icell8  (TaKaRa)


Bioz Verified Symbol TaKaRa is a verified supplier
Bioz Manufacturer Symbol TaKaRa manufactures this product  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 94

    Structured Review

    TaKaRa icell8
    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the <t>ICELL8</t> cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Icell8, supplied by TaKaRa, used in various techniques. Bioz Stars score: 94/100, based on 16 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/icell8/product/TaKaRa
    Average 94 stars, based on 16 article reviews
    Price from $9.99 to $1999.99
    icell8 - by Bioz Stars, 2022-09
    94/100 stars

    Images

    1) Product Images from "A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease"

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    Journal: bioRxiv

    doi: 10.1101/2021.10.25.465702

    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Figure Legend Snippet: Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Techniques Used: RNA Sequencing Assay, Single-cell Isolation, Sequencing

    Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.
    Figure Legend Snippet: Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Techniques Used: Derivative Assay, Marker

    Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).
    Figure Legend Snippet: Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).

    Techniques Used: RNA Sequencing Assay, Expressing

    Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.
    Figure Legend Snippet: Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Techniques Used: Chromatin Immunoprecipitation

    2) Product Images from "Maturing heart muscle cells: Mechanisms and transcriptomic insights"

    Article Title: Maturing heart muscle cells: Mechanisms and transcriptomic insights

    Journal: Seminars in cell & developmental biology

    doi: 10.1016/j.semcdb.2021.04.019

    Overview of single-cell RNA-seq pipeline and downstream data analysis. Isolation of healthy cells from dissociated hearts depends on the timepoint as postnatal and adult CMs require LP-FACS or the iCell8, while drop-seq and 10X can be used for embryonic cells. Libraries are then prepared, preferably using UMIs and spike-ins to improve normalization. Following sequencing, downstream analyses include trajectory-based determination of maturation status, GRN reconstruction, and unsupervised clustering to group cell types. Acronyms: LP-FACs, large particle fluorescence-activated cell sorting; UMI, unique molecular identifier; scRNA-seq, single cell RNA-sequencing; GRN, gene regulatory network.
    Figure Legend Snippet: Overview of single-cell RNA-seq pipeline and downstream data analysis. Isolation of healthy cells from dissociated hearts depends on the timepoint as postnatal and adult CMs require LP-FACS or the iCell8, while drop-seq and 10X can be used for embryonic cells. Libraries are then prepared, preferably using UMIs and spike-ins to improve normalization. Following sequencing, downstream analyses include trajectory-based determination of maturation status, GRN reconstruction, and unsupervised clustering to group cell types. Acronyms: LP-FACs, large particle fluorescence-activated cell sorting; UMI, unique molecular identifier; scRNA-seq, single cell RNA-sequencing; GRN, gene regulatory network.

    Techniques Used: RNA Sequencing Assay, Isolation, FACS, Sequencing, Fluorescence

    3) Product Images from "A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease"

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    Journal: bioRxiv

    doi: 10.1101/2021.10.25.465702

    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Figure Legend Snippet: Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Techniques Used: RNA Sequencing Assay, Single-cell Isolation, Sequencing

    Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.
    Figure Legend Snippet: Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Techniques Used: Derivative Assay, Marker

    Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).
    Figure Legend Snippet: Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).

    Techniques Used: RNA Sequencing Assay, Expressing

    Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.
    Figure Legend Snippet: Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Techniques Used: Chromatin Immunoprecipitation

    4) Product Images from "A novel single-cell RNA-sequencing approach and its applicability connecting genotype to phenotype in ageing disease"

    Article Title: A novel single-cell RNA-sequencing approach and its applicability connecting genotype to phenotype in ageing disease

    Journal: Scientific Reports

    doi: 10.1038/s41598-022-07874-1

    Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. ( a ) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. ( b ) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. ( c ) Unsupervised t-SNE of the CellenONE-ICELL8 approach. ( d ) t-SNE showing most prominent markers for different clusters. ( e ) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.
    Figure Legend Snippet: Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. ( a ) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. ( b ) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. ( c ) Unsupervised t-SNE of the CellenONE-ICELL8 approach. ( d ) t-SNE showing most prominent markers for different clusters. ( e ) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Techniques Used: Derivative Assay, Marker

    Study design for the scRNA-Seq approaches validation. ( a ) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8 nano-well chip was used for all eight dermal fibroblasts patient samples. ( b ) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8 nano-well chips were used for all eight dermal fibroblasts patient samples. ( c ) Bar plot displaying proportions of reads left in both single-cell methods after each step of the analysis. ( d ) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.
    Figure Legend Snippet: Study design for the scRNA-Seq approaches validation. ( a ) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8 nano-well chip was used for all eight dermal fibroblasts patient samples. ( b ) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8 nano-well chips were used for all eight dermal fibroblasts patient samples. ( c ) Bar plot displaying proportions of reads left in both single-cell methods after each step of the analysis. ( d ) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Techniques Used: Chromatin Immunoprecipitation

    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. ( a ) the 10× Genomics Chromium ( b ) the ICELL8 cx Single-Cell system; ICELL8 ( c ) the CellenONE X1 system, CellenONE and ( d ) advantages of the combined CellenONE X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Figure Legend Snippet: Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. ( a ) the 10× Genomics Chromium ( b ) the ICELL8 cx Single-Cell system; ICELL8 ( c ) the CellenONE X1 system, CellenONE and ( d ) advantages of the combined CellenONE X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Techniques Used: RNA Sequencing Assay, Single-cell Isolation, Sequencing

    5) Product Images from "A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease"

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    Journal: bioRxiv

    doi: 10.1101/2021.10.25.465702

    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Figure Legend Snippet: Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Techniques Used: RNA Sequencing Assay, Single-cell Isolation, Sequencing

    Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.
    Figure Legend Snippet: Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Techniques Used: Derivative Assay, Marker

    Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).
    Figure Legend Snippet: Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).

    Techniques Used: RNA Sequencing Assay, Expressing

    Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.
    Figure Legend Snippet: Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Techniques Used: Chromatin Immunoprecipitation

    6) Product Images from "Automated CUT Tag profiling of chromatin heterogeneity in mixed-lineage leukemia"

    Article Title: Automated CUT Tag profiling of chromatin heterogeneity in mixed-lineage leukemia

    Journal: Nature Genetics

    doi: 10.1038/s41588-021-00941-9

    Optimization of CUT Tag-Direct for single cell applications on the ICELL8. a , Titrating the concentration of SDS and Triton-X in the nanowell increases the library yield for individual cells, and identifies optimum conditions for Tn5 release and PCR enrichment (Arrow). For all box plots center line = median; box limits = first and third quartiles; whiskers show all data within 1.5 IQRs of the lower and upper quartiles; outliers are not shown; P values were computed using a two sample t-test (two-sided); from left to right n = 84, 63, 51, 71, 37, 62, 31, 39 cells. b , The Fraction of Reads in Peaks (FRiPs) varies across SDS and Triton-X titration conditions. Arrow indicates the optimum conditions. N values the same as in (a). c , Boxplot of unique reads per cell across all of the cells profiled for H3K27me3, H3K36me3 and H3K4me3; H3K27me3, n = 3,611 cells; H3K36me3, n = 1,137 cells; H3K4me3, n = 1,528 cells. d , UMAP projection of single-cells profiled with H3K4me3, H3K27me3 or H3K36me3 colored according to the FRiP scores of each individual cell using peaks called on the aggregate data of all cells profiled for a given mark. Cells with low FRiP scores tend to fall in between clusters and were removed as a quality control. e , Violin plot of FRiP scores for all individual cells profiled using the indicated histone mark; red lines indicate quality control cut-offs for each mark. N values same as (c). f , UMAP projection of single cells profiled for H3K4me3 and colored according to batch (R1, R2, R3). Replicates profiled on different days group together in UMAP space indicating that batch effects have a minimal impact on clustering cells according to the H3K4me3 profiles. g , Same as (f) for H3K27me3. h , Same as (f) for H3K36me3.
    Figure Legend Snippet: Optimization of CUT Tag-Direct for single cell applications on the ICELL8. a , Titrating the concentration of SDS and Triton-X in the nanowell increases the library yield for individual cells, and identifies optimum conditions for Tn5 release and PCR enrichment (Arrow). For all box plots center line = median; box limits = first and third quartiles; whiskers show all data within 1.5 IQRs of the lower and upper quartiles; outliers are not shown; P values were computed using a two sample t-test (two-sided); from left to right n = 84, 63, 51, 71, 37, 62, 31, 39 cells. b , The Fraction of Reads in Peaks (FRiPs) varies across SDS and Triton-X titration conditions. Arrow indicates the optimum conditions. N values the same as in (a). c , Boxplot of unique reads per cell across all of the cells profiled for H3K27me3, H3K36me3 and H3K4me3; H3K27me3, n = 3,611 cells; H3K36me3, n = 1,137 cells; H3K4me3, n = 1,528 cells. d , UMAP projection of single-cells profiled with H3K4me3, H3K27me3 or H3K36me3 colored according to the FRiP scores of each individual cell using peaks called on the aggregate data of all cells profiled for a given mark. Cells with low FRiP scores tend to fall in between clusters and were removed as a quality control. e , Violin plot of FRiP scores for all individual cells profiled using the indicated histone mark; red lines indicate quality control cut-offs for each mark. N values same as (c). f , UMAP projection of single cells profiled for H3K4me3 and colored according to batch (R1, R2, R3). Replicates profiled on different days group together in UMAP space indicating that batch effects have a minimal impact on clustering cells according to the H3K4me3 profiles. g , Same as (f) for H3K27me3. h , Same as (f) for H3K36me3.

    Techniques Used: Concentration Assay, Polymerase Chain Reaction, Titration

    7) Product Images from "Simultaneous CUT Tag profiling of the accessible and silenced regulome in single cells"

    Article Title: Simultaneous CUT Tag profiling of the accessible and silenced regulome in single cells

    Journal: bioRxiv

    doi: 10.1101/2021.12.19.473377

    Deconvolution of CUT Tag2for1 using fragment size and feature width: ( a ) Schematic of the single-cell CUT Tag2for1 experimental rationale, in which two cell types are profiled in bulk in parallel and then arrayed on an ICELL8 microfluidic chip for cell-specific barcoding via amplification and mixing before sequencing. ( b ) Schematic of the deconvolution approach using a Bayesian model by considering differences in fragment length distributions and feature widths of the two targets. PDF: Probability density function. ( c ) Genome browser screenshot showing a CUT Tag2for1 profile (green) in comparison with H3K27me3 CUT Tag (red) and Pol2S5p-CUTAC (blue) for a representative region in H1 human embryonic stem cells (hESC), along with inferred peaks from single-cell CUT Tag2for1 data. ( d ) Same as (c) for K562 cells. ( e-f ) Single antibody and CUT Tag2for1 data at the inferred Pol2S5p (left) and H3K27me3 peaks (right) for H1 and K562 cells, where misclassified peak numbers and percentages are: H1 Pol2S5p (1261 = 8.2%), H1 H3K27me3 (161 = 11.0%), K562 Pol2S5p (396 = 1.0%), and K562 H3K27me3 (496 = 3.7%). In c-f, CUT Tag2for1 P5K27 data represent the pseudo-bulk aggregate for all cells derived by pooling single-cell data, and Pol2S5p and H3K27me3 data are from single antibody data. Results were obtained by pooling cells from two single-cell replicates.
    Figure Legend Snippet: Deconvolution of CUT Tag2for1 using fragment size and feature width: ( a ) Schematic of the single-cell CUT Tag2for1 experimental rationale, in which two cell types are profiled in bulk in parallel and then arrayed on an ICELL8 microfluidic chip for cell-specific barcoding via amplification and mixing before sequencing. ( b ) Schematic of the deconvolution approach using a Bayesian model by considering differences in fragment length distributions and feature widths of the two targets. PDF: Probability density function. ( c ) Genome browser screenshot showing a CUT Tag2for1 profile (green) in comparison with H3K27me3 CUT Tag (red) and Pol2S5p-CUTAC (blue) for a representative region in H1 human embryonic stem cells (hESC), along with inferred peaks from single-cell CUT Tag2for1 data. ( d ) Same as (c) for K562 cells. ( e-f ) Single antibody and CUT Tag2for1 data at the inferred Pol2S5p (left) and H3K27me3 peaks (right) for H1 and K562 cells, where misclassified peak numbers and percentages are: H1 Pol2S5p (1261 = 8.2%), H1 H3K27me3 (161 = 11.0%), K562 Pol2S5p (396 = 1.0%), and K562 H3K27me3 (496 = 3.7%). In c-f, CUT Tag2for1 P5K27 data represent the pseudo-bulk aggregate for all cells derived by pooling single-cell data, and Pol2S5p and H3K27me3 data are from single antibody data. Results were obtained by pooling cells from two single-cell replicates.

    Techniques Used: Chromatin Immunoprecipitation, Amplification, Sequencing, Derivative Assay

    8) Product Images from "CUT Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells"

    Article Title: CUT Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells

    Journal: Genome Biology

    doi: 10.1186/s13059-022-02642-w

    Deconvolution of CUT Tag2for1 using fragment length, cut-site density and feature width. a Schematic of the single-cell CUT Tag2for1 experimental rationale, in which two cell types are profiled in bulk in parallel and then arrayed on an ICELL8 microfluidic chip for cell-specific barcoding via amplification and mixing before sequencing. b Schematic of the deconvolution approach using a Bayesian model by considering differences in fragment length distributions, feature widths of the two targets and cut-site probability density function (PDF). c Genome browser screenshot showing a CUT Tag2for1 profile (green) in comparison with H3K27me3 CUT Tag (red) and Pol2S5p-CUTAC (blue) for a representative region in H1 human embryonic stem cells (hESC), along with inferred peaks from single-cell CUT Tag2for1 data. d Same as c for K562 cells. e , f Single antibody and CUT Tag2for1 data at the inferred Pol2S5p (left) and H3K27me3 peaks (right) for H1 and K562 cells, where misclassified peak numbers and percentages are H1 Pol2S5p (1261 = 8.2%), H1 H3K27me3 (161 = 11.0%), K562 Pol2S5p (396 = 1.0%), and K562 H3K27me3 (496 = 3.7%). In c – f , CUT Tag2for1 data represent the pseudo-bulk aggregate for all cells derived by pooling single-cell data, and Pol2S5p and H3K27me3 data are from single antibody data. Results were obtained by pooling cells from two single-cell replicates
    Figure Legend Snippet: Deconvolution of CUT Tag2for1 using fragment length, cut-site density and feature width. a Schematic of the single-cell CUT Tag2for1 experimental rationale, in which two cell types are profiled in bulk in parallel and then arrayed on an ICELL8 microfluidic chip for cell-specific barcoding via amplification and mixing before sequencing. b Schematic of the deconvolution approach using a Bayesian model by considering differences in fragment length distributions, feature widths of the two targets and cut-site probability density function (PDF). c Genome browser screenshot showing a CUT Tag2for1 profile (green) in comparison with H3K27me3 CUT Tag (red) and Pol2S5p-CUTAC (blue) for a representative region in H1 human embryonic stem cells (hESC), along with inferred peaks from single-cell CUT Tag2for1 data. d Same as c for K562 cells. e , f Single antibody and CUT Tag2for1 data at the inferred Pol2S5p (left) and H3K27me3 peaks (right) for H1 and K562 cells, where misclassified peak numbers and percentages are H1 Pol2S5p (1261 = 8.2%), H1 H3K27me3 (161 = 11.0%), K562 Pol2S5p (396 = 1.0%), and K562 H3K27me3 (496 = 3.7%). In c – f , CUT Tag2for1 data represent the pseudo-bulk aggregate for all cells derived by pooling single-cell data, and Pol2S5p and H3K27me3 data are from single antibody data. Results were obtained by pooling cells from two single-cell replicates

    Techniques Used: Chromatin Immunoprecipitation, Amplification, Sequencing, Derivative Assay

    Similar Products

  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 92
    TaKaRa takara bio icell8 chip
    Nano-well scATAC-seq implementation on the <t>ICELL8</t> platform.
    Takara Bio Icell8 Chip, supplied by TaKaRa, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/takara bio icell8 chip/product/TaKaRa
    Average 92 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    takara bio icell8 chip - by Bioz Stars, 2022-09
    92/100 stars
      Buy from Supplier

    94
    TaKaRa icell8
    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the <t>ICELL8</t> cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.
    Icell8, supplied by TaKaRa, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/icell8/product/TaKaRa
    Average 94 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    icell8 - by Bioz Stars, 2022-09
    94/100 stars
      Buy from Supplier

    94
    TaKaRa icell8 collection kit
    µATAC-seq: a nano-well scATAC-seq implementation on the <t>ICELL8</t> platform. a µATAC-seq workflow. b Distribution of cell counts per well measured by fluorescence microscopy (Hoechst). c µATAC-seq library complexity for null, mouse, and human targeted wells using two separate polymerases (e2Tak and Q5) for well barcoding and amplification ( n = 5000 wells). For each sample, the box denotes the interquartile range centered at the median (red line), while the whiskers span the 5th and 95th percentile range. d Correlation between nano-well chips processed with either a e2Tak (replicate 1) or Q5 polymerase (replicate 2) across all accessible loci. e Inter-well mixing of mouse and human µATAC-seq fragments. f Representative population 22 and single-cell ATAC-seq genome tracks for the Gapdh locus. g Signal-to-background (percent reads in peaks) as a function of read depth ( n = 792). Only cells lying in the upper right quadrant (marked by dashed lines) are retained for downstream analysis
    Icell8 Collection Kit, supplied by TaKaRa, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/icell8 collection kit/product/TaKaRa
    Average 94 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    icell8 collection kit - by Bioz Stars, 2022-09
    94/100 stars
      Buy from Supplier

    94
    TaKaRa icell8 blank chip reagent kit
    Single-cell CUT RUN fragment recovery. The unique reads from each single cell after scCUT Tag is plotted. A single <t>ICELL8</t> chip was used for H3K27me3 sc-CUT Tag, and a second chip for H3K4me2 scCUT Tag. Cells from a single experiment with anti-H3K27me3 antibody were dispensed, and cells from two biological replicates with anti-H3K4me2 antibody were dispensed in alternate sectors of the second chip. Nanowells were excluded if examination of microscopy images showed it to be empty or possibly containing multiple cells. This left 956 nanowells for H3K27me3 CUT Tag and 808 nanowells for H3K4me2 with confirmed single cells that were subjected to PCR enrichment and sequencing.
    Icell8 Blank Chip Reagent Kit, supplied by TaKaRa, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/icell8 blank chip reagent kit/product/TaKaRa
    Average 94 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    icell8 blank chip reagent kit - by Bioz Stars, 2022-09
    94/100 stars
      Buy from Supplier

    Image Search Results


    Nano-well scATAC-seq implementation on the ICELL8 platform.

    Journal: bioRxiv

    Article Title: High-throughput chromatin accessibility profiling at single-cell resolution

    doi: 10.1101/310284

    Figure Lengend Snippet: Nano-well scATAC-seq implementation on the ICELL8 platform.

    Article Snippet: Per cell cost estimateThe per cell library preparation cost is conservatively estimated (assuming only 1200 single cells captured per chip) at 98¢/cell: (1) Takara Bio ICELL8 chip (52¢/cell), (2) Illumina Tn5 (24¢/cell), (3) e2Tak polymerase (4¢/cell).

    Techniques:

    Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Journal: bioRxiv

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    doi: 10.1101/2021.10.25.465702

    Figure Lengend Snippet: Technical information of state-of-the-art integrated platforms for single-cell RNA-Sequencing. Methods used in each of the platforms for single-cell isolation, barcoding, chemistry and sequencing. (a) the 10 x Genomics Chromium (b) the ICELL8 cx Single-Cell system; ICELL8 (c) the CellenONE® X1 system, CellenONE and (d) advantages of the combined CellenONE® X1 and iCELL8 cx Single-Cell systems, CellenONE-ICELL8.

    Article Snippet: It is important to note, that the batch effect observed by merging data from three ICELL8® 5,184 nano-well chips dispensing the same samples was very low (supplementary Figure 2), suggesting that a more sensitive platform with deeper sequencing depth can mitigate the variation to a large degree of cells.

    Techniques: RNA Sequencing Assay, Single-cell Isolation, Sequencing

    Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Journal: bioRxiv

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    doi: 10.1101/2021.10.25.465702

    Figure Lengend Snippet: Analysis of transcriptional heterogeneity of dermal fibroblast derived from patients using the novel platform. (a) t-SNE of the ICELL8 and CellenONE-ICELL8 experiments. (b) Circo plot showing connection of top 100 marker genes of the samples in both ICELL8 and CellenONE-ICELL8 platforms. (c) Unsupervised t-SNE of the CellenONE-ICELL8 approach. (d) t-SNE showing most prominent markers for different clusters. (e) Heatmap of rank scores of the top 30 markers in each unsupervised cluster, including enriched pathways.

    Article Snippet: It is important to note, that the batch effect observed by merging data from three ICELL8® 5,184 nano-well chips dispensing the same samples was very low (supplementary Figure 2), suggesting that a more sensitive platform with deeper sequencing depth can mitigate the variation to a large degree of cells.

    Techniques: Derivative Assay, Marker

    Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).

    Journal: bioRxiv

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    doi: 10.1101/2021.10.25.465702

    Figure Lengend Snippet: Correlations of scRNA-Seq platforms and bulk RNA-seq. (a) Normalized expressions of top 100 markers in each sample from both single-cell platforms: ICELL8 and CellenONE-ICELL8, including a regression line with 95% confidence interval (in gray) and the overall correlation coefficient R. (b) Normalized expression and correlation of the top 100 markers in sample GOE247 between both single-cell and bulk RNA datasets, with the correlation coefficients indicated in the upper triangle (c) Venn diagram of all significant markers detected for sample GOE247 in both platforms (ICELL8 and CellenONE-ICELL8).

    Article Snippet: It is important to note, that the batch effect observed by merging data from three ICELL8® 5,184 nano-well chips dispensing the same samples was very low (supplementary Figure 2), suggesting that a more sensitive platform with deeper sequencing depth can mitigate the variation to a large degree of cells.

    Techniques: RNA Sequencing Assay, Expressing

    Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Journal: bioRxiv

    Article Title: A novel single-cell RNA-sequencing platform and its applicability connecting genotype to phenotype in ageing-disease

    doi: 10.1101/2021.10.25.465702

    Figure Lengend Snippet: Study design for the scRNA-Seq platforms validation. (a) Experimental design for the CellenONE-ICELL8 Single-Cell System combination. Only one ICELL8® nano-well chip was used for all eight dermal fibroblasts patient samples. (b) Experimental design for the ICELL8 Single-Cell System. A total of three (3) ICELL8® nano-well chips were used for all eight dermal fibroblasts patient samples. (c) Bar plot displaying proportions of reads left in both single-cell platforms after each step of the analysis. (d) Venn diagram displaying the number of genes in QC-filtered data from both approaches and the violin plot shows the number of QC-filtered genes expressed in individual cells in both approaches.

    Article Snippet: It is important to note, that the batch effect observed by merging data from three ICELL8® 5,184 nano-well chips dispensing the same samples was very low (supplementary Figure 2), suggesting that a more sensitive platform with deeper sequencing depth can mitigate the variation to a large degree of cells.

    Techniques: Chromatin Immunoprecipitation

    µATAC-seq: a nano-well scATAC-seq implementation on the ICELL8 platform. a µATAC-seq workflow. b Distribution of cell counts per well measured by fluorescence microscopy (Hoechst). c µATAC-seq library complexity for null, mouse, and human targeted wells using two separate polymerases (e2Tak and Q5) for well barcoding and amplification ( n = 5000 wells). For each sample, the box denotes the interquartile range centered at the median (red line), while the whiskers span the 5th and 95th percentile range. d Correlation between nano-well chips processed with either a e2Tak (replicate 1) or Q5 polymerase (replicate 2) across all accessible loci. e Inter-well mixing of mouse and human µATAC-seq fragments. f Representative population 22 and single-cell ATAC-seq genome tracks for the Gapdh locus. g Signal-to-background (percent reads in peaks) as a function of read depth ( n = 792). Only cells lying in the upper right quadrant (marked by dashed lines) are retained for downstream analysis

    Journal: Nature Communications

    Article Title: High-throughput chromatin accessibility profiling at single-cell resolution

    doi: 10.1038/s41467-018-05887-x

    Figure Lengend Snippet: µATAC-seq: a nano-well scATAC-seq implementation on the ICELL8 platform. a µATAC-seq workflow. b Distribution of cell counts per well measured by fluorescence microscopy (Hoechst). c µATAC-seq library complexity for null, mouse, and human targeted wells using two separate polymerases (e2Tak and Q5) for well barcoding and amplification ( n = 5000 wells). For each sample, the box denotes the interquartile range centered at the median (red line), while the whiskers span the 5th and 95th percentile range. d Correlation between nano-well chips processed with either a e2Tak (replicate 1) or Q5 polymerase (replicate 2) across all accessible loci. e Inter-well mixing of mouse and human µATAC-seq fragments. f Representative population 22 and single-cell ATAC-seq genome tracks for the Gapdh locus. g Signal-to-background (percent reads in peaks) as a function of read depth ( n = 792). Only cells lying in the upper right quadrant (marked by dashed lines) are retained for downstream analysis

    Article Snippet: PCR products were extracted by centrifugation at ~3000 g for 10 min using the supplied SMARTer ICELL8 Collection Kit (Takara Bio USA).

    Techniques: Fluorescence, Microscopy, Amplification

    Single-cell CUT RUN fragment recovery. The unique reads from each single cell after scCUT Tag is plotted. A single ICELL8 chip was used for H3K27me3 sc-CUT Tag, and a second chip for H3K4me2 scCUT Tag. Cells from a single experiment with anti-H3K27me3 antibody were dispensed, and cells from two biological replicates with anti-H3K4me2 antibody were dispensed in alternate sectors of the second chip. Nanowells were excluded if examination of microscopy images showed it to be empty or possibly containing multiple cells. This left 956 nanowells for H3K27me3 CUT Tag and 808 nanowells for H3K4me2 with confirmed single cells that were subjected to PCR enrichment and sequencing.

    Journal: bioRxiv

    Article Title: CUT Tag for efficient epigenomic profiling of small samples and single cells

    doi: 10.1101/568915

    Figure Lengend Snippet: Single-cell CUT RUN fragment recovery. The unique reads from each single cell after scCUT Tag is plotted. A single ICELL8 chip was used for H3K27me3 sc-CUT Tag, and a second chip for H3K4me2 scCUT Tag. Cells from a single experiment with anti-H3K27me3 antibody were dispensed, and cells from two biological replicates with anti-H3K4me2 antibody were dispensed in alternate sectors of the second chip. Nanowells were excluded if examination of microscopy images showed it to be empty or possibly containing multiple cells. This left 956 nanowells for H3K27me3 CUT Tag and 808 nanowells for H3K4me2 with confirmed single cells that were subjected to PCR enrichment and sequencing.

    Article Snippet: Control wells containing 0.5X PBS (25 μL) and fiducial mix (25 μL) (Takara Bio USA, Cat. #640196) were also included in the source loading plate.

    Techniques: Chromatin Immunoprecipitation, Microscopy, Polymerase Chain Reaction, Sequencing

    Chromatin profiling of individual cells. a, Single cell CUT Tag (scCUT Tag) processing. All steps from antibody incubations through adapter tagmentation are done on a population of permeabilized unfixed cells. Individual cells are then dispensed into nanowells of a Takara ICELL8 chip. After verifying nanowells with single cells by microscopy, combinations of two indexed barcoded primers are added to each well and fragment libraries are enriched by PCR. Libraries from the chip are pooled for multiplex sequencing. b, A chromatin landscape across a 500 kb segment of the human genome is shown for H3K27me3 CUT Tag. Tracks from bulk CUT Tag, aggregated scCUT Tag, and for 956 single cells are shown. Single cells are ordered by total read counts in each cell.

    Journal: bioRxiv

    Article Title: CUT Tag for efficient epigenomic profiling of small samples and single cells

    doi: 10.1101/568915

    Figure Lengend Snippet: Chromatin profiling of individual cells. a, Single cell CUT Tag (scCUT Tag) processing. All steps from antibody incubations through adapter tagmentation are done on a population of permeabilized unfixed cells. Individual cells are then dispensed into nanowells of a Takara ICELL8 chip. After verifying nanowells with single cells by microscopy, combinations of two indexed barcoded primers are added to each well and fragment libraries are enriched by PCR. Libraries from the chip are pooled for multiplex sequencing. b, A chromatin landscape across a 500 kb segment of the human genome is shown for H3K27me3 CUT Tag. Tracks from bulk CUT Tag, aggregated scCUT Tag, and for 956 single cells are shown. Single cells are ordered by total read counts in each cell.

    Article Snippet: Control wells containing 0.5X PBS (25 μL) and fiducial mix (25 μL) (Takara Bio USA, Cat. #640196) were also included in the source loading plate.

    Techniques: Chromatin Immunoprecipitation, Microscopy, Polymerase Chain Reaction, Multiplex Assay, Sequencing