Kidney Single Cell Study
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001002325 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:We intend to use single cell transcriptome analysis to explore the heterogenity of different cell types within the kidney. .
This dataset contains all the data available for this study on 2018-08-20.
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Project description:We report the first use of genome-edited human kidney organoids, combined with single-cell transcriptomics, to study APOL1 risk variants at the native genomic locus in different nephron cell types. This approach captures interferon-mediated induction of APOL1 gene expression and cellular dedifferentiation with a secondary insult“second hit” of endoplasmic reticulum stress.
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Project description:We analyzed single cell transcriptomes over 80,000 cells isolated from 65 organoids differentiated from iPSCs and ESCs using two different protocols. We find that both protocols generate kidney organoids that contain a diverse range of kidney cells at differing ratios as well as non-renal cell types. We reconstructed lineage relationships during organoid differentiation through pseudotemporal ordering, and identified transcription factor networks associated with fate decisions. When comparing to adult human kidney, we reveal immaturity of all kidney organoid cell types. These results define impressive kidney organoid cell diversity, identify incomplete differentiation as a major roadblock for current directed differentiation protocols and provide a human adult kidney snRNA-seq dataset against which to benchmark future progress.
2018-10-13 | GSE118184 | GEO
Project description:Kidney development is a complex process involving multiple interacting and transforming cell types. These cell types were recently characterized using the Drop-seq single-cell technology for measuring gene expression from many thousands of individual cells. However, many genes can also be alternatively spliced and this creates an additional layer of cellular heterogeneity that cannot be measured with the Drop-seq technology. This study describes the use of full transcript length single-cell RNA sequencing to characterize alternative splicing in the developing mouse fetal kidney; in particular, the identification of genes that are alternatively spliced during the transition from mesenchymal to epithelial cell states, as well as their splicing regulators. These results improve our understanding of the molecular mechanisms involved in kidney development.
2020-07-27 | GSE146988 | GEO
Project description:Kidney development is a complex process involving multiple interacting and transforming cell types. These cell types were recently characterized using the Drop-seq single-cell technology for measuring gene expression from many thousands of individual cells. However, many genes can also be alternatively spliced and this creates an additional layer of cellular heterogeneity that cannot be measured with the Drop-seq technology. This study describes the use of full transcript length single-cell RNA sequencing to characterize alternative splicing in the developing mouse fetal kidney; in particular, the identification of genes that are alternatively spliced during the transition from mesenchymal to epithelial cell states, as well as their splicing regulators. These results improve our understanding of the molecular mechanisms involved in kidney development.
2020-07-27 | GSE147525 | GEO
Project description:We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq, 10X) and computational analysis of human kidney allograft biopsies will reveal new cell types and cell states and yield insights to personalize the care of transplant recipients.
2022-04-19 | GSE151671 | GEO
Project description:Sepsis is a life-threatening condition that often leads to multi-organ dysfunction and energy dysruption. To explore the cellular response of the mouse kidney during sepsis, we employed snRNA sequencing to profile differentially expressed genes (DEGs) across various kidney cell types.
2025-05-26 | GSE275526 | GEO
Project description:In this proof-of-concept study, spatial transcriptomics combined with public single-cell RNA sequencing data were used to explore the potential of this technology to study kidney allograft rejection. We aimed to map gene expression patterns within diverse pathological states by examining biopsies classified across non-rejection, T cell-mediated acute rejection, and interstitial fibrosis and tubular atrophy (IFTA). Our results revealed distinct immune cell signatures, including those of T and B lymphocytes, monocytes, mast cells, and plasma cells, and their spatial organization within the renal interstitium. We also mapped chemokine receptors and ligands to study immune-cell migration and recruitment. Finally, our analysis demonstrated differential spatial enrichment of transcription signatures associated with kidney allograft rejection across various biopsy regions. Interstitium regions displayed higher enrichment scores for rejection-associated gene expression patterns than did tubular areas, which had negative scores. This implies that these signatures are primarily driven by processes unfolding in the renal interstitium. Overall, this study highlights the value of spatial transcriptomics for revealing cellular heterogeneity and immune signatures in renal transplant biopsies, and demonstrates its potential for studying the molecular and cellular mechanisms associated with rejection. However, certain limitations must be borne in mind regarding the development and future applications of this technology.
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Project description:Our team utilized single-cell RNA sequencing and spatial multi-omics to explore the spatial landscape of kidney in mice with hyperuricemia at single-cell level
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Project description:Diabetic kidney disease (DKD), a common and devastating microvascular complication of diabetes, is the leading cause of end-stage renal disease (ESRD). Since mechanisms of kidney injury in DKD were largely unknown, we performed single-cell RNA sequencing (scRNA-seq) on human kidneys collected from 3 DKD and 3 normal samples using 10×Genomics. In our study, a total of 51315 cells were enrolled for analyses and nine kidney cell types and seven immune cell types were identified. The cell-type-specific changes in gene expression and signaling pathways of podocyte, mesangial cells, endothelial cells, proximal tubule and macrophages indicate abnormal regulation associated with inflammation, apoptosis, oxidative stress, extracelluar matrix accumulation, and immune activation. In particular, we show that podocytes and renal tubular epithelial cells have a tremendous capacity to regenerate, which is involved in the repairment of injury. And extracellular vesicles, an important mediator of intercellular communication, might play a vital role for this progress. Besides, we identified new candidate transcription factors responsible for the progression of DKD. We also revealed a M1-M2 hybrid pattern, in which M1 and M2 are coupled activation in macrophages of DKD. Furthermore, we demonstrated a complex intercellular interaction between kidney cells and kidney cells or kidney cells and immune cells. Thus, our study will further the understanding of DKD pathogenesis and provide novel therapeutic targets for its treatment in the future.
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