Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Xenium]

ABSTRACT: Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.

ORGANISM(S): Mus musculus

PROVIDER: GSE269719 | GEO | 2025/06/07

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Visium]

Project description:Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.

2025-06-07 | GSE269622 | GEO

Open-ST: High-resolution spatial transcriptomics in 3D [xenium]

Project description:Open-ST: High-resolution spatial transcriptomics in 3D [xenium]

| PRJNA1097649 | ENA

Open-ST: High-resolution spatial transcriptomics in 3D [xenium]

Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

2024-06-23 | GSE263498 | GEO

Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis [Spatial Transcriptomics]

Project description:Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis [Spatial Transcriptomics]

| PRJNA1028558 | ENA

Xenium data for a comparison of spatial transcriptomics technologies for medulloblastoma cryosections

Project description:10X Genomics Xenium data from human Medulloblastoma samples. The data was acuired in the course of a study performing a comparison of four imaging-based ST methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, and Xenium – together with sequencing-based ST (Visium). These technologies were used to study cryosections of medulloblastoma with extensive nodularity (MBEN), a tumor chosen for its distinct microanatomical features. Our analysis reveals that automated imaging-based ST methods are well suited to delineating the intricate MBEN microanatomy, capturing cell-type-specific transcriptome profiles. We devise approaches to compare the sensitivity and specificity of the different methods together with their unique attributes to guide method selection based on the research aim. Furthermore, we demonstrate how reimaging of slides after the ST analysis can markedly improve cell segmentation accuracy and integrate additional transcript and protein readouts to expand the analytical possibilities and depth of insights. This study highlights key distinctions between various ST technologies and provides a set of parameters for evaluating their performance. Our findings aid in the informed choice of ST methods and delineate approaches for enhancing the resolution and breadth of spatial transcriptomic analyses, thereby contributing to advancing ST applications in solid tumor research.

2025-03-17 | GSE291688 | GEO

PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration [Xenium]

Project description:This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing (scRNA-seq) integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. Our semi-supervised analysis pipeline was applied to examine pancreatic intraepithelial neoplasias (PanIN), the most frequent premalignant lesions that can develop into pancreatic adenocarcinoma (PDAC). Their strict diagnosis on FFPE samples has limited previous characterization of human PanINs within their microenvironment through single-cell approaches. We leverage unbiased whole transcriptome FFPE spatial profiling to enable this characterization in a rare cohort of matched low-grade and high-grade PanIN lesions to track progression and map cellular phenotypes relative to scRNA-seq data of advanced PDAC tumors. We demonstrate that cancer associated fibroblasts (CAF), including antigen-presenting CAFs (apCAF), are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We use these findings to guide panel design to perform single-cell validation with high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our pipeline for spatial multi-omics characterization provides a resource for future PanIN studies. Moreover, our semi-supervised learning framework to spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

2024-05-20 | GSE267680 | GEO

Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed paraffin-embedded tumor samples [Xenium]

Project description:Imaging-based spatial transcriptomics (ST) is evolving as a pivotal technology in studying tumor biology and associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. We use serial 5 m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma samples in tissue microarrays to compare the performance of the ST platforms (CosMx, MERFISH, and Xenium (uni/multi-modal)) in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx, and hematoxylin and eosin staining data. In addition to an objective assessment of automatic cell segmentation and phenotyping, we perform a manual phenotyping evaluation to assess pathologically meaningful comparisons between ST platforms. Here, we show the intricate differences between the ST platforms, reveal the importance of parameters such probe design in determining the data quality, and suggest reliable workflows for accurate spatial profiling and molecular discovery.

2025-06-30 | GSE300007 | GEO

Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis

Project description:Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis

| PRJNA845449 | ENA

Adaptively evolved yeast mutants on galactose shows trade-offs in carbon utilization on glucose.

Project description:Exploring molecular details of carbon utilization trade-offs in galactose-evolved yeast Adaptively evolved yeast mutants on galactose for around 400 generations showed diminished growth and carbon uptake rates on glucose. Genome-scale approaches were applied to characterize the molecular genetic basis of these trade-offs in carbon source utilization. Engineered mutants showing trade-offs in a specific carbon uptake rate between both carbons were used as controls. The transcriptional responses of the evolved mutants were almost identical during growth on both carbon sources. These carbon-independent conserved patterns were clearly observed in specific pathways and genes. Up-regulation of PGM2, a confirmed beneficial genetic change for improving galactose utilization was preserved on both carbons. In addition, HXK1, GLK1 and genes involved in reserve carbohydrate metabolism were up-regulated, while HXK2 was down-regulated. Genes that have a transcription factor binding site for Gis1p, Rph1p, Msn2/4p and Nrg1p were up-regulated. These results indicated changes in the metabolic pathways involved in metabolism of both carbons and in nutrient signaling pathway. The concentration profile of trehalose and glycogen supported these findings. Mutations in RAS2 and ERG5 genes were selected because of their beneficial and neutral effect on galactose utilization, respectively in our previous study. Site-directed mutants containing galactose-beneficial mutations in RAS2 only resulted in a significant decrease in glucose utilization. Integration of all these analyses clearly suggest an antagonistic pleiotropic trade-off in carbon source utilization caused by changes in regulatory region, and we hereby demonstrate how systems biology can be used to gain insight into evolutionary processes at the molecular level. Yeast galactose evolved mutants having improved galactose availability were grown on aerobic batch with glucose as carbon source

2012-07-31 | E-GEOD-39065 | biostudies-arrayexpress

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics

Project description:Spatial proteomics enables in-depth mapping of cellular components and tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, the trade-offs among sampling resolution, throughput, and proteome coverage still limit the applicability of these strategies, especially toward deep proteome profiling with high spatial resolution. Here, we propose PSPro (Proximity labeling for Spatial Proteomics) by leveraging precise antibody-targeted biotinylation at three-dimension scale and efficient affinity purification for MS-based cell-type-specific proteome profiling from single tissue slice. With fine-tuned labeling parameters, PSPro achieves all-at-once cell-type proteome capture with sub-micrometer resolution and shows comparable performance to flow cytometry- and LMD-based proteomic workflows. We apply PSPro to fixed tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. For tissue microenvironments with high heterogeneity, we further incorporate region-resolved LMD into PSPro to facilitate direct comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts traditional “antibody-epitope” paradigm to “antibody-cell type proteome” for spatial biology in a user-friendly manner.

2024-02-02 | MSV000093995 | MassIVE

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