Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrated spatial multi-omics reveals fibroblast fate during tissue repair

ABSTRACT: Integrated spatial multi-omics reveals fibroblast fate during tissue repair

PROVIDER: PRJNA740299 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	SRR14890400_1.fastq.gz	Fastqsanger.gz
	SRR14890400_2.fastq.gz	Fastqsanger.gz
	SRR14890500_1.fastq.gz	Fastqsanger.gz
	SRR14890500_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 841

Similar Datasets

Integrated spatial multi-omics reveals fibroblast fate during tissue repair

Project description:In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to define the mechanisms controlling cell fate during migration, proliferation, and differentiation following tissue injury and thereby reexamine the canonical phases of wound healing. We show that wounding triggers a polyclonal proliferation of tissue-resident, mechano-responsive fibroblasts, the subpopulations of which inhabit spatially-distinct regions within the wound and harbor distinct activities. These findings have broad implications for the study of tissue repair in complex organ systems.

2021-06-24 | GSE178758 | GEO

Integrated spatial multiomics reveals fibroblast fate during tissue repair.

Project description:In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.

| S-EPMC8521719 | biostudies-literature

Integrated analysis of omics profiles

Project description:This SuperSeries is composed of the following subset Series: GSE32691: Autoantibody profile timecourse of UNK GSE32874: Personal Omics Profiling Reveals Dynamic Molecular Phenotypes and Actionable Medical Risks Refer to individual Series

2012-03-16 | E-GEOD-33029 | biostudies-arrayexpress

Integrated multi-omics analysis

Project description:Integrated multi-omics analysis

| PRJNA1069651 | ENA

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Project description:Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrate that SpatialGlue can more accurately resolve spatial domains at a higher resolution across different tissue types and technology platforms, to enable biological insights into cross-modality spatial correlations.

2024-06-07 | GSE263617 | GEO

IgG from systemic sclerosis patients induce a profibrosing and serotype-dependent phenotype in normal dermal fibroblast: a multi-omics study

Project description:This study explored the effect of purified IgG from SSc patients on the phenotype and function of healthy dermal fibroblast (FB) using an innovative multi-omics approach.

2022-06-07 | GSE178299 | GEO

Parallel single cell multi-omics analysis of neonatal skin reveals transitional fibroblast states that restrict differentiation into distinct fates

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

SORC: an integrated spatial omics resource in cancer.

Project description:The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

| S-EPMC10768140 | biostudies-literature

Profiling transcriptional and translational response to anoxia or hypoxia of genes in DNA damage response and repair pathways in human fibroblast and prostate cancer cells using NanoString nCounter.

Project description:Identification of genes in DNA damage response and repair pathways differentially transcribed or translated under anoxia or hypoxia in GM05757 normal human fibroblast cells and DU145 human prostate cancer cells. Comparison of mRNA abundance and translation efficiency of genes in DNA damage response and repair pathways in selected anoxia/hypoxia-treated cells with those in normoxia-treated controls.

2015-07-01 | E-GEOD-52461 | biostudies-arrayexpress

IgG from systemic sclerosis patients induce a profibrosing and serotype-dependent phenotype in normal dermal fibroblast: a multi-omics study

Project description:Autoantibodies (Aab) are frequent in systemic sclerosis (SSc). While recognized as potent biomarkers, their pathogenic role is much debated. This study explored the effect of purified IgG from SSc patients on the phenotype and function of healthy dermal fibroblast (FB) using an innovative multi-omics approach.

2022-06-29 | PXD025885 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data