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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: The violin plot shows the number of genes detected in each cell across 20 scRNA-seq datasets. The plot was generated using Seurat (version 3.1). Each dot represents a single cell. The violin shapes summarize the data distributions, which are colored in the background to signify each of the 20 different scRNA seq datasets. Each scRNA-seq dataset is plotted on the X-axis; the Y-axis shows the corresponding number of genes detected in a cell (nGene) for that dataset. The average number of genes detected in each cell was about 4000 and most of the cells had 2500–7500 genes, except for samples C1_LLU_A and C1_LLU_B. The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: (a–c) Evaluation of the UMI-based (10X) data with Cell Ranger, UMI-Tools, or zUMIs. (d–e) Evaluation of data from non-UMI based technologies C1 full-length transcript, C1 HT, and ICELL8 full-length transcript using FeatureCounts, Kallisto, or RSEM. (a) Bar plot showing the number of cells captured with UMI-based technology; (b) and (d) Box plot showing the number of genes detected per cell in UMI-based and non-UMI based technologies, respectively; (c) and (e) Violin plots showing the gene expression correlation and consensus genes [represented by IoU (Intersection over Union)] per cell between any two pipelines in UMI-based and non-UMI based technologies, respectively. The sample sizes (n) used to derive statistics in (b) and (d) were: (b) 10X_LLU_A, n= 3045 cells; 10X_NCI_A, n=6425 cells; 10X_NCI_M_A, n=6483 cells; 10X_LLU_B, n=1439 cells; 10X_NCI_B, n=3296 cells; 10X_NCI_M_B, n=3273 cells; (d) C1_LLU_A, n=80 cells; C1_FDA_HT_A, n=203 cells; ICELL8_SE_A, n=600 cells; ICELL8_PE_A, n=598 cells; C1_LLU_B, n=66 cells; C1_FDA_B, n=241 cells; ICELL8_SE_B, n=600 cells; ICELL8_PE_B, n=596 cells. For detailed statistics regarding minima, maxima, centre, bounds of box and whiskers and percentile related to the figure, please refer to Supplementary Table 5.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques: Gene Expression

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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: Five different batches of scRNA-seq data (10X_LLU_A, 10X_LLU_B, 10X_NCI_A, 10X_NCI_B, and 10X_NCI_Mix5) generated at two sites (LLU and NCI) are shown either as t-SNE plots (panels a-d) or as UMAPs (panels e-h). (a) LogNormalized, scaled data with no regression; (b) LogNormalized, scaled data filtered with mitochondrial (Mito) gene regression >5% and UMI normalization by Seurat v3; (c) ScTransform with no regression; (d) SCTransform with mitochondrial gene regression and UMI normalization; (e) LogNormalized, scaled data with no regression; (f) scaled data with mitochondrial gene regression and UMI normalization; (g) SCTransform with no regression; and (h) SCTransform with mitochondrial gene regression and UMI normalization.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: 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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: t-SNE plots and UMAPs showing the batch-effect corrections performed by seven methods using 20 scRNA-seq datasets across different platforms. Datasets from 10X were down-sampled to 1200 cells per dataset. *Note, for BBKNN, only UMAP was available and shown. The scRNA-seq datasets are colored to identify the four different platforms: 10X 3´ scRNA-seq platform (red), C1 3´ HT scRNA-seq platform (yellow), C1 full-length scRNA-seq platform (light blue), and ICELL8 full-length scRNA-seq platform (dark 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). Scanorama failed to separate two cell types into discrete clusters when non-10X platforms were included in the analysis. The top 2000 HVGs across all datasets were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques:

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) t-SNE plot and (b) UMAP showing batch-effect corrections using twelve 10X Genomics scRNA-seq datasets consisting of both mixed and non-mixed samples from two sites (LLU and NCI) in different batches after Scanorama (version 1.4.) batch correction. (c) t-SNE plot and (d) UMAP showing projections of batch-effect corrections using six 10X scRNA-seq datasets consisting of only non-mixed samples from two sites (LLU and NCI) in different batches after Scanorama (version 1.4.) batch correction. Different colors represent different datasets. All the datasets were down-sampled to 1200 cells per dataset. After the batch correction, cells from the same cell line type clustered together and mixed adequately within the same cell types. All the data were preprocessed using CellRanger 3.1.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques:

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: t-SNE plots and UMAPs showing batch-effect corrections performed by seven methods using 14 non-mixture scRNA-seq datasets across different platforms and sites. Six spiked-in mixture scRNA-seq datasets (10X_LLU_Mix10, 10X_NCI_Mix5, 10X_NCI_Mix5_F, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were removed from the 20 datasets in Scenario 1 for batch-effect correction evaluation. The fourteen non-mixture scRNA-seq datasets are from both breast cancer cells (10X_LLU_A, 10X_NCI_A, 10X_NCI_M_A, C1_FDA_HT_A, C1_LLU_A, ICELL8_SE_A, and ICELL8_PE_A) and B-lymphocytes (10X_LLU_B, 10X_NCI_B, 10X_NCI_M_B, C1_FDA_HT_B, C1_LLU_B, ICELL8_SE_B, and ICELL8_PE_B). Datasets from 10X were down-sampled to 1200 cells per dataset. *Note, for BBKNN, only UMAP was available and shown. 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). All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques:

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: Panels (a-c) show results obtained using fastMNN when the spiked-in (mixed) datasets (i.e., 10X_LLU_Mix10, 10X_NCI_Mix5, 10X_NCI_Mix5_F, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were imported into the pipeline before other non-mixed scRNA-seq datasets from the 20 scRNA-seq datasets of Scenario 1. (a) t-SNE vs. UMAP with color-coding by dataset; (b) tSNE vs. UMAP, colored by cell types (HCC1395, red; HCC1395BL, blue); and (c) A silhouette score = 0.52 showing that fastMNN correctly separated the two cell types into two clusters representing breast cancer cells and B lymphocytes. Panels (d-f) show results obtained using fastMNN when the non-mixed datasets were imported into the pipeline before the mixture datasets. (d) tSNE vs. UMAP with color-coding by datasets or (e) tSNE vs. UMAP colored by cell types; and (f) A low silhouette score of 0.22 showing that fastMNN had difficulty correctly separating the two cell types in this case. Batch-effect corrections were performed using fastMNN (SeuratWrappers v0.1.0) and silhouette width scores were calculated using the silhouette function from the R package cluster (v.2.0.8). Datasets from 10X were down-sampled to 1200 cells per dataset. The order of dataset input is shown on the top of the Figures (a, b, c or d, e, f).

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques:

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: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques: Gene Expression, 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: Feature plots generated across 20 scRNA-seq datasets using the top 10 DEGs specific for (a) breast cancer cells before batch-effect correction; (b) breast cancer cells after fastMNN batch-effect correction; (c) B lymphocytes before batch correction; and (d) B lymphocytes after fastMNN batch-effect correction. Datasets from 10X were down-sampled to 1200 cells per dataset. In feature plots, genes with relatively high expression in each cell are highlighted in brick red (corresponding to breast cancer cells; Sample A) or blue (corresponding to B cells; Sample B).

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques: Generated, Expressing

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) Gene detection sensitivity measured separately for each of the three classes of scRNA-seq protocol: 10X-, non-10X-based 3´ tagging, and full-length. (b) Normalization methods ranked by their clusterability as measured by Z-scores (either the median or the variance of the silhouette width across the 14 datasets). (c) Batch-correction methods ranked by their clusterability as measured by Z-score from the harmonic mean of the silhouette scores (Scenarios #1 and #4). (d) Batch-correction methods ranked by their mixability as measured by Z-score from the harmonic mean of kBET acceptance scores (Scenarios #1–#4). Z-scores are plotted as circles with their size and color shade scaled to the Z-score value from large to small, and dark blue to light blue. Note that larger Z-score values imply better performance, except for clusterability variance, where a smaller value is preferred: *Larger is better; **Smaller is better. (e) Best practice recommendations for single-cell RNA-seq analysis. #The current version of Scanorama did not correct batch effects for data from multiple platforms; however, it worked well when only 10X Genomics data were analyzed. ##Seurat v.3 was suitable for biologically similar samples, but over-corrected batch effects and misclassified cell types if large fractions of distinct cell types were present in different batches.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

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: (a, un-corrected) UMAP of 10 datasets (10X: PBMCs 68K, PBMCs 3K, CD19+ B cells, CD14+ monocytes, CD4+ helper T cells, CD56+ NK cells, CD8+ cytotoxic T cells, CD4+CD45RO+ memory T cells, CD4+CD25+ regulatory T cells; Drop-seq: PBMCs) out of 26 datasets from Hie et al.8 before batch correction by Scanorama. (b, corrected-based on dataset) UMAP of 10 different datasets shown in (a) from Hie et al. after batch correction by Scanorama, colored to identify the datasets. (c, corrected-based on platform) UMAP of 10 different datasets shown in (a) from Hie et al. colored to identify the two different platforms used (10X Genomics and Drop-seq); note poor results using Drop-seq. (d, un-corrected) UMAP of 8 datasets (breast cancer cells: C1_FDA_HT_A, C1_LLU_A, ICELL8_SE_A, and ICELL8_PE_A; and B lymphocytes: C1_FDA_HT_B, C1_LLU_B, ICELL8_SE_B, and ICELL8_PE_B) out of 20 datasets in our study analyzed using three different non-10X sequencing platforms before batch correction by Scanorama. (e, corrected-based on dataset) UMAP of 8 datasets shown in (d) after batch correction by Scanorama, colored to identify the datasets. Note lack of discrimination between different cell types. (f, corrected-based on platform) UMAP of 8 datasets shown in (d) after batch correction by Scanorama, colored to identify the platforms (C1_FDA_HT, blue; C1, purple; ICELL8, pink). The PBMC datasets were downloaded from http://scanorama.csail.mit.edu/data_light.tar.gz. Our eight datasets were preprocessed using the featureCounts pipeline and batch-effect correction was performed using Scanorama V1.4.

Article Snippet: Abbreviations and notations for : 10X_LLU , single cells were captured using a 10X Genomics Chromium controller; scRNA-seq was done at the LLU Center for Genomics using the standard 10X Genomics protocol (26+98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at the NCI sequencing facility using a modified 10X sequencing protocol (26+56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries prepared at the NCI sequencing facility were also sequenced at LLU using the standard 10X sequencing protocol (26+98 bp); C1_FDA_HT , single cells were captured using a Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA sequencing facility (75×2 bp, PE); C1_LLU , single cells were captured using a Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, PE, ~4–4.77M reads/cell); ICELL8_PE , single cells were captured using an ICELL8 chip at TBU and scRNA-seq libraries were paired end sequenced (75×2 bp, PE) at TBU; ICELL8_SE , the same scRNA-seq libraries generated at TBU site were also sequenced, single-end (SE), at LLU (150×1 bp, SE, ~1M reads/cell).

Techniques: Sequencing

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a , Schematic image of the Tn5 fusion proteins used in the experiments. b , Bar plot depicting number of cells used as input for nano-CT and number of cells recovered. c , Comparison of the antibody- and Tn5-binding strategy between scCUT&Tag and nano-CT. d , Cartoon depiction of the tagmentation and library preparation strategy. The nano-Tn5 is loaded with MeA/Me-Rev oligonucleotides, tagmented genomic DNA is used as template for linear amplification, which is then tagmented in a second round with standard Tn5 loaded with MeB/Me-Rev oligonucleotides. The resulting library is amplified by PCR and sequenced. e , Violin plot depicting number of unique reads per cell obtained by scCUT&Tag and nano-CT targeting H3K27me3 per replicate. Violin plots 1–4 from left show multimodal nano-CT performed without ATAC (1 and 3 from left) or with ATAC-seq (2 and 4 from left), and violin plot 5 depicts unimodal nano-CT experiment. f , Individual UMAP embeddings of the single-modality scCUT&Tag (left) and nano-CT (right) data depicting the identified clusters, (scCUT&Tag: 13,932 cells in 4 biological replicates; nano-CT: 6,798 cells in 1 biological replicate; 200,000 cells used as input) g , UMAP co-embedding of the scCUT&Tag data (13,932 cells in 4 biological replicates) together with nano-CT data (6,798 cells in 1 biological replicate; 200,000 cells used as input). Raw matrices obtained by scCUT&Tag and nano-CT were merged together and analyzed without integration. VASC, vascular; AST, astrocytes; RGCs, radial glial cells; OECs, olfactory ensheathing cells; OPCs, oligodendrocyte progenitor cells; MOLs, mature oligodendrocytes; BG, bergman glia; EXC, excitatory neurons; INH, inhibitory neurons; MGL, microglia.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Comparison, Binding Assay, Amplification

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a , Cartoon depicting the strategy used to profile multiple epigenomic modalities. Individual Tn5 and nano-Tn5 are loaded with barcoded oligonucleotides that are used in the analysis to identify the source of tagmentation and demultiplex the modalities. b , Violin plots depicting the number of unique fragments per cell per replicate and modality. c , Violin plots depicting FrIP per cell per replicate and modality. d , UMAP embeddings of the multimodal nano-CT data for ATAC-seq, H3K27ac, and H3K27me3. The lines connect representations of the same cells in the individual modalities (4,434 cells in two biological replicates, which passed quality control for all three modalities individually and originate from the three-modal datasets; 200,000 cells used as input for all replicates). e , UMAP embedding of the individual modalities with cluster labels. n = 2 biological replicates—each biological replicate was profiled both by nano-CT with ATAC (3-modal) and nano-CT without ATAC (2-modal): 4,960 cells ATAC-seq, 12,464 cells H3K27ac, 12,763 cells H3K27me3; 200,000 cells were used as input for all replicates. Cell is shown in modality UMAP if it passes quality control in its respective modality regardless of the other modalities. AST_NT, astrocytes non-telencephalon; AST-TE, astrocytes telencephalon; AST_3, astrocytes 3; AST_4, astrocytes 4; INH1–4, inhibitory neurons; EXC1–4, excitatory neurons; MGL1–3, microglia 1–3; MAC, macrophages; VEC, vascular endothelial cells; PER, pericytes; CHP, choroid plexus epithelial cells; EPE, ependymal cells; CHP-EPE, choroid plexus + ependymal cells; BG, Bergmann glia; VSMC, vascular smooth muscle cells; ABC, arachnoid barrier cells.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Control

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a . Upset plots showing the overlap between cells that pass QC within the different modalities for combinations of H3K27ac and H3K27me3, and also ATAC, H3K27ac and H3K27me3. b . Violin plot showing the distribution of fraction of reads per cell in nano-CT and scCUT&Tag. c . Violin plot showing the distribution of number of reads per in peak regions (FrIP) in nano-CT and scCUT&Tag. d . Violin plot showing the number of fragments per cell for scCUT&Tag and nano-CT datasets downscaled to 30,000,000 reads for each dataset. e . Violin plot depicting the number of fragments per cell for multi-CUT&Tag and nano-CT and for two modalities (H3K27ac and H3K27me3). f . Fraction of reads not mapped, unique, result of linear amplification duplicates or result of PCR duplicates in nano-CT and multimodal nano-CT experiments. g . UMAP co-embedding of CCA-integrated H3K27ac nano-CT together with scRNA-seq dataset. Gene body and promoters were used as gene activity scores for the integration. h . UMAP co-embedding of CCA-integrated nano-CT ATAC-seq together with scRNA-seq dataset. Gene body and promoters were used as gene activity scores for the integration.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Amplification, Activity Assay

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: Genome browser tracks of the multimodal data for several clusters showing marker peak regions. Markers: Mag for ATAC/H3K27ac in mature oligodendrocytes and Dmkn for H3K27me3 in microglia.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Marker

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a . Genome browser tracks for all modalities profiled by multimodal nano-CT (ATAC, H3K27ac, H3K27me3) around the a . HoxB, b . HoxC and c . HoxD loci.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques:

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a . Scatter plot showing the signal of scATAC-seq (single modality profiled. from 10x Genomics) and multimodal ATAC-seq performed with nano-CT in cluster astrocytes and in astrocyte-specific peaks. b . The same scatter plot as in a .,but stratified by H3K27ac signal. H3K27ac low represents peaks with the 20% lowest quantile of H3K27ac signal and H3K27ac high represents peaks with 20% highest quantile of H3K27ac signal. c . Metagene heatmaps showing the genomic distribution of the ATAC-seq signal in the same regions as in b . d . Scatter plot showing the cluster astrocytes H3K27ac signal profiled either together with H3K27me3 (without_ATAC) or together with ATAC-seq and H3K27me3 (with ATAC) within the astrocyte-specific peaks. e . The scatter plot of H3K27ac astrocytic signal within peak regions stratified by ATAC-seq signal. ATAC low represents peaks with the 20% lowest quantile of ATAC-seq signal and ATAC high represents peaks with the 20% highest quantile of ATAC-seq signal. f . Metagene heatmaps showing the genomic distribution of H3K27ac signal in the same regions as in e .

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques:

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: Confusion matrix of broad cell identities between a . ATAC and H3K27ac b . ATAC and H3K27me3 and c . H3K27ac and H3K27me3. Confusion matrices for fine cluster identities across the different modalities for d . ATAC and H3K27ac e .ATAC and H3K27me3 and f . H3K27ac and H3K27me3.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques:

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a , UMAP embedding of the individual modalities with cluster labels identified through WNN analysis. Embedding is based on individual modalities, whereas cluster identities are assigned from WNN dimensionality reduction. b , Venn diagram showing the overlap of peaks identified from the individual modalities. c , d , UMAP projection and visualization of ATAC, H3K27ac and H3K27me3 signal intensity in single cells at the Foxg1 ( c ) and Irx2 loci ( d ). Gray lines connect the cells with same the single-cell barcodes across the different modalities. Clusters for telencephalon astrocytes (AST_TE) and non-telencephalon astrocytes (AST_NT) were selected for the visualization. Aggregated pseudo-bulk tracks for all modalities together with genomic annotations are shown to the right.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques:

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a . Alluvial diagram of corresponding cell identities between the ATAC, H3K27ac and H3K27me3 modalities. b,c . UMAP projection and visualization of ATAC, H3K27ac and H3K27me3 signal intensity in single cells at the Lhx2 (b) and Foxb1 (c) locus. Gray lines connect the cells with same single-cell barcodes across the different modalities. Clusters telencephalon astrocytes (AST_TE) and non-telencephalon astrocytes (AST_NT) were selected for the visualization.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques:

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a , UMAP embedding showing pseudo-time calculated by slingshot on the basis of WNN dimensionality reduction and cluster identities. b , Scatter plot depicting meta-region score for all modalities ( y -axis) and pseudo-time ( x -axis). The score was calculated as a sum of normalized score across all regions. The regions were selected on the basis of P value ( P < 0.05, Wilcoxon test) and log fold change > 0 at the marker regions of the ATAC modality, and top 200 regions were used. The line depicts local polynomial regression fit (loess) of the data and shaded regions depict 95% confidence intervals. c , Heat map representation of the H3K27me3 signal intensity at the regions the marker regions that are gaining H3K27me3 during oligodendrocytes differentiation ( P < 0.05, Wilcoxon test, log fold change > 0, top 200 regions). Each column depicts one single cell and row single genomic region (peak). Cells are ordered by pseudo-time calculated as shown in a . The order of the regions is based on k -means clustering of the matrix with k = 2. d , Scatter plots depicting meta-region score for all modalities ( y -axis) and pseudo-time ( x -axis). The score was calculated as a sum of normalized score across all regions. The regions were selected on the basis of P value ( P < 0.05, Wilcoxon test) and log fold change > 0 at the marker regions of the H3K27ac modality, and top 200 regions were used. The regions were further stratified to wave 1 and wave 2 regions on the basis of k -means clustering as shown in c . The line depicts local polynomial regression fit (loess) of the data and shaded regions depict 95% confidence intervals.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Marker

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a , UMAP projection and chromatin velocity visualization. The chromatin velocity was calculated by using ATAC-seq gene-by-cell matrix as input into the unspliced layer and H3K27ac gene-by-cell matrix into the spliced layer and then running scvelo algorithm using default parameters. b , Phase plots of ATAC-seq and H3K27ac signal for key genes associated with oligodendrocyte differentiation ( Mal , Mag ). c , UMAP projection of the latent time calculated by the scvelo algorithm. d , Heat map showing H3K27ac signal normalized with sctransform . Rows depict individual top velocity driver genes, sorted by time of value with maximum intensity and columns depict individual cells sorted by latent time. e , Heat map representing gene expression profiles measured by scRNA-seq in the oligodendrocyte lineage. Rows depicts individual genes, clustered by similarity and columns depict single cells ordered in pseudo-time. f , Violin plot showing normalized expression of set of marker genes identified in scRNA-seq dataset, and normalized expression of a set of genes identified as the key driver genes by scvelo. g , UMAP projection and velocity vectors projection of chromatin velocity calculated using H3K27ac gene-by-cell matrix used as input into unspliced layer and H3K27me3 gene-by-cell matrix used as input into the spliced layer and then running the scvelo algorithm using default parameters.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Gene Expression, Expressing, Marker

Journal: Nature Biotechnology

Article Title: Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

doi: 10.1038/s41587-022-01535-4

Figure Lengend Snippet: a . Phase plots of H3K27ac and ATAC, velocity and expression signal for top 10 most likely velocity driver genes identified by scvelo. b . Heatmap showing ATAC-seq signal normalized with sctransform . Rows depict individual top velocity driver genes, sorted by time of value with maximum intensity and columns depict individual cells sorted by latent time. c . Heatmap showing the chromatin velocity. Rows depict individual top velocity driver genes, sorted by time of value with maximum intensity and columns depict individual cells sorted by latent time. d . Gene ontology enrichment analysis of the most important velocity driver genes. P value was calculated using R package enrichGO, using one sided Fischer’s test with Benjamini-Hochberg correction for multiple hypothesis testing.

Article Snippet: Single-cell indexing was performed using Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit v1.1 (10x Genomics).

Techniques: Expressing

Journal: Discover Oncology

Article Title: CRISPR/Cas technologies in pancreatic cancer research and therapeutics: recent advances and future outlook

doi: 10.1007/s12672-025-03383-5

Figure Lengend Snippet: Generation of pancreatic cancer models employing a range of CRISPR systems. Various CRISPR gene editing techniques are instrumental in generating GEMMs. Among them, CRISPR/Cas9 plays a pivotal role in the creation of transplantation models, either by modifying the genome of pancreatic cancer cells or by manipulating the immune system to facilitate PDX models. This system has also been widely applied in generating transgenic pancreatic cancer cell lines and genetically modified organoids, advancing research in cancer biology and therapeutic development

Article Snippet: 2 μg/ml puromycin at different time point (Day 15, 27, 31, 35) , HPAF-II, AsPC-1, PaTu8988S , Knockout , Toronto KnockOut (TKO) CRISPR Library (addgene No. 1000000069) , Wnt pathway genes, FZD5 , CRISPR knockout, Antibody-mediated inhibition , -Reduced proliferation , [ ] .

Techniques: CRISPR, Transplantation Assay, Transgenic Assay, Genetically Modified

Journal: Discover Oncology

Article Title: CRISPR/Cas technologies in pancreatic cancer research and therapeutics: recent advances and future outlook

doi: 10.1007/s12672-025-03383-5

Figure Lengend Snippet: Pooled CRISPR screening approaches in different experimental conditions. In direct in vivo screening, CRISPR is delivered into living organisms (e.g., mice) to induce genetic modifications in their natural biological context. In indirect in vivo screening, CRISPR is applied to cell lines or organoids derived from the in vivo model, which are then reintroduced into the organism, allowing for controlled exploration of genetic modifications. In vitro CRISPR screening is conducted in cultured cells for high-throughput gene editing and analysis of specific genetic targets. Sequencing technologies, such as NGS, are then used to identify novel oncogenes and druggable targets, providing insights into gene functions and the impact of specific genetic changes in isolated cells

Article Snippet: 2 μg/ml puromycin at different time point (Day 15, 27, 31, 35) , HPAF-II, AsPC-1, PaTu8988S , Knockout , Toronto KnockOut (TKO) CRISPR Library (addgene No. 1000000069) , Wnt pathway genes, FZD5 , CRISPR knockout, Antibody-mediated inhibition , -Reduced proliferation , [ ] .

Techniques: CRISPR, In Vivo, Derivative Assay, In Vitro, Cell Culture, High Throughput Screening Assay, Sequencing, Isolation

Journal: Discover Oncology

Article Title: CRISPR/Cas technologies in pancreatic cancer research and therapeutics: recent advances and future outlook

doi: 10.1007/s12672-025-03383-5

Figure Lengend Snippet: Therapeutic applications of CRISPR-driven gene editing. Through the targeted modification of key factors involved in the pathogenesis of pancreatic cancer, CRISPR systems, employing diverse mechanisms, offer promising prospects for advancing therapeutic strategies in the treatment of pancreatic cancer. PC pancreatic cancer

Article Snippet: 2 μg/ml puromycin at different time point (Day 15, 27, 31, 35) , HPAF-II, AsPC-1, PaTu8988S , Knockout , Toronto KnockOut (TKO) CRISPR Library (addgene No. 1000000069) , Wnt pathway genes, FZD5 , CRISPR knockout, Antibody-mediated inhibition , -Reduced proliferation , [ ] .

Techniques: CRISPR, Modification