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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

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			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

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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.

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