Journal: Nature communications

Article Title: Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors.

doi: 10.1038/s41467-017-00965-y

Figure Lengend Snippet: Fig. 2 Comparison of whole-exome sequencing of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses

Article Snippet: Matched tumor biopsies were processed and sequenced through the Broad Institute Genomics Platform’s Research Whole Exome Sequencing deep coverage pipeline (http://genomics.broadinstitute.org/data-sheets/DTS_WES_1Page_52016_0.pdf).

Techniques: Comparison, Sequencing, Variant Assay, Mutagenesis

Journal: Advanced Science

Article Title: Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single‐Cell Reference and Domain Adaptive Matching

doi: 10.1002/advs.202306329

Figure Lengend Snippet: Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell transcriptome data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.

Article Snippet: The raw sequencing reads from a cDNA library using the BD Rhapsody Whole Transcriptome Assay Analysis Pipeline (v1.8) were processed.

Techniques: RNA Sequencing Assay, Sequencing, Expressing, Construct, Transformation Assay

Journal: Nature Communications

Article Title: Cell type-dependent differential activation of ERK by oncogenic KRAS in colon cancer and intestinal epithelium

doi: 10.1038/s41467-019-10954-y

Figure Lengend Snippet: Differential effects of BRAF V600E or KRAS G12V on gene expression and intestinal cell hierarchies. All panels: t-SNE visualisations and clustering of organoid single-cell transcriptomes clustered with k-means, 24 h after induction of FLUC control, BRAF V600E or KRAS G12V transgenes. a Colour code for six k-means clusters, and inferred differentiation trajectories starting at cluster 1 shown as grey overlay. b Colour code for transgene and CD44 positivity, as inferred from flow cytometry. CD44 positivity was used to direct cell selection, and thus relative fractions of CD44-high and -low cells are not representative. For CD44 status of the cell populations, see Supplementary Fig. . c Mapping of cell- and pathway-specific differentiation signatures. Numbers of signature genes detected are given per single-cell transcriptome

Article Snippet: Single-cell RNA-sequencing data were pre-processed using the BD Precise Whole Transcriptome Assay Analysis Pipeline v2.0 .

Techniques: Expressing, Flow Cytometry, Selection