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

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

PROVIDER: PRJNA607949 | ENA |

REPOSITORIES: ENA

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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 [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 [single nuclei]

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

| PRJNA607967 | 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: Not available

| 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

Spatial gene expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons

Project description:Tissue and organ function has been conventionally understood in terms of the interactions among discrete and homogeneous cell types. This approach has proven difficult in neuroscience due to the marked diversity across different neuron classes, but may also be further hampered by prominent within-class variability. Here, we considered a well-defined, canonical neuronal population â hippocampal CA1 pyramidal cells â and systematically examined the extent and spatial rules of transcriptional heterogeneity. Using next-generation RNA sequencing, we identified striking variability in CA1 PCs, such that the differences along the dorsal-ventral axis rivaled differences across distinct pyramidal neuron classes. This variability emerged from a spectrum of continuous expression gradients, producing a profile consistent with a multifarious continuum of cells. This work reveals an unexpected amount of variability within a canonical and narrowly defined neuronal population and suggests that continuous, within-class heterogeneity may be an important feature of neural circuits. Hippocampal RNA profiles were generated by deep sequencing on Illumina HiSeq 2500, with three biological replicates per population

2016-01-05 | E-GEOD-67403 | biostudies-arrayexpress

Spatial gene expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons

Project description:Tissue and organ function has been conventionally understood in terms of the interactions among discrete and homogeneous cell types. This approach has proven difficult in neuroscience due to the marked diversity across different neuron classes, but may also be further hampered by prominent within-class variability. Here, we considered a well-defined, canonical neuronal population – hippocampal CA1 pyramidal cells – and systematically examined the extent and spatial rules of transcriptional heterogeneity. Using next-generation RNA sequencing, we identified striking variability in CA1 PCs, such that the differences along the dorsal-ventral axis rivaled differences across distinct pyramidal neuron classes. This variability emerged from a spectrum of continuous expression gradients, producing a profile consistent with a multifarious continuum of cells. This work reveals an unexpected amount of variability within a canonical and narrowly defined neuronal population and suggests that continuous, within-class heterogeneity may be an important feature of neural circuits.

2016-01-05 | GSE67403 | GEO

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