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: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.
Project description:Knowledge of the expression profile and spatial landscape of the transcriptome in individual cells is essential for understanding the rich repertoire of cellular behaviors. Here we report multiplexed error-robust fluorescence in situ hybridization (MERFISH), a single-molecule imaging approach that allows the copy numbers and spatial localizations of thousands of RNA species to be determined in single cells. Using error-robust encoding schemes to combat single-molecule labeling and detection errors, we demonstrated the imaging of 100 – 1000 unique RNA species in hundreds of individual cells. Correlation analysis of the ~10^4 – 10^6 pairs of genes allowed us to constrain gene regulatory networks, predict novel functions for many unannotated genes, and identify distinct spatial distribution patterns of RNAs that correlate with properties of the encoded proteins. A single sample is analyzed
Project description:An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.