Ontology highlight
ABSTRACT:
PROVIDER: PRJNA718079 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Integrated analysis of multimodal single-cell data
| PRJNA690251 | ENA
Project description:The development of single cell transcriptomic technologies yields large datasets comprising multimodal informations such as transcriptomes and immunophenotypes. Currently however, there is no software to easily and simultaneously analyze both types of data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and epitopes of functional maturation in peripheral T lymphocytes from healthy donors. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.
2020-01-30 | GSE144434 | GEO
Project description:New technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization. However, the sparsity of these measurements and the challenge of integrating multiple binding maps represent significant challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.
2022-02-24 | GSE195725 | GEO
Project description:The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity.
2021-01-15 | GSE164378 | GEO
Project description:Regulation of chromatin states involves the dynamic interplay between different histone modifications to control gene expression. Recent advances have enabled mapping of histone marks in single cells, but most methods are constrained to profile only one histone mark per cell. Here we present an integrated experimental and computational framework, scChIX-seq (single-cell chromatin immunocleavage and unmixing), to map multiple histone marks in single cells. scChIX-seq multiplexes two histone marks together in single cells, then computationally deconvolves the signal using training data from respective histone mark profiles. This framework learns the cell type-specific correlation structure between histone marks, and therefore does not require a priori assumptions of their genomic distributions. Using scChIX-seq, we demonstrate multimodal analysis of histone marks in single cells across a range of mark combinations: two repressive marks, two active marks, and an active plus a repressive mark. In mouse gastrulation, we find that cell type-specific regulation in active chromatin can be accompanied by stable heterochromatin landscapes that are shared across cell types. Applying scChIX-seq to two active marks during macrophage differentiation, we find H3K4me1 dynamics preceding H3K36me3. Modeling these dynamics enables integrated analysis of chromatin velocity during differentiation. Overall, scChIX-seq unlocks systematic interrogation of the interplay between histone modifications in single cells.
2021-11-30 | GSE155280 | GEO
Project description:We developed an optimized, low-cost, split-pool barcoding-based multimodal profiling protocol based upon SHARE-seq (concurrent single-cell ATAC/RNA-seq). With SHARE-seq, we profiled human kidney samples from multiple different anatomical regions. Therefore, we develop a large-scale multimodal single-cell atlas for 3D anatomy of the human kidney.
2024-02-15 | GSE234788 | GEO
Project description:Mammalian kidneys maintain fluid homeostasis through the cellular activity of nephrons and the conjoined collecting system. Each epithelial network originates from distinct progenitor cell populations that reciprocally interact during development. To extend our understanding of human and mouse kidney development, we profiled chromatin organization (ATAC-seq) and gene expression (RNA-seq) in developing human and mouse kidneys. Data were analyzed at a species level and then integrated into a common, cross-species multimodal data set. Comparative analysis of cell types and developmental trajectories identified conserved and divergent features of chromatin organization and linked gene activity, revealing species- and cell-type specific regulatory programs. Identification of human-specific enhancer regions linked through GWAS studies to kidney disease highlights the potential of developmental modeling to provide clinical insight.
2024-06-21 | GSE232478 | GEO
Project description:Mammalian kidneys maintain fluid homeostasis through the cellular activity of nephrons and the conjoined collecting system. Each epithelial network originates from distinct progenitor cell populations that reciprocally interact during development. To extend our understanding of human and mouse kidney development, we profiled chromatin organization (ATAC-seq) and gene expression (RNA-seq) in developing human and mouse kidneys. Data were analyzed at a species level and then integrated into a common, cross-species multimodal data set. Comparative analysis of cell types and developmental trajectories identified conserved and divergent features of chromatin organization and linked gene activity, revealing species- and cell-type specific regulatory programs. Identification of human-specific enhancer regions linked through GWAS studies to kidney disease highlights the potential of developmental modeling to provide clinical insight.
2024-06-21 | GSE232480 | GEO
Project description:Multimodal characterization of cell-free DNA (cfDNA) in the blood can enable the sensitive and non-invasive detection of human cancers but remains technically challenging and costly. Here, we developed Multimodal Epigenetic Sequencing Analysis (MESA), a flexible and sensitive method of capturing and integrating multimodal epigenetic information of cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq (EM-seq) and TET-assisted pyridine borane sequencing (TAPS). MESA can simultaneously infer cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and fragmentation profile for regions surrounding the promoters and polyadenylation sites (PASs). MESA’s integrated analysis of multimodal epigenetic features significantly improved the performance of cancer detection models compared to the usage of any single modality alone. MESA captures additional and highly complementary epigenetic information from cfDNA without additional experimental assays, highlighting the importance and clinical prospect of using multimodal epigenetic features for non-invasive cancer detection
| EGAS00001006462 | EGA