.ydbio.2017.11.006

Figure Lengend Snippet: A) ICGS delineated cellular heterogeneity in the top ~2,000 DropSeq captured barcodes, segregated into 16 populations. B–C) Supervised classification of Fluidigm-800 chip captured libraries and 10X Genomics Chromium In-Drop barcodes using B) K-nearest neighbor (knn) classification against the Drop-Seq population centroids or C) MarkerFinder classified cells (rather than genes) for the top 862 population-specific genes from the Drop-Seq analysis. The upper panel displays the 862 Drop-Seq population specific genes and lower panel the top de novo MarkerFinder-gene results obtained following cell-population assignment. D) Comparison of population-specific genes jointly identified by all three scRNA-Seq technological platforms from the knn or MarkerFinder analysis. E) Number of genes jointly identified population-specific genes for each individual population for the two classification approaches. DCSB: Distal comma shaped body.

Article Snippet: In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney.

Techniques:

Journal: Developmental biology

Article Title: Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf

doi: 10.1016/j.ydbio.2017.11.006

Figure Lengend Snippet: A) Cell-population annotation predictions assigned from literature and GUDMAP gene-set annotations. Corresponding kidney compartments are colored according to the ICGS cell cluster colors. B) t-SNE analysis of the harmonized knn classified cell states using the de novo identified MarkerFinder genes for each scRNA-Seq platform. The number of cells present in the plot are indicated. C) Gene expression bar chart (log2) for prior annotated kidney developmental marker genes from the knn classified datasets. D) Comparison of population-specific genes consistently identified by two or more scRNA-Seq platforms or that are specific to a single platform by MarkerFinder-gene analysis.

Article Snippet: In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney.

Techniques: Expressing, Marker

Journal: Developmental biology

Article Title: Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf

doi: 10.1016/j.ydbio.2017.11.006

Figure Lengend Snippet: Heatmaps using data from all three scRNA-seq platforms show Gdnf expression by stromal cells. Six2, Cited1 and Crym are markers of cap mesenchyme (CM), while Meis1, Foxd1, Crabp1 and Aldh1a2 are expressed in stroma. As expected, cells that clustered with cap mesenchyme as determined by these markers as well as complete gene expression signatures often showed expression of Gdnf. Surprisingly, many cells that strongly clustered with the stromal cell compartment also showed robust Gdnf expression.

Article Snippet: In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney.

Techniques: Expressing

Journal: Science (New York, N.Y.)

Article Title: Single-cell transcriptomics uncovers molecular funneling of cell identities during axolotl limb regeneration

doi: 10.1126/science.aaq0681

Figure Lengend Snippet: (A) Schematic of CT scRNA-seq experiments. ScRNA-seq was performed on FACS sorted mCherry+ CT cells of the uninjured axolotl upper arm (0 days post amputation, dpa) and during regeneration of the upper arm blastema at 3 dpa, 5 dpa, 8 dpa, 11 dpa and 18 dpa using Prrx1:Cre-ER;Caggs:Lp-Cherry animals (conversion at 1 cm size). (B) Cellular heterogeneity of the uninjured upper arm CT based on 2375 single cell transcriptomes. tSNE projection reveals 8 clusters referring to 7 CT subtypes. The 8th cluster contains cycling cells marked by expression of Ccnb1 shown as an inset. fCT: fibroblastic connective tissue. (C) Violin plots showing distribution of expression for selected tSNE cluster marker genes (panel B). The cluster of cycling cells was excluded. Colors refer to cluster colors of tSNE map (panel B). (D) Diffusion map projection (16) describes lineage relationships between uninjured CT cells and cells from all blastema time points as well as cells from a fully regenerated upper arm. DC: Diffusion component. CT cells from limb regenerate cluster with cells from uninjured upper arm tissue. (E) Diffusion component (DC) 3 captures the cell type heterogeneity in the uninjured CT, which is lost in the blastema. (F) Cellular heterogeneity of the mature CT is lost in the blastema. Expression of cell type marker genes (gene groups i to vi) identified for the uninjured CT is shown for each blastema time point as heatmap with genes in columns and cells hierarchically clustered in rows. Transcript levels are scaled across columns, respectively. (G) Mean pairwise correlation (Pearson) between genes of each of the 6 identified gene groups (panel F) across all cells was calculated for each experimental time point. Mean correlation coefficients decrease over the course of blastema formation indicating the loss of cell type heterogeneity in the blastema. Error bars indicate standard deviation. (H) Heatmap visualization of time point-specific marker genes (columns) with cells (rows) ordered by diffusion pseudotime (see also fig. S4I). GO enrichments are provided below the heatmap for each gene group and exemplary genes are shown at the top (see also fig. S5A). Colored sidebar on the left indicates time points. (I) Pseudotemporal expression of different gene signatures across all cells from uninjured upper arm CT to blastema 18dpa. Smoothed conditional means using LOESS are presented.

Article Snippet: To understand the molecular pathways involved in CT regeneration, we used a high transcriptome coverage scRNA-seq method (Fluidigm C1) to analyze mCherry + cells isolated by FACS ( Prrx1:Cre-ER;Caggs:lp-Cherry ) along a dense time course that captures the major transitions during regeneration.

Techniques: Expressing, Marker, Diffusion-based Assay, Standard Deviation

Journal: Science (New York, N.Y.)

Article Title: Single-cell transcriptomics uncovers molecular funneling of cell identities during axolotl limb regeneration

doi: 10.1126/science.aaq0681

Figure Lengend Snippet: (A) Overview of scRNA-seq experiments on three axolotl limb bud stages. 279 limb bud CT cells were in silico identified based on Prrx1 expression and their transcriptomes were compared to scRNA-seq data of the blastema cells. (B) Left: Heatmap showing expression of genes (columns) that distinguish mature limb CT cells from limb bud CT cells (rows). Right: Heatmap showing expression of marker genes for uninjured CT cell types (columns) across mature limb and limb bud CT cells (rows). Cells are hierarchically clustered (Pearson) based on expression of all shown genes. (C) Bar graphs show fraction of cells per embryonic stage that express genes involved in proximal-distal patterning (Meis2, Hoxa11, Hoxa13) or in anterior-posterior patterning (Fgf8, Shh). (D) Spatial patterning genes describe most of the heterogeneity found in the limb bud CT (See also fig. S6C). Intercellular correlation network constructed for stage 44 limb bud cells (circles) based on expression of 5 known patterning genes places cells on a hypothetical position within an imaginary limb bud. Note, that Hand2 instead of Shh was used as anterior marker due to the low number of Shh expressing cells (Fig. 3C). (E) Limb bud patterning genes are reactivated during blastema formation. Bar graphs show fraction of cells per blastema time point that express genes involved in proximal-distal patterning (Meis2, Hoxa11, Hoxa13) or in anterior-posterior patterning (Fgf8, Shh). (F) Intercellular correlation network constructed for all blastema 11 dpa cells (circles) based on expression of 5 patterning genes places cells on a hypothetical position within an imaginary limb blastema. (See also fig. S6D). (G) Correlation analysis reveals the highest similarity of limb bud progenitors with blastema 11 dpa cells. Boxplot shows distributions of scaled correlation between single cell transcriptomes at any given time point and the mock bulk transcriptome of stage 44 limb bud CT cells. (H) Correlation analysis reveals the highest similarity of stage 28 limb field cells with blastema 11 dpa cells. Boxplots show distributions of scaled correlation values between single-cell transcriptomes at the different sampled time points and the mock bulk transcriptome of limb field CT cells. (I) Scatterplot showing differential correlation of single cell transcriptomes (dots, color-coded based on time point) with limb bud versus uninjured mature CT transcriptomes (y-axis) and with blastema 5 dpa versus blastema 11 dpa transcriptomes (x-axis). (J) Dotplot visualizing expression of genes shared between blastema 11 dpa and limb bud progenitor cells. Circle size represents the fraction of cells of each time point expressing the gene and color represents the average expression level.

Article Snippet: To understand the molecular pathways involved in CT regeneration, we used a high transcriptome coverage scRNA-seq method (Fluidigm C1) to analyze mCherry + cells isolated by FACS ( Prrx1:Cre-ER;Caggs:lp-Cherry ) along a dense time course that captures the major transitions during regeneration.

Techniques: In Silico, Expressing, Marker, Construct