bioinformatics core facility performed 10x genomics chromium single cell capture  (10X Genomics)

Journal: Cell reports

Article Title: Temporal dynamics of immune cell transcriptomics in brain metastasis progression influenced by gut microbiome dysbiosis

doi: 10.1016/j.celrep.2025.115356

Figure Lengend Snippet:

Article Snippet: ND GBCF prepared cells for 10X Genomics Chromium single cell capture and cDNA libraries according to the standard CITE-seq ( https://citeseq.files.wordpress.com/2019/02/cite-seq_and_hashing_protocol_190213.pdf ) and 10X Genomics standard protocols.

Techniques: In Vivo, Recombinant, Blocking Assay, Red Blood Cell Lysis, Multiplex Assay, Sequencing, Software, Amplification, Fluorescence

Journal: EMBO Reports

Article Title: RNA binding protein ZCCHC24 promotes tumorigenicity in triple-negative breast cancer

doi: 10.1038/s44319-024-00282-8

Figure Lengend Snippet: Reagents and tools table

Article Snippet: 10X Chromium Single Cell Capture Chip , 10X Genomics , Cat#1000127.

Techniques: Recombinant, Sequencing, Negative Control, Modification, Red Blood Cell Lysis, Viability Assay, Software, Microscopy, RNA Expression, Expressing

Journal: Genome Research

Article Title: A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data

doi: 10.1101/gr.278234.123

Figure Lengend Snippet: Overview of the scBayes algorithm. ( A ) The scBayes algorithm combines single-cell RNA sequencing-based transcriptomic analysis (shaded in yellow) with bulk DNA sequencing-based genetic subclone analysis (shaded in blue) to derive subclone-specific expression profiles (shaded in red) via assigning each cell a tumor subclone identity. Cells representing normal cell contamination are also assigned. CP stands for cellular prevalence. ( B ) Simplified overview of our probabilistic model. For a given cell, scBayes evaluates the Bayesian posterior probabilities that the cell represents each of the genetic subclones while taking evolution into consideration (e.g., for H3, both variants of subclone 1 and subclone 3 are considered positive evidences because subclone 3 is the descendent of subclone 1). The cell is assigned to the subclone that maximizes the posterior probability, and meets a minimum probability threshold. See Methods for a complete description of our statistics model.

Article Snippet: We acquired bulk DNA sequencing and single-cell RNA sequencing (10x Genomics Chromium 5′ capture protocol) data from three chronic lymphocytic leukemia patients ( ).

Techniques: RNA Sequencing, DNA Sequencing, Expressing

Journal: Genome Research

Article Title: A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data

doi: 10.1101/gr.278234.123

Figure Lengend Snippet: Single-cell assignment and subclone-specific EMT signature enrichment analysis in a longitudinal breast cancer data set. ( A ) Tumor subclone evolution reconstructed from bulk whole-genome DNA sequencing data across doxorubicin treatment of a refractory breast cancer patient from our previous study . ( B ) Cell-to-subclone assignment from scRNA-seq data collected from the tumor samples before and after doxorubicin treatment. Blue bars and numbers represent the number of cells from the pretreatment sample assigned to each subclone; orange bars and numbers represent the number of cells from the posttreatment sample assigned to each subclone. ( C ) Z -score of ssGSEA enrichment scores for epithelial to mesenchymal transition (EMT) signature: grouped by subclone ( left ), and by time point ( right ).

Article Snippet: We acquired bulk DNA sequencing and single-cell RNA sequencing (10x Genomics Chromium 5′ capture protocol) data from three chronic lymphocytic leukemia patients ( ).

Techniques: DNA Sequencing

Journal: Genome Research

Article Title: A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data

doi: 10.1101/gr.278234.123

Figure Lengend Snippet: Subclone-specific differential expression and clonotype analysis in a data set collected from a CLL patient undergoing ibrutinib treatment. ( A ) Genetic subclone structure over three time points from bulk DNA-seq analysis. ( B ) UMAP of cell clusters of scRNA-seq data collected at the three time points, colored by time point and labeled by cell types. ( C ) Cell assignment results of B cells, colored by subclone identity assigned via scBayes: blue, orange, green, and gray represents SC1, SC2, normal, and unassigned, respectively. ( D ) Sample and subclone-specific expression profiles of genes TNFRSF13B , TXNIP , and CD69 , highlighting different patterns of expression change across the three time points. (*) P < 0.005 and FDR < 0.05. ( E ) Overall and subclone-specific V(D)J clonotype diversity. Each bar represents a unique clonotype; the height of a bar corresponds to the percentage of that clonotype within the B cell ( left most column) or specific B cell subclone ( right three columns) population.

Article Snippet: We acquired bulk DNA sequencing and single-cell RNA sequencing (10x Genomics Chromium 5′ capture protocol) data from three chronic lymphocytic leukemia patients ( ).

Techniques: Quantitative Proteomics, DNA Sequencing, Labeling, Expressing