c1 fluidigm single cell rna sequencing scrna seq  (fluidigm)

Journal: bioRxiv

Article Title: CAbiNet: Joint visualization of cells and genes based on a gene-cell graph

doi: 10.1101/2022.12.20.521232

Figure Lengend Snippet: a , Runtime across all parameter choices for simulated data. b , Mean clustering error (CE) of simulated data sets over all parameter choices. Different simulation parameters (the clustering “difficulty”) are differentiated by shape, whereas the data set used to estimate the base parameters (zeisel and pbmc3k) is marked by different shades of grey. c , Mean Adjusted Rand Index of (bi-)clustering results over all parameter choices for each algorithm on both real and simulated scRNA-seq data sets. The entries colored as grey indicate a failure of the respective algorithm on a certain data set. This can be due to either all runs failing on a data set or the algorithm only detecting a single bicluster.

Article Snippet: For a thorough comparison between CAbiNet and other algorithms, we included diverse data sets with different characteristics: The number of sequenced cells ranges from from 466 to 35,192 cells and the data sets have been produced through different scRNA sequencing methods such as 10x Genomics, Smart-seq2, Fluidigm C1, CEL-seq and Stereo-seq.

Techniques:

Journal: bioRxiv

Article Title: CAbiNet: Joint visualization of cells and genes based on a gene-cell graph

doi: 10.1101/2022.12.20.521232

Figure Lengend Snippet:

Article Snippet: For a thorough comparison between CAbiNet and other algorithms, we included diverse data sets with different characteristics: The number of sequenced cells ranges from from 466 to 35,192 cells and the data sets have been produced through different scRNA sequencing methods such as 10x Genomics, Smart-seq2, Fluidigm C1, CEL-seq and Stereo-seq.

Techniques:

Journal: Cell Death & Disease

Article Title: Trajectory modeling of endothelial-to-mesenchymal transition reveals galectin-3 as a mediator in pulmonary fibrosis

doi: 10.1038/s41419-021-03603-0

Figure Lengend Snippet: A Sing-cell transcriptome sequencing (scRNA-seq) using the Fluidigm C1 platform. Lung cells from the BLM and control groups were collected and sorted by FACS to harvest Tie2 + cells for scRNA-seq. B UMAP visualization of single-cell transcriptomes in the BLM and control groups. Left panel: colored by origin of cells (BLM or control). Right panel: colored according to the unsupervised clustering of cells. C Cluster-wide expression of EndMT-related markers/regulators screened among differentially expressed genes of high significance ( p < 0.01) based on their GO-terms. D Expression levels of candidate regulators in whole lung samples from the BLM (day 7) and control groups according to RT-PCR. The data are presented as means ± SD, n = 3. * p < 0.05, ** p < 0.01, *** p < 0.001.

Article Snippet: A Sing-cell transcriptome sequencing (scRNA-seq) using the Fluidigm C1 platform.

Techniques: Sequencing, Control, Expressing, Reverse Transcription Polymerase Chain Reaction

Journal: Blood

Article Title: Dissecting CLL through high-dimensional single-cell technologies

doi: 10.1182/blood-2018-09-835389

Figure Lengend Snippet: Summary of single-cell studies in CLL

Article Snippet: Herein we highlight the insights they have provided. table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 Reference Sample input Methodology Insight Zhao et al 49 116 CLL cells from 2 time points Whole-genome DNA amplification in individual PCR tubes sCNA identified and subclonal architecture determined Wang et al 50 1152 CLL cells from 5 patients Plate-based targeted multiplex PCR assay following whole-genome amplification sCNA and sSNV identified and subclonal architecture determined Landau et al 58 393 CD19 + B cells from 2 healthy donors and 111 CLL cells from 1 patient Multiplexed scRRBS Uniformly high proportion of discordant reads in CLL cells compared with normal B cells Chaligne et al 59 383 CD19 + B cells from 4 healthy donors and 324 CLL cells from 3 patients Multiplexed scRRBS Epigenetic phylogenetic tree reconstruction Chaligne et al 59 42 CLL cells Multiplexed scRRBS and scRNA sequencing Increasing phylogenetic distance correlates with decreasing transcriptional similarity Zhao et al 49 362 CLL cells from 5 time points (35-126 cells per time point) Plate-based Smart-Seq2 scRNA sequencing Identification of 6 transcriptional clusters and their evolution over time and with treatment Wang et al 50 289 CLL cells from 4 patients Fluidigm C1 System Smart-Seq scRNA sequencing Using pathway and gene set overdispersion analysis identified unique biological processes demonstrating transcriptional heterogeneity Wang et al 44 845 CLL cells from 6 patients Plate-based targeted qPCR using Fluidigm BioMark Single-cell correlation of alternative splicing variants with SF3B1 mutational status Landau et al 13 310 CLL cells from 4 patients Fluidigm C1 System Smart-Seq scRNA sequencing Correlational of single-cell transcriptional heterogeneity with methylation data Burger et al 12 473 cells from 3 time points Plate-based targeted qPCR using Fluidigm BioMark Mutation detection to determine subclonal architecture Burger et al 12 Polydimethylsiloxane microfluidic device and targeted qPCR High-sensitivity mutation detection, including demonstration of the presence of rare resistant cells before ibrutinib initiation Open in a separate window PCR, polymerase chain reaction; qPCR, quantitative PCR; sCNA, somatic copy number alteration; scRNA, single-cell RNA; scRRBS, single-cell reduced representation bisulfite sequencing; sSNV, somatic single nucleotide variant.

Techniques: DNA Amplification, Multiplex Assay, Whole Genome Amplification, Sequencing, Alternative Splicing, Methylation, Mutagenesis

Journal: Blood

Article Title: Dissecting CLL through high-dimensional single-cell technologies

doi: 10.1182/blood-2018-09-835389

Figure Lengend Snippet: Summary of single-cell studies in CLL

Article Snippet: Herein we highlight the insights they have provided. table ft1 table-wrap mode="anchored" t5 Table 1. caption a7 Reference Sample input Methodology Insight Zhao et al 49 116 CLL cells from 2 time points Whole-genome DNA amplification in individual PCR tubes sCNA identified and subclonal architecture determined Wang et al 50 1152 CLL cells from 5 patients Plate-based targeted multiplex PCR assay following whole-genome amplification sCNA and sSNV identified and subclonal architecture determined Landau et al 58 393 CD19 + B cells from 2 healthy donors and 111 CLL cells from 1 patient Multiplexed scRRBS Uniformly high proportion of discordant reads in CLL cells compared with normal B cells Chaligne et al 59 383 CD19 + B cells from 4 healthy donors and 324 CLL cells from 3 patients Multiplexed scRRBS Epigenetic phylogenetic tree reconstruction Chaligne et al 59 42 CLL cells Multiplexed scRRBS and scRNA sequencing Increasing phylogenetic distance correlates with decreasing transcriptional similarity Zhao et al 49 362 CLL cells from 5 time points (35-126 cells per time point) Plate-based Smart-Seq2 scRNA sequencing Identification of 6 transcriptional clusters and their evolution over time and with treatment Wang et al 50 289 CLL cells from 4 patients Fluidigm C1 System Smart-Seq scRNA sequencing Using pathway and gene set overdispersion analysis identified unique biological processes demonstrating transcriptional heterogeneity Wang et al 44 845 CLL cells from 6 patients Plate-based targeted qPCR using Fluidigm BioMark Single-cell correlation of alternative splicing variants with SF3B1 mutational status Landau et al 13 310 CLL cells from 4 patients Fluidigm C1 System Smart-Seq scRNA sequencing Correlational of single-cell transcriptional heterogeneity with methylation data Burger et al 12 473 cells from 3 time points Plate-based targeted qPCR using Fluidigm BioMark Mutation detection to determine subclonal architecture Burger et al 12 Polydimethylsiloxane microfluidic device and targeted qPCR High-sensitivity mutation detection, including demonstration of the presence of rare resistant cells before ibrutinib initiation Open in a separate window PCR, polymerase chain reaction; qPCR, quantitative PCR; sCNA, somatic copy number alteration; scRNA, single-cell RNA; scRRBS, single-cell reduced representation bisulfite sequencing; sSNV, somatic single nucleotide variant.

Techniques: DNA Amplification, Multiplex Assay, Whole Genome Amplification, Sequencing, Alternative Splicing, Methylation, Mutagenesis