Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single cell and spatial transcriptomic profile of whole CLAD and donor control lung tissue

ABSTRACT: Single cell and spatial transcriptomic profile of whole CLAD and donor control lung tissue

PROVIDER: PRJNA930324 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Single cell and spatial transcriptomic profile of whole CLAD and donor control lung tissue

Project description:Chronic lung allograft dysfunction (CLAD) is the leading cause of death in lung transplant recipients. CLAD is characterized clinically by a persistent decline in pulmonary function and histologically by the development of airway centered fibrosis known as bronchiolitis obliterans. We performed single-cell sequencing and spatial transcriptomic analysis of explanted tissues from human lung recipients with CLAD and donor control lungs.

2023-03-21 | GSE224210 | GEO

A spatial transcriptomics study of splenic marginal zone lymphomas (SMZL) and spleen healthy donor

Project description:Purpose: Spatial transcriptomics was used to characterize in depth splenic samples obtained from splenic marginal zone lymphomas Methods: 4 frozen samples of spleen from SMZL patients, and 1 healthy donor were selected and thawed for performing a 10X genomics Visium Spatial assay. Results: Tumor cells formed mainly patient specific-B cell clusters, while T-cells from the 3 patients and healthy donors clustered together. Conclusion: Single RNA-Seq unveils inter- and intra-patient heterogeneity.

2024-09-17 | GSE228056 | GEO

Donor lung tissues: Control vs. Primary graft dysfunction (PGD)

Project description:Primary graft dysfunction (PGD) continues to be a major cause of early death after lung transplantation. Moreover, there remains a lack of accurate pre-transplant molecular markers for predicting PGD. To identify distinctive gene expression signatures associated with PGD, we profiled human donor lungs using microarray technology prior to the graft implantation. The genomic profiles of 10 donor lung samples from patients who subsequently developed clinically defined severe PGD were compared with 16 case-matched donor lung samples from those who had a favorable outcome without PGD. Matched factors used were: recipient age (± 10 years), recipient gender, recipient lung disease, and type of transplantation (single or bilateral). Keywords: Observational case-control study

2008-09-15 | GSE9102 | GEO

In-depth Spatial Proteomics for Whole Tissue Profiling by Sparse Sampling Strategy

Project description:To achieve a deep quantitative proteomic profiling of whole tissue with the spatial localization preserved, we designed an untargeted spatial proteomics workflow with a sparse sampling strategy. We combined a computational approach and MS-based proteomics analysis to localize high-throughput proteome mapping within a tissue by microdissecting the slices into a series of parallel strips with different angles, that allow subsequent image reconstruction. We successfully reconstructed a whole-tissue mapping in a mouse brain, consisting of 8,645 protein distributions and expressions with a spatial resolution of approx. 300 μm.

2024-12-31 | PXD059321 |

Donor lung tissues: Control vs. Primary graft dysfunction (PGD)

Project description:Primary graft dysfunction (PGD) continues to be a major cause of early death after lung transplantation. Moreover, there remains a lack of accurate pre-transplant molecular markers for predicting PGD. To identify distinctive gene expression signatures associated with PGD, we profiled human donor lungs using microarray technology prior to the graft implantation. The genomic profiles of 10 donor lung samples from patients who subsequently developed clinically defined severe PGD were compared with 16 case-matched donor lung samples from those who had a favorable outcome without PGD. Matched factors used were: recipient age (Â± 10 years), recipient gender, recipient lung disease, and type of transplantation (single or bilateral). Keywords: Observational case-control study Matched case-control observational study: 10 primary graft dysfunction cases vs 16 Good outcome cases. One replicate per array.

2008-09-15 | E-GEOD-9102 | biostudies-arrayexpress

Spatial transcriptomics of fetal skin

Project description:Spatial organization of different cell types within prenatal skin across various anatomical sites is not well understood. To address this, here we have generated spatial transcriptomics data from prenatal facial and abdominal skin obtained from a donor at 10 post conception weeks. This in combination with our prenatal skin scRNA-seq dataset has helped us map the location of various identified cell types.

2024-06-04 | E-MTAB-13024 | biostudies-arrayexpress

Spatial transcriptomics of whole mouse lung cells

Project description:We report the distribution of mRNA in fibrotic lung sections on spatial trancriptomics sequencing.

2022-03-02 | GSE183683 | GEO

Spatial transcriptomics of whole mouse lung cells

Project description:Spatial transcriptomics of whole mouse lung cells

| PRJNA761678 | ENA

Spatial transcriptomic maps of whole mouse embryos

Project description:Spatial transcriptomic maps of whole mouse embryos

| PRJNA810020 | ENA

Cell type inference in human lung tissue by domain adaptation of single-cell and spatial transcriptomic data

Project description:We developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks, and applied to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. CellDART elucidated the cell type predominance defined by the human lung cell atlas across the human lung tissue compartments and it corresponded to the known prevalent cell types.

2022-06-15 | GSE172416 | GEO

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