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

ABSTRACT: Multimodal spatial transcriptomic characterization of mouse kidney injury and repair

PROVIDER: PRJNA1124059 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	SRR29365050_1.fastq.gz	Fastqsanger.gz
	SRR29365050_2.fastq.gz	Fastqsanger.gz
	SRR29365051_1.fastq.gz	Fastqsanger.gz
	SRR29365051_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 31

Similar Datasets

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

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

| PRJNA1123296 | ENA

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

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

| PRJNA1122779 | ENA

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

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 | GSE269719 | GEO

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

2025-06-07 | GSE269622 | GEO

Entospletinib modulates spatial gene expression in kidney immune microenvironments during injury and repair

Project description:Spatial transcriptomic profiling was performed on mouse kidney sections using the NanoString GeoMx Digital Spatial Profiler with the Whole Transcriptome Atlas. Mice were subjected to ischemia-reperfusion injury (IRI) and treated with the SYK inhibitor Entospletinib or vehicle control. Regions of interest were selected from the renal cortex and medulla in two mice per condition at days 1, 7, and 14 post-injury. The dataset captures spatial gene expression dynamics during kidney injury and repair, and the effects of SYK inhibitio

2025-07-07 | GSE299516 | GEO

Multimodal spatial transcriptomic characterization of mouse kidney injury and repair

Project description:This SuperSeries is composed of the SubSeries listed below.

2025-06-07 | GSE269884 | GEO

Entospletinib modulates spatial gene expression in kidney immune microenvironments during injury and repair

Project description:Entospletinib modulates spatial gene expression in kidney immune microenvironments during injury and repair

| PRJNA1274891 | ENA

Cellular and Spatial Drivers of Unresolved Injury Link to Functional Decline in the Human Kidney

Project description:Building upon a foundational Human Kidney resource, we present a comprehensive multi-modal atlas that defines spatially resolved versus unresolved repair states and mechanisms in human kidney disease. Homeostatic interactions between injured kidney epithelium and its surrounding milieu determine successful repair outcomes, while pathogenic signaling promotes unresolved inflammation and fibrosis leading to chronic disease. We integrated multiple single-cell and spatial modalities across ~700 samples from >350 patients (~250 research biopsies), analyzing ~1.7 million cells alongside complementary mouse multi-omic profiles spanning acute-to-chronic injury and aging (>300,000 cells) and spatial transcriptomic analysis of 150 human biopsies. This cross-species atlas delineates functional pathways and druggable targets across the nephron and defines gene regulatory networks and chromatin landscapes governing tubular, fibroblast, and immune cell transitions from injury to either recovery or failed repair states. We identified distinct cellular states associated with specific pathological features that show dynamic distributions between acute kidney injury (AKI) and chronic kidney disease (CKD), organized within unique spatial niches that reveal progression mechanisms from early injury to unresolved disease. Gene regulatory analyses prioritized key transcription factor activities (SOX4, SOX9, NFKB1, REL, KLFs) and their target networks establishing disease states and tissue microenvironments. These regulatory programs were directly linked to clinical outcomes, identifying molecular signatures of recovery and secreted biomarkers predictive of AKI-to-CKD progression, providing a key resource for therapeutic development and precision medicine approaches in kidney disease.

2025-10-09 | GSE308709 | GEO

Multimodal Coregistration and Fusion between Spatial Metabol-omics and Biomedical Imaging

Project description:Spatial omics are increasingly "fused" with classical biomedical imaging modalities to complement them with spatially-resolved molecular profiling, enabling deeper understanding of the biochemical basis of health and diesease. Before the multimodal information can be fused, all contributing imaging data must be spatially aligned through a process called coregistration, which remains a challenge. Here we present a multimodal coregistration framework with great genericity and accuracy for spatial omics data. Specifically, an original dimension reduction algorithm "PseudoRep" is used to bridge cross-modal gaps: PseudoRep can generate a single-channel representation of the highly multiplexed molecular image from spatial omics and ensure that the resulting representation appears "pseudo-unimodal" to the image from the other modality. Then, a Deep Learning-based transformation algorithm "DeepReg" is adopted to accurately model nonlinear tissue deformations due to anatomical variations or sample manipulations. Our method demonstrated its versatility by coregistering mass spectrometry imaging-mediated spatial metabolomics data with diverse modalities (magnetic resonance imaging, brain atlas, microscopy, and spatial transcriptomics) and reduced 38\%\textemdash69\% of coregistration errors compared to existing methods. Notably, we open-source a toolbox to implement both the coregistration and its downstream tasks (co-expression analysis, anatomical annotation, and pansharpening), which is expected to greatly facilitate the democratization of multimodal fusion studies in the spatial omics community.

2023-03-06 | PXD040604 |

Spatial Multimodal Analysis: MALDI-MSI and Spatial Transcriptomics within the same tissue section

Project description:We present a spatial omics approach that merges and expands the capabilities of independently performedin situassays on a single tissue section. Our spatial multimodal analysis combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinson’s disease.

2023-05-21 | GSE232910 | 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