Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

ABSTRACT: Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

PROVIDER: PRJNA786471 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR17139186_1.fastq.gz	Fastqsanger.gz
	SRR17139186_2.fastq.gz	Fastqsanger.gz
	SRR17139187_1.fastq.gz	Fastqsanger.gz
	SRR17139187_2.fastq.gz	Fastqsanger.gz
	SRR17139188_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 14

Similar Datasets

Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

Project description:Understanding lung immunity requires an unbiased profiling of tissue-resident T cells at their precise anatomical locations within the lung, but such information has not been characterized in the immunized mouse model. In this pilot study, using 10x Genomics Chromium and Visium platform, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using a well-established Klebsiella pneumoniae infection model. We built an optimized deconvolution pipeline to accurately decipher specific cell-type compositions by anatomic location. We discovered that combining scATAC-seq and scRNA-seq data may provide more robust cell-type identification, especially for lineage-specific T helper cells. Combining all three modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines. In summary, our proof-of-principle study demonstrated the power and potential of single-cell multi-omics analysis to uncover spatial- and cell-type-dependent mechanisms of lung immunity.

| S-EPMC9418911 | biostudies-literature

Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

Project description:Immunological memory is key to productive adaptive immunity. An unbiased, high through-put gene expression profiling of tissue-resident memory T cells residing in various anatomical location within the lung is fundamental to understand lung immunity but still lacking. In this study, using a well-established model on Klebsiella pneumoniae, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after Immunization using the 10x Genomics Chromium and Visium platform. We employed several deconvolution algorithms and established an optimized deconvolution pipeline to accurately decipher specific cell-type composition by location. We identified and located 12 major cell types by scRNA-seq and spatial transcriptomic analysis. Integrating scATAC-seq data from the same cells processed in parallel with scRNA-seq, we found epigenomic profiles provide more robust cell type identification, especially for lineage-specific T helper cells. When combining all three data modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines for chemotaxis. Furthermore, cell-cell communication analysis of spatial transcriptome provided evidence of lineage-specific T helper cells receiving designated cytokine signaling. In summary, our first-in-class study demonstrated the power of multi-omics analysis to uncover intrinsic spatial- and cell-type-dependent molecular mechanisms of lung immunity. Our data provides a rich research resource of single cell multi-omics data as a reference for understanding spatial dynamics of lung immunization.

2022-08-15 | GSE190225 | GEO

Single-cell profiling of developing mouse lungs

Project description:Single-cell profiling of developing mouse lungs

| PRJNA604809 | ENA

Single cell ATAC-sequencing on E17.5 mouse lungs.

Project description:Single cell ATAC-sequencing on E17.5 mouse lungs.

| PRJNA661084 | ENA

Single-cell connectomic analysis of adult mammalian lungs

Project description:Single-cell connectomic analysis of adult mammalian lungs

| PRJNA552370 | ENA

Single-cell profiling of developing mouse lungs

Project description:Single-cell profiling of wild type lungs was performed to delineate the expression profile of epithelial, endothelial, mesenchymal and immune cells.

2020-06-02 | GSE144769 | GEO

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.

Project description:Single cell experimental techniques reveal transcriptomic and epigenetic heterogeneity among cells, but how these are related is unclear. We present MATCHER, an approach for integrating multiple types of single cell measurements. MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type. Using scM&T-seq and sc-GEM data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences. MATCHER also reveals new insights into the dynamic interplay between the transcriptome and epigenome in single embryonic stem cells and induced pluripotent stem cells.

| S-EPMC5525279 | biostudies-other

Single-cell RNA-seq in AT2 cell-ablated mouse lungs

Project description:Single-cell RNA-seq in AT2 cell-ablated mouse lungs

| PRJNA727357 | ENA

Single-cell RNA-seq in AT1 cell-ablated mouse lungs

Project description:Single-cell RNA-seq in AT1 cell-ablated mouse lungs

| PRJNA904777 | ENA

Single-cell RNA-seq in AT1 cell-ablated mouse lungs

Project description:We performed single-cell RNA-seq analysis using Drop-seq S3 method in mouse lungs following AT1 cell ablation

2023-11-02 | GSE218666 | GEO