Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [single nuclei]

ABSTRACT: Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [single nuclei]

PROVIDER: PRJNA607967 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11139124_1.fastq.gz	Fastqsanger.gz
	SRR11139124_2.fastq.gz	Fastqsanger.gz
	SRR11139125_1.fastq.gz	Fastqsanger.gz
	SRR11139125_2.fastq.gz	Fastqsanger.gz
	SRR12145990_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 8

Similar Datasets

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [single nuclei]

Project description:Single-cell transcriptomics from dissociated organs lack information regarding the spatial origin of each cell, which limits their interpretation, particularly in complex and regionally heterogeneous tissues. This is relevant in the kidney, where cell types are exposed to a wide spectrum of cellular microenvironments along the corticomedullary axis, including steep gradients of extracellular osmolality and oxygen tension. Whether kidney single-cell transcriptomes can be exploited to predict spatial origins of cells and to provide physiological readouts of the cellular microenvironment is unknown. Here, we obtained single-cell transcriptomes of mouse kidney tissue from whole organs and from defined kidney zones (cortex, outer and inner medulla) and applied computational methods to reconstruct the spatial position of kidney tubule cells along the corticomedullary axis based on their transcriptomes. Our approach enabled a spatially resolved analysis of gene expression, showed a coordinated activation of osmolality-and hypoxia-associated genes towards the kidney medulla, and predicted that transcriptomes of a given cell type across different kidney zones change gradually rather than being clearly distinct in different anatomical zones. In genetically modified mice with a tubular concentration defect, spatial reconstruction of single-cell transcriptomics and osmogene expression quantitation accurately predicted reduced medullary osmolality. We conclude that our approach, which can be applied to any mouse whole kidney single-cell transcriptomic dataset, uncovers previously underappreciated information regarding spatial origin and microenvironment-dependent cellular states, adding improved readouts to existing and future datasets.

2020-11-26 | GSE145689 | GEO

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [Drop-seq]

2020-11-26 | GSE145688 | GEO

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [bulk RNA-seq]

2020-11-26 | GSE145687 | GEO

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients

Project description:Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients

| PRJNA607949 | ENA

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [Drop-seq]

Project description:Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [Drop-seq]

| PRJNA607962 | ENA

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [bulk RNA-seq]

Project description:Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients [bulk RNA-seq]

| PRJNA607959 | ENA

Kidney Single-cell Transcriptomes Predict Spatial Corticomedullary Gene Expression and Tissue Osmolality Gradients.

Project description:BackgroundSingle-cell transcriptomes from dissociated tissues provide insights into cell types and their gene expression and may harbor additional information on spatial position and the local microenvironment. The kidney's cells are embedded into a gradient of increasing tissue osmolality from the cortex to the medulla, which may alter their transcriptomes and provide cues for spatial reconstruction.MethodsSingle-cell or single-nuclei mRNA sequencing of dissociated mouse kidneys and of dissected cortex, outer, and inner medulla, to represent the corticomedullary axis, was performed. Computational approaches predicted the spatial ordering of cells along the corticomedullary axis and quantitated expression levels of osmo-responsive genes. In situ hybridization validated computational predictions of spatial gene-expression patterns. The strategy was used to compare single-cell transcriptomes from wild-type mice to those of mice with a collecting duct-specific knockout of the transcription factor grainyhead-like 2 (Grhl2CD-/-), which display reduced renal medullary osmolality.ResultsSingle-cell transcriptomics from dissociated kidneys provided sufficient information to approximately reconstruct the spatial position of kidney tubule cells and to predict corticomedullary gene expression. Spatial gene expression in the kidney changes gradually and osmo-responsive genes follow the physiologic corticomedullary gradient of tissue osmolality. Single-nuclei transcriptomes from Grhl2CD-/- mice indicated a flattened expression gradient of osmo-responsive genes compared with control mice, consistent with their physiologic phenotype.ConclusionsSingle-cell transcriptomics from dissociated kidneys facilitated the prediction of spatial gene expression along the corticomedullary axis and quantitation of osmotically regulated genes, allowing the prediction of a physiologic phenotype.

| S-EPMC8054904 | biostudies-literature

Kidney single-cell transcriptomes predict spatial corticomedullary gene expression and tissue osmolality gradients

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

2020-11-26 | GSE145690 | GEO

Placental infection single-cell and single-nuclei maps

Project description:Placental infection single-cell and single-nuclei maps

| PRJEB72863 | ENA

Placental infection single-cell and single-nuclei maps

Project description:This resource comprises a single-cell multi-lineage map of first trimester infected placental cells. We have included data from both uninfected cells and cells infected with three pathogens known to cause maternal and fetal disorders: Plasmodium falciparum, Listeria monocytogenes, and Toxoplasma gondii. We also generated single-nuclei map of infected trophoblasts and their corresponding controls. Furthermore, we created a single-nuclei reference dataset containing information from uninfected primary placental organoids as well as organoids infected with P. falciparum. Additionally, we conducted sequencing at a single-cell level for P. falciparum parasites that were bound to the placenta (pf_b), parasites unbound to the placenta (pf_nb), and parasites that were cultured in vitro (pf_iv).

2024-03-01 | E-MTAB-12795 | biostudies-arrayexpress

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