Project description:Despite the widespread adoption of ChIP-seq there is still no consensus on quality assessment metrics. No single published metric can reliably discriminate the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. We introduce a new framework for ChIP-seq data quality assessment that overcomes the limitation of previous solutions. Our tool called "ChIC" incorporates a novel set of quality control metrics integrated into one single score summarizing the sample quality and a reference compendium with thousands of published ChIP-seq samples, for easier evaluation of new data. This test dataset contain an example of succesfull and non-succesfull ChIP-seq sample for mouse H3K27me3.
Project description:We present a microfluidic device for rapid gene expression profiling in single cells using multiplexed quantitative polymerase chain reaction (qPCR). This device integrates all processing steps, including cell isolation and lysis, complementary DNA synthesis, pre-amplification, sample splitting, and measurement in twenty separate qPCR reactions. Each of these steps is performed in parallel on up to 200 single cells per run. Experiments performed on dilutions of purified RNA establish assay linearity over a dynamic range of at least 104, a qPCR precision of 15 %, and detection sensitivity down to a single cDNA molecule. We demonstrate the application of our device for rapid profiling of microRNA expression in single cells. Measurements performed on a panel of twenty miRNA in two types of cells revealed clear cell-to-cell heterogeneity, with evidence of spontaneous differentiation manifest as distinct expression signatures. Highly multiplexed microfluidic RT-qPCR fills a gap in current capabilities for single-cell analysis, providing a rapid and cost-effective approach for profiling panels of marker genes, thereby complementing single-cell genomics methods that are best suited for global analysis and discovery. We expect this approach to enable new studies requiring fast, cost-effective, and precise measurements across hundreds of single cells.
Project description:Paired-Tag is an ultra-high throughput single-cell method for simultaneous profiling of gene expression and histone modifications, enabling identification of cell-type-specific cis-regulatory elements and correlation of their chromatin states with the expression levels of putative target genes. However, the lack of an automated end-to-end pipeline has limited its application. Here, we present easyPairedTag, a Snakemake pipeline for Paired-Tag data processing and quality control. Key features include flexible configuration for diverse experimental setups, automated sub-library merging and sample demultiplexing, and comprehensive quality control metrics. When applied to published mouse brain datasets, easyPairedTag improves overlapping gene quantification via strand-specific analysis for precise cell clustering. In mouse hypothalamus datasets, easyPairedTag is compatible with the processing of paired-end sequencing data to detect histone modification peaks with higher sensitivity and specificity, facilitating the discovery of putitive cell-type-specific cis-regulatory elements.
Project description:Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.
Project description:Quality control is a crucial preliminary step in any single-cell RNAseq experiment, where hard thresholds are commonly used. In order to develop a methodology for a more precise cell filtering in the early steps of a scRNAseq data analysis, we collected tissue samples before chemotherapy from 4 patients.
Project description:Cancer is a heterogeneous disease, where multiple, phenotypically distinct subpopulations co-exist. Tumour evolution is a result of a complex interplay of genetic and epigenetic factors. To predict the molecular drivers of distinct cancer responses, we apply single-cell lineage tracing (scRNA-Seq of barcoded cells) on a triple-negative breast cancer model. SUM159PT cells infected with a lentiviral barcode library (Perturb-seq Library) were sorted according to the presence of BFP signal, treated or not with paclitaxel (PTX), multiplexed with MULTI-Seq protocol, and then processed by scRNA-Seq.