Project description:We performed single-cell combinatorial indexing ATAC-seq on the basal-like TNBC cell line HCC1143 under MEK, PI3K, BET and combination treatments as well as DMSO controls
Project description:The goal of this experiment was to understand the changes in gene expression in the human basal-like breast cancer cell line HCC1143 following treatment with the MEK inhibitor Trametinib (T), PI3K/mTOR inhibitor BEZ235 (B), the BET inhibition JQ1 (JQ), Trametinib + JQ1 (TJ), or BEZ235 + JQ1(BJ), compared to a DMSO control (D). Samples were treated for 72hr and run in triplicate.
Project description:We developed a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or nuclei (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We applied sci-RNA-seq to profile nearly 50,000 cells from C. elegans at the L2 stage, effectively ~56-fold “shotgun cellular coverage” of its somatic cell composition.
Project description:Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed “single-cell combinatorial fluidic indexing” (scifi). The scifi-RNA-seq assay combines one-step combinatorial pre-indexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Pre-indexing allows us to load multiple cells per droplet and bioinformatically demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and it provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow. In contrast to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets.
Project description:Pancreatic cancer is one of the most lethal cancers. Preclinical studies have shown adaptive resistance to Raf/MEK/ERK and PI3K/Akt pathway inhibition. We identify common protein expression alterations associated with adaptive resistance to MEK and PI3K kinase inhibition in KRas-mutant pancreatic cancer cells.
Project description:Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated prior to biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from over 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas. 3 replicates from GM12878 and HL-60 cell lines collected for differential gene expression analysis.
Project description:We describe a new generalizable library generation chemistry with increased efficiency that is amendable to tagmentation-based split-pool barcoding strategies, such as single-cell combinatorial indexing (sci). Symmetrical strand sci (‘s3’) uses a novel uracil-based adapter switching approach that provides an improved rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-ATAC) to profile human cortical and mouse whole brain tissues, with mouse datasets demonstrating a 6-to-13-fold improvement in usable reads obtained per cell when compared to other available methods performed on the same sample type. We also demonstrate the generalizability of s3 by applying it to single-cell whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), for structural variant calling in a patient-derived cancer cell line model. Using the high-coverage profiles produced by the s3 technologies we characterized preserved clonal structure and identified a putative subclone-specific translocation.
Project description:Human T98G glioblastoma cells were stimulated with platelet-derived growth factor (PDGF) and analyzed by DNA microarrays, which identified 74 immediate-early gene transcripts. Cells were then treated with inhibitors to identify subsets of genes that are targets of the phosphatidylinositol 3-kinase (PI3K) and MEK/ERK signaling pathways. Four groups of PDGF-induced genes were defined: independent of PI3K and MEK/ERK signaling, dependent on PI3K signaling, dependent on MEK/ERK signaling, and dependent on both pathways. Cells were grown in Minimal Essential Medium (Invitrogen) supplemented with fetal calf serum (10%). For growth factor/inhibitor treatments, cells were incubated in serum-free medium for 72 h, and either left unstimulated, or stimulated for 30 min with human PDGF-BB (50 ng/ml) (Sigma). U0126 (Cell Signaling Technology) and LY294002 (BioMol) were added 60 min prior to PDGF addition. Keywords: other