Single cell multi-omics (scNOVA) group for Skin fibroblast
Ontology highlight
ABSTRACT:
PROVIDER: EGAC00001002833 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Data Access Committee EGAC00001001866
| EGAC00001001866 | EGA
Project description:Data Access Committee EGAC00001001880
| EGAC00001001880 | EGA
Project description:Data Access Committee EGAC00001003353
| EGAC00001003353 | EGA
Project description:Data Access Committee EGAC00001002852
| EGAC00001002852 | EGA
Project description:Organoids were generated from H9 cells. Single cells were sorted from 4-month-old brain organoids differentiated using the telencephalon organoids protocol.
2023-07-10 | E-MTAB-13144 | biostudies-arrayexpress
Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.
2022-06-15 | E-MTAB-11264 | biostudies-arrayexpress
Project description:Data Access Committee EGAC00001001126
| EGAC00001001126 | EGA
Project description:Integrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.
| S-EPMC8095090 | biostudies-literature
Project description:One of the keys to achieving skin regeneration lies within understanding the heterogeneity of neonatal fibroblasts, which support skin regeneration. However, the molecular underpinnings regulating the cellular states and fates of these cells are not fully understood. To investigate this, we performed a parallel multi-omics analysis by processing neonatal murine skin for single-cell ATAC-sequencing (scATAC-seq) and single-cell RNA-sequencing (scRNA-seq) separately. Our approach revealed that fibroblast clusters could be sorted into papillary and reticular lineages based on transcriptome profiling, as previously published. However, scATAC-seq analysis of neonatal fibroblast lineage markers, such as, Dpp4/CD26, Corin, and Dlk1 along with markers of myofibroblasts, revealed accessible chromatin in all fibroblast populations despite their lineage specific transcriptome profiles. These results suggests that accessible chromatin does not always translate to gene expression and that many fibroblast lineage markers reflect a fibroblast state, which includes neonatal papillary, reticular, and myofibroblasts. This analysis also provides a possible explanation as to why these marker genes can be promiscuously expressed in different fibroblast populations under different conditions. Our scATAC-seq analysis also revealed that the functional lineage restriction between dermal papilla and adipocytes fates are regulated by distinct chromatin landscapes. Finally, we have developed a webtool for our multi-omics analysis: https://skinregeneration.org/scatacseq-and-scrnaseq-data-from-thompson-et-al-2021-2/.
2021-11-24 | GSE189210 | GEO
Project description:Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generate the first single cell integrated map of chromatin accessibility and transcriptome in early embryos and demonstrate the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states. The ability to obtain these two layers of omics data will help provide more accurate definitions of "single cell state" and enable the deconvolution of regulatory heterogeneity from complex cell populations.
| S-EPMC6349937 | biostudies-literature