individual medium cells  (fluidigm)

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) Schematic overview of the study design (see detailed descriptions and notations in the Methods). Two reference cell lines (Sample A, HCC1395; and Sample B, HCC1395BL) were used to generate scRNA-seq data across four platforms (10X Genomics, Fluidigm C1, Fluidigm C1 HT, and Takara Bio ICELL8), four testing sites (LLU, NCI, FDA, and TBU). At the LLU and NCI sites (10X), mixed single-cell captures and library constructions were also prepared with either 10% or 5% cancer cells spiked into the B lymphocytes. At the NCI site, single-cell captures and library constructions were also performed with methanol-fixed cell mixtures (5% cancer cells spiked into B lymphocytes, Fixed 1 & 2). One set of 10X scRNA libraries from NCI was also sequenced using a shorter modified sequencing method. Bulk cell RNA-seq was also obtained from these cell lines, each in triplicate. See Methods for details about study design. (b) For both the breast cancer cell line (Sample A) and the B lymphocyte line (Sample B) across 14 pair-wise datasets, percentage of reads mapped to the exonic region (blue), non-exonic region (orange), or not mapped to the human genome (gray). For unique molecular identifier (UMI) methods (10X), dark blue indicates the exonic reads with UMIs. (c) Median number of genes detected per cell at different sequencing read depths.

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: Modification, Sequencing, RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) Batch-effect correction in Scenario #1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395; and Sample B, B-lymphocyte line HCC1395BL). Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Batch-effect correction in Scenario #2, where five scRNA-seq datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells were generated separately at the four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Batch-effect correction in Scenario #3, where five scRNA-seq datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from the B lymphocytes were generated separately at the four centers on the same four platforms; (d) Batch-effect correction in Scenario #4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, 10X_NCI_M_Mix5_F2) were generated from 5% or 10% breast cancer cells spiked into B lymphocytes, and analyzed with the 10X Genomics platform at two centers in four different batches. Each dataset is indicated by a unique color in panels (a) to (d). Idealized projection of cells for the four different scenarios is presented on the left. *Note for BBKNN, only UMAP is available and shown. Silhouette width score quantifying the clusterability for (e) Scenario #1 or (f) Scenario #4, corresponding to panels (a) and (d), respectively. (g) kBET acceptance score quantifying the mixability, calculated using the cross-platform/center scRNA-seq data acquired either from breast cancer cells only or from B-lymphocytes only for all four scenarios (a-d, also labeled as Scenarios #1–#4).

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: Generated, Labeling

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Batch-effect corrections were performed for the following four scenarios: (a) Scenario 1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395 and Sample B, B-lymphocyte line HCC1395BL); Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Scenario 2, where five datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells (Sample A, HCC1395) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Scenario 3, where five datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from B-lymphocytes (Sample B, HCC1395BL) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); and (d) Scenario 4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, 10X_NCI_M_Mix5_F2) were generated from 5% or 10% of breast cancer cells (Sample A, HCC1395) spiked into the B-lymphocytes (Sample B, HCC1395BL) and analyzed with the 10X Genomics platform at two centers (LLU and NCI) in four different batches. *For BBKNN, only UMAPs were available and shown in (a-d). The HCC1395 breast cancer cells (Sample A) were labeled in red and the HCC1395BL B lymphocytes (Sample B) were labeled in blue. Batch correction methods included Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). The top 2000 HVGs were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: Generated, Labeling

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Boxplot of silhouette values stratified by eight normalization methods across 14 datasets, including (a) 10X_LLU, (b) 10X_NCI, (c) 10X_NCI_M, (d) C1_FDA_HT, (e) C1_LLU, (f) ICELL8_PE, and (g) ICELL8_SE in breast cancer cells (HCC1395; Sample A) and B lymphocytes (HCC1395BL; Sample B). Eight normalization methods included SCTransform, Scran Deconvolution, CPM, LogCPM, TMM, DESeq, Quantile, and Linnorm. For each dataset, reads of each cell were down-sampled to two different read depths (10K and 100K per cell) before calculating the silhouette width values. LogCPM normalization performed fairly well and was used as the default normalization for our subsequent batch-effect correction analyses. Two normalization methods developed for bulk cell RNA-seq (TMM and Quantile) had the lowest scores. The sample sizes (n) used to derive statistics were: 10X_LLU_A, n= 3560 cells, 10X_LLU_B, n=1770 cells; 10X_NCI_A, n=4284 cells, 10X_NCI_B, n=4136 cells; 10X_NCI_M_A, n=1372 cells, 10X_NCI_M_B, n=2082 cells; C1_LLU_A, n=160 cells, C1_LLU_B, n=132 cells; C1_FDA_HT_A, n=318 cells, C1_FDA_HT_B, n=374 cells; ICELL8_SE_A, n=1134 cells, ICELL8_SE_B, n=1078 cells; ICELL8_PE_A, n=980 cells, ICELL8_PE_B, n=954 cells). For detailed statistics regarding minima, maxima, centre, bounds of box and whiskers and percentile related to the figure, please refer to Supplementary Table 6.

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Batch-effect corrections were performed for the following four scenarios: (a) Scenario 1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395 and Sample B, B-lymphocyte line HCC1395BL); Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Scenario 2, where five datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells (Sample A, HCC1395) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Scenario 3, where five datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from B-lymphocytes (Sample B, HCC1395BL) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); and (d) Scenario 4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were generated from 5% or 10% of breast cancer cells (Sample A, HCC1395), spiked into the B-lymphocytes (Sample B, HCC1395BL), and analyzed with the 10X Genomics platform at two centers (LLU and NCI) in four different batches. Batch correction methods included Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). The top 2000 highly variable genes (HVGs) of these datasets were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: Generated

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Scatter plots displaying the gene expression profile correlations between each of seven scRNA-seq datasets (10X_LLU, 10X_NCI, 10X_NCI_M, C1_FDA, C1_LLU, ICELL8_SE, and ICELL8_PE) vs. their corresponding bulk RNA-seq dataset (BK_RNA-seq) for either (a) breast cancer cells or (b) B lymphocytes. The commonly detected transcripts [(log(CPM +1) normalized] across all datasets were used (15,553 genes for breast cancer cells and 15,201 genes for B lymphocytes) to generate the scatter plots. Each dot represents each gene as a point in each scatterplot; x,y values represent the gene expression variation in a pair of compared datasets. The middle diagonal bar charts display the distribution of the most abundant or rare genes in each dataset and also provide the labels for the respective datasets. The Pearson correlation coefficient R between each of the datasets compared is shown to display the consistency of the different RNA-seq datasets.

Article Snippet: Single-cell full-length cDNA generation and RNA-seq using the C1 Fluidigm system Single cells were loaded on a medium-sized (10–17 μm) RNA-seq integrated fluidic circuit (IFC) at a concentration of 200 cells/μl.

Techniques: Gene Expression, RNA Sequencing