Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A spatial transcriptomics based Label-free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

ABSTRACT: Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. This study introduces a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. The hMSCs were introduced into a mouse model with lung fibrosis, followed by the manipulation of ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, the method allowed for the examination of molecular changes associated with the spatial distribution of hMSCs. Therefore, our method has the potential to serve as an effective tool for various cell-based therapeutic agents.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE253378 | GEO | 2024/05/01

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Spatial Transcriptomics-based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue

Project description:To elucidate the factors that determine nanomedicine tumor uptake, we developed a molecular marker identification platform by integrating microscopic fluorescence images of nanomedicine distribution with spatial transcriptomics (ST) information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glucose metabolism and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. We expect that our method can be applied to explain the distribution of a wide range of nanomedicines, and furthermore, the data obtained from this analysis can be used in precise utilization of the nanomedicine.

2024-08-04 | GSE196506 | GEO

Project description:spatial distribution

| PRJNA548974 | ENA

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Project description:Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER removes the systematic difference between the ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimations, SDePER imputes for cell type compositions and gene expression at enhanced resolution. We assessed the performance of SDePER and six existing methods using simulations and four real datasets. All results showed that SDePER achieved significantly more accurate and robust results than the existing methods suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.

2024-08-01 | GSE231385 | GEO

Spatiotemporal transcriptional atlas of lung adenocarcinoma from adenocarcinoma in situ to invasive carcinoma [ST]

Project description:To illuminate the evolutionary trajectory of LUAD from AIS to IAC, high-throughput scRNA-seq and ST data were generated and integrated to create a large-scale, single-cell spatiotemporal atlas of LUAD. We compiled a multi-omics atlas of the early-stage LUAD invasion process that reflects the heterogeneity of cancer cells, the competitive polyclonal origin of LUAD, signalling interactions between cancer cells and the TME, and the pseudo-chronological nature of LUAD invasion. The spatial distribution characteristics of LUAD cells revealed the spatial heterogeneity of LUAD and the mechanism of spatial immune escape in LUAD, which provides strong evidence supporting clinical diagnosis and surgical intervention at the single-cell level. The seurat object of ST data are available on https://drive.google.com/drive/folders/1dkGF4kKMbHSm04DKHg6P_y_iTD1pXCH8?usp=sharing.

2021-11-30 | GSE189487 | GEO

Transcriptome profile of Human Mesenchymal Stem Cells with Reduced Senescence and Increased Trilineage Differentiation Ability upon Drug Treatment

Project description:Human Mesenchymal stem cells (hMSCs) are multi-potential cells which are widely used in cell therapy. However, the frequently emerged senescence and decrease of differentiation capabilities limited the broad applications of MSC. In this study, hMSCs were treated by rapamycin, oltipraz, metformin, and vitamin C for the indicated time and the cells were subjected to senescence evaluation and trilineage differentiation. Transcriptomics dataset of hMSCs after drugs treatment was analyzed to interpret biological pathways responsible for the anti-senescence effects. Although four drugs exhibited significant activities in promoting MSC osteogenic differentiation, metformin is the optimal drug to promote trilineage differentiation. GO terms illustrated that the anti-aging effects of drugs were mainly associated with cellular senescence, mitotic and meiosis process. The method established and discussed in this study can be used to study different biological phenotypes upon drug intervention in MSC which will extend the clinical application of hMSCs.

2021-05-06 | GSE165201 | GEO

Spatial transcriptomics analysis of soft tissue sarcomas.

Project description:The tumor microenvironment plays a crucial role in soft tissue sarcoma development and response to therapy. We used spatial transcriptomics to analyze the spatial distribution of malignant, immune, and other stromal cells present within soft tissue sarcomas.

2023-12-12 | GSE212526 | GEO

T-cell receptor sequencing of tumor-infiltrating lymphocytes (TIL) in breast cancer

Project description:The spatial distribution of tumor-infiltrating lymphocytes (TIL) predicts breast cancer outcome and response to systemic therapy, highlighting the importance of an intact tissue structure for characterizing patientsÃ¢Â€Â™ tumors. Here, we present ST-FFPE, a spatial transcriptomics method for the analysis of formalin-fixed paraffin-embedded samples, which opens the possibility of interrogating widely available archival tissue. The method involves extraction, exome capture and sequencing of RNA from different tumor compartments microdissected by laser-capture, and can be used to study the cellular composition of various tumor environments. Focusing on triple-negative breast cancer (TNBC), we characterized T cells, B cells, dendritic cells, fibroblasts and endothelial cells in both stromal and intra-epithelial compartments. We found a highly variable spatial distribution of immune cell subsets among tumors. This analysis revealed that the immune repertoires of intra-epithelial T and B cells were consistently less diverse than those of stromal T and B cells. T-cell receptor (TCR) sequencing confirmed a reduced diversity and revealed higher clonality of intra-epithelial T cells relative to the corresponding stromal T cells. Analysis of the top ten dominant clonotypes in the two compartments showed a majority of shared but also some unique clonotypes both in stromal and intra-epithelial T cells. Hyperexpanded clonotypes were more abundant among intra-epithelial than stromal T cells. These findings validate the ST-FFPE method and suggest an accumulation of antigen-specific T cells within tumor core. Because ST-FFPE is readily applicable for analysis of previously collected tissue samples, it could be useful for rapid assessment of intratumoral cellular heterogeneity in multiple disease and treatment settings.

| EGAS00001007125 | EGA

Effect of electromagnetic fields on the chondrogenic differentiation under microgravity conditions

Project description:A combination therapy of electromagnetic fields (EMF) and simulated microgravity (SMG) has not been examined in regenerative medicine of cartilage. In the present study, a bioreactor system using extremely low-frequency EMF and SMG was applied during the chondrogenic differentiation of human mesenchymal stem cells (hMSCs). It was hypothesized that a beneficial effect of EMF regarding chondrogenesis (COL2A) could be combined with an avoiding effect of SMG regarding hypertrophy (COLXA1) of cartilage. Pellet cultures of hMSCs formed cartilaginous tissue under the addition of growth factors (FGF; TGF-Î²3). Pure SMG reduced COLXA1 expression but also COL2A expression of hMSCs. Pure EMF showed no gene expression changes of hMSCs during chondrogenic differentiation. Combining EMF/SMG resulted in a re-increase of COL2A but did not reach control levels. The COL2A to COLXA1 ratio of combined EMF/SMG was not significantly different from control levels. The combination therapy of EMF/SMG did not significantly improve the chondrogenic potential of hMSCs. chondrogenic differentiation, electromagnetic stimulation-control, 1 timepoint with/without stimulation

2015-12-31 | E-GEOD-57298 | biostudies-arrayexpress

Lactate influences the gene expression profile of human mesenchymal stem cells (hMSC) in a dose dependent manner

Project description:Wounds, especially non healing wounds are characterised by elevated tissue lactate concentrations. Lactate is known for being able to stimulate collagen synthesis and vessel growth. Lately it has been shown that lactate, in vivo, plays an important role in homing of stem cells. With this work we aimed to show the influence of lactate on the gene expressionprofil of human mesenchymal stem cells (hMSC). hMSCs were obtained from bone marrow and characterised with fluorescence-activated cell sorting (FACS) analysis. Subsequently the hMSCs were treated with either 0, 5, 10 and 15 mM lactate (pH 7,4) for 24 hours. RNA Isolation from stimulated hMSCs and controls was performed. The Microarray analysis was performed using Affymetrix HuGene 1.0 ST Gene Chip. Selected targets were subsequently analysed using quantitative real time PCR (RTq-PCR). We were able to show that lactate in moderate concentrations of 5 respectively 10 mM leads to an anti-imflammatory, anti-apoptotic but growth and proliferation promoting gene expression after 24 h. In contrast, high latate concentrations of 15 mM leads to the opposed effect, namely promoting inflammation and apoptosis. Hypoxia induced genes did not not show any significant regulation. Contrary to expectation, we were not able to show any significant regulation of glycolysis associate candiadtes. We were able to show that lactate alters gene expression but does not change the cell phenotype, which might be helpful for further investigations of new treatment strategies for chronic non-healing wounds as well as tumor-therapy and neuronal plasticity. Timecourse experiment with hMSCs treated with 5, 10, 15 mM lactic acid for 1, 3, 7 days. hMSCs without lactate served as controls *** CEL files lost due to hard disk crash

2013-01-09 | E-GEOD-40205 | biostudies-arrayexpress

Spatial transcriptomics revealing host-microbe interactions

Project description:We developed an analysis pipeline that can extract microbial sequences from Spatial Transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline Spatial Meta-transcriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from this novel data that that demonstrated host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively demonstrated the capture efficiency and recall of our methods. This proof of concept work demonstrates the feasibility of Spatial Meta-transcriptomic analysis, and paves the way for further experimental optimization and application.

2022-08-10 | GSE210738 | 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