Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrated analysis of multimodal single-cell data

ABSTRACT: Integrated analysis of multimodal single-cell data

PROVIDER: PRJNA690251 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Integrated analysis of multimodal single-cell data

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

Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination

Project description:Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination

| PRJNA985377 | ENA

Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination

Project description:Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination

| PRJNA985376 | ENA

Integrated analysis of multimodal single-cell data.

Project description:The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. 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 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate 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 coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

| S-EPMC8238499 | biostudies-literature

Integrated single-cell analysis of preleukemic mouse models

Project description:Integrated single-cell analysis of preleukemic mouse models

| PRJNA942925 | ENA

Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

Project description:Probing epigenomic marks such as histone modifications at a single cell level in thousands of cells has been recently enabled by technologies such as scCUT&Tag. Here we developed a multimodal and optimized iteration of scCUT&Tag called nano-CT (for nano-CUT&Tag) that allows simultaneous probing of three epigenomic modalities at single-cell resolution, using nanobody-Tn5 fusion proteins. nano-CT is compatible with starting materials as low as 25 000 cells and has significantly higher resolution than scCUT&Tag, with a 16-fold increase in the number of fragments per cells. We used nano-CT to simultaneously profile chromatin accessibility, H3K27ac and H3K27me3 in a complex tissue - juvenile mouse brain. The obtained multimodal dataset allowed for discrimination of more cell types/states that scCUT&Tag, and inference of chromatin velocity between ATAC and H3K27ac in the oligodendrocyte (OL) lineage. In addition, we used nano-CT to deconvolute H3K27me3 repressive states and infer two sequential waves of H3K27me3 repression at distinct gene modules during OL lineage progression. Thus, given its high resolution, versatility, and multimodal features, nano-CT allows unique insights in epigenetic landscapes in different biological systems at single cell level.

2022-09-20 | GSE198467 | GEO

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

Project description:SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

| PRJNA1079350 | ENA

A multimodal atlas of human brain cell types_2021 data

Project description:A multimodal atlas of human brain cell types_2021 data

| PRJNA781341 | ENA

A multimodal atlas of human brain cell types_2021 data

Project description:A multimodal atlas of human brain cell types_2021 data

| PRJNA816970 | ENA

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis II

Project description:SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis II