Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ScPCOR-seq enables co-profiling of chromatin occupancy and RNAs in single cells.

ABSTRACT: Cell-to-cell variation in gene expression is a widespread phenomenon, which may play important roles in cellular differentiation, function, and disease development^1-9. Chromatin is implicated in contributing to the cellular heterogeneity in gene expression^10-16. Fully understanding the mechanisms of cellular heterogeneity requires simultaneous measurement of RNA and occupancy of histone modifications and transcription factors on chromatin due to their critical roles in transcriptional regulation^17,18. We generally term the occupancy of histone modifications and transcription factors as Chromatin occupancy. Here, we report a technique, termed scPCOR-seq (single-cell Profiling of Chromatin Occupancy and RNAs Sequencing), for simultaneously profiling genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell. We demonstrated that scPCOR-seq can profile either H3K4me3 or RNAPII and RNAs in a mixture of human H1, GM12878 and 293 T cells at a single-cell resolution and either H3K4me3, RNAPII, or RNA profile can correctly separate the cells. Application of scPCOR-seq to the in vitro differentiation of the erythrocyte precursor CD36 cells from human CD34 stem or progenitor cells revealed that H3K4me3 and RNA exhibit distinct properties in clustering cells during differentiation. Overall, our work provides a promising approach to understand the relationships among different omics layers.

SUBMITTER: Pan L

PROVIDER: S-EPMC9270334 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells.

Project description:Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

| S-EPMC5823944 | biostudies-literature

Co-Profiling of Chromatin Occupancy and RNAs in single cells

Project description:Elucidating the relationship between cellular heterogeneity in epigenome and gene expression is critical to understand the underlying cellular states and functions. Jointly profiling chromatin occupancy and RNA at the single level is needed for this purpose, which is however not available currently. Therefore, we developed Single-cell co-profiling of chromatin occupancy and RNAs sequencing (scPCOR-seq) for simultaneously profiling genome-wide gene expression and TF/histone bindings in the same cell. By applying scPCOR-seq to human H1 and 293T cells, scPCOR-seq can successfully detect gene expression and PolII bindings in the same cell at the single cell level and identify cell type specific Cis-regulatory elements (CRE). We observed that the cellular variation in PolII bindings is strongly correlated with that in gene expression, which is affected by the location of polII bindings. This suggests that a new mechanism for the source of cellular heterogeneity in gene expression. Overall, our work provides a promising approach to understand the relationships among different omics layers.

2022-07-12 | GSE152057 | GEO

Co-Profiling of Chromatin Occupancy and RNAs in single cells

Project description:Co-Profiling of Chromatin Occupancy and RNAs in single cells

| PRJNA638163 | ENA

Protocol for scChaRM-seq: Simultaneous profiling of gene expression, DNA methylation, and chromatin accessibility in single cells.

Project description:Single-cell multi-omics sequencing technology can infer cell heterogeneity and reveal relationships across molecular layers. Combining single-cell RNA sequencing, DNA methylation, and chromatin accessibility allows a multimodal understanding of cell function and epigenetic regulation within individual cells. Here, we offer a protocol to perform scChaRM-seq (single-cell chromatin accessibility, RNA barcoding, and DNA methylation sequencing), which has been applied to study de novo DNA methylation and its relationship with transcription and chromatin accessibility in single human oocytes. For complete details on the use and execution of this protocol, please refer to Yan et al. (2021).

| S-EPMC8608654 | biostudies-literature

Modeling co-occupancy of transcription factors using chromatin features.

Project description:Regulation of gene expression requires both transcription factor (TFs) and epigenetic modifications, and interplays between the two types of factors have been discovered. However study of relationships between chromatin features and TF-TF co-occupancy remains limited. Here, we revealed the relationship by first illustrating distinct profile patterns of chromatin features related to different binding events, including single TF binding and TF-TF co-occupancy of 71 TFs from five human cell lines. We further implemented statistical analyses to demonstrate the relationship by accurately predicting co-occupancy genome-widely using chromatin features including DNase I hypersensitivity, 11 histone modifications (HMs) and GC content. Remarkably, our results showed that the combination of chromatin features enables accurate predictions across the five cells. For individual chromatin features, DNase I enables high and consistent predictions. H3K27ac, H3K4me 2, H3K4me3 and H3K9ac are more reliable predictors than other HMs. Although the combination of 11 HMs achieves accurate predictions, their predictive ability varies considerably when a model obtained from one cell is applied to others, indicating relationship between HMs and TF-TF co-occupancy is cell type dependent. GC content is not a reliable predictor, but the addition of GC content to any other features enhances their predictive ability. Together, our results elucidate a strong relationship between TF-TF co-occupancy and chromatin features.

| S-EPMC4797273 | biostudies-literature

Targeted Perturb-seq enables genome-scale genetic screens in single cells.

Project description:The transcriptome contains rich information on molecular, cellular and organismal phenotypes. However, experimental and statistical limitations constrain sensitivity and throughput of genetic screening with single-cell transcriptomics readout. To overcome these limitations, we introduce targeted Perturb-seq (TAP-seq), a sensitive, inexpensive and platform-independent method focusing single-cell RNA-seq coverage on genes of interest, thereby increasing the sensitivity and scale of genetic screens by orders of magnitude. TAP-seq permits routine analysis of thousands of CRISPR-mediated perturbations within a single experiment, detects weak effects and lowly expressed genes, and decreases sequencing requirements by up to 50-fold. We apply TAP-seq to generate perturbation-based enhancer-target gene maps for 1,778 enhancers within 2.5% of the human genome. We thereby show that enhancer-target association is jointly determined by three-dimensional contact frequency and epigenetic states, allowing accurate prediction of enhancer targets throughout the genome. In addition, we demonstrate that TAP-seq can identify cell subtypes with only 100 sequencing reads per cell.

| S-EPMC7610614 | biostudies-literature

Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.

Project description:Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogeneity regarding breast cancer subtype and crucial cancer-related pathways. Most of the non-cancer cells are immune cells, with three distinct clusters of T lymphocytes, B lymphocytes and macrophages. T lymphocytes and macrophages both display immunosuppressive characteristics: T cells with a regulatory or an exhausted phenotype and macrophages with an M2 phenotype. These results illustrate that the breast cancer transcriptome has a wide range of intratumoral heterogeneity, which is shaped by the tumour cells and immune cells in the surrounding microenvironment.

| S-EPMC5424158 | biostudies-literature

NicE-seq: high resolution open chromatin profiling.

Project description:Open chromatin profiling integrates information across diverse regulatory elements to reveal the transcriptionally active genome. Tn5 transposase and DNase I sequencing-based methods prefer native or high cell numbers. Here, we describe NicE-seq (nicking enzyme assisted sequencing) for high-resolution open chromatin profiling on both native and formaldehyde-fixed cells. NicE-seq captures and reveals open chromatin sites (OCSs) and transcription factor occupancy at single nucleotide resolution, coincident with DNase hypersensitive and ATAC-seq sites at a low sequencing burden. OCSs correlate with RNA polymerase II occupancy and active chromatin marks, while displaying a contrasting pattern to CpG methylation. Decitabine-mediated hypomethylation of HCT116 displays higher numbers of OCSs.

| S-EPMC5488340 | biostudies-literature

GENE-SWitCH Chicken chromatin accessibility profiling by ATAC-seq

Project description: Not available

| S-BSST901 | biostudies-other

GENE-SWitCH Pig chromatin accessibility profiling by ATAC-seq

Project description: Not available

| S-BSST903 | biostudies-other