Explore the vast range of titles available.

The integration of single cell transcriptome and chromatin accessibility datasets enables a deeper understanding of cell heterogeneity. We performed single nucleus ATAC (snATAC-seq) and RNA (snRNA-seq) sequencing to generate paired, cell-type-specific chromatin accessibility and transcriptional profiles of the adult human kidney. We demonstrate that snATAC-seq is comparable to snRNA-seq in the assignment of cell identity and can further refine our understanding of functional heterogeneity in the nephron. The majority of differentially accessible chromatin regions are localized to promoters and a significant proportion are closely associated with differentially expressed genes. Cell-type-specific enrichment of transcription factor binding motifs implicates the activation of NF-κB that promotes VCAM1 expression and drives transition between a subpopulation of proximal tubule epithelial cells. Our multi-omics approach improves the ability to detect unique cell states within the kidney and redefines cellular heterogeneity in the proximal tubule and thick ascending limb. Single cell transcriptomic and epigenomic sequencing of human kidney highlight diverse cell types and states. These findings help characterize a novel population of injured proximal tubule cells and illustrate the power of multi-omic approaches to characterizing human tissue.

Diabetic nephropathy is characterized by damage to both the glomerulus and tubulointerstitium, but relatively little is known about accompanying cell-specific changes in gene expression. We performed unbiased single-nucleus RNA sequencing (snRNA-seq) on cryopreserved human diabetic kidney samples to generate 23,980 single-nucleus transcriptomes from 3 control and 3 early diabetic nephropathy samples. All major cell types of the kidney were represented in the final dataset. Side-by-side comparison demonstrated cell-type–specific changes in gene expression that are important for ion transport, angiogenesis, and immune cell activation. In particular, we show that the diabetic thick ascending limb, late distal convoluted tubule, and principal cells all adopt a gene expression signature consistent with increased potassium secretion, including alterations in Na⁺/K⁺-ATPase, WNK1, mineralocorticoid receptor, and NEDD4L expression, as well as decreased paracellular calcium and magnesium reabsorption. We also identify strong angiogenic signatures in glomerular cell types, proximal convoluted tubule, distal convoluted tubule, and principal cells. Taken together, these results suggest that increased potassium secretion and angiogenic signaling represent early kidney responses in human diabetic nephropathy.

Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans- regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state. A profibrotic, proinflammatory kidney cell population has been identified as a driver of chronic kidney disease. Here, authors generate a human kidney single cell multiomic dataset and apply a regularised regression approach to identify transcription factors underpinning this cell population.

Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease characterized by progressive expansion of kidney cysts. To better understand the cell types and states driving ADPKD progression, we analyze eight ADPKD and five healthy human kidney samples, generating single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. Activation of proinflammatory, profibrotic signaling pathways are driven by proximal tubular cells with a failed repair transcriptomic signature, proinflammatory fibroblasts and collecting duct cells. We identify GPRC5A as a marker for cyst-lining collecting duct cells that exhibits increased transcription factor binding motif availability for NF-κB, TEAD, CREB and retinoic acid receptors. We identify and validate a distal enhancer regulating GPRC5A expression containing these motifs. This single cell multiomic analysis of human ADPKD reveals previously unrecognized cellular heterogeneity and provides a foundation to develop better diagnostic and therapeutic approaches. Autosomal dominant polycystic kidney disease (ADPKD) is a complicated disease that involves numerous cell types. Here the authors used a multiomics approach consisting of single nucleus transcriptomes and epigenomes to redefine cell states in ADPKD and to dissect the cellular interactions and molecular mechanisms of ADPKD.

Abstract Cues associated with intoxication can elicit cravings for alcohol, leading to increased consumption and relapse in people recovering from alcohol use disorder. Petruccelli et al. employed genetic tools in... Repeated alcohol experiences can produce long-lasting memories for sensory cues associated with intoxication. These memories can problematically trigger relapse in individuals recovering from alcohol use disorder (AUD). The molecular mechanisms by which ethanol changes memories to become long-lasting and inflexible remain unclear. New methods to analyze gene expression within precise neuronal cell types can provide further insight toward AUD prevention and treatment. Here, we used genetic tools in Drosophila melanogaster to investigate the lasting consequences of ethanol on transcription in memory-encoding neurons. Drosophila rely on mushroom body (MB) neurons to make associative memories, including memories of ethanol-associated sensory cues. Differential expression analyses revealed that distinct transcripts, but not genes, in the MB were associated with experiencing ethanol alone compared to forming a memory of an odor cue associated with ethanol. Adult MB-specific knockdown of spliceosome-associated proteins demonstrated the necessity of RNA-processing in ethanol memory formation. These findings highlight the dynamic, context-specific regulation of transcription in cue-encoding neurons, and the lasting effect of ethanol on transcript usage during memory formation.

Cues associated with intoxication can elicit cravings for alcohol, leading to increased consumption and relapse in people recovering from alcohol use disorder. Petruccelli et al. employed genetic tools in... Repeated alcohol experiences can produce long-lasting memories for sensory cues associated with intoxication. These memories can problematically trigger relapse in individuals recovering from alcohol use disorder (AUD). The molecular mechanisms by which ethanol changes memories to become long-lasting and inflexible remain unclear. New methods to analyze gene expression within precise neuronal cell types can provide further insight toward AUD prevention and treatment. Here, we used genetic tools in Drosophila melanogaster to investigate the lasting consequences of ethanol on transcription in memory-encoding neurons. Drosophila rely on mushroom body (MB) neurons to make associative memories, including memories of ethanol-associated sensory cues. Differential expression analyses revealed that distinct transcripts, but not genes, in the MB were associated with experiencing ethanol alone compared to forming a memory of an odor cue associated with ethanol. Adult MB-specific knockdown of spliceosome-associated proteins demonstrated the necessity of RNA-processing in ethanol memory formation. These findings highlight the dynamic, context-specific regulation of transcription in cue-encoding neurons, and the lasting effect of ethanol on transcript usage during memory formation.

Acute kidney injury (AKI) causes epithelial damage followed by subsequent repair. While successful repair restores kidney function, this process is often incomplete and can lead to chronic kidney disease (CKD) in a process called failed repair. To better understand the epigenetic reprogramming driving this AKI-to-CKD transition we generated a single nucleus multiomic atlas for the full mouse AKI time course, consisting of ~280,000 single nucleus transcriptomes and epigenomes. We reveal cell-specific dynamic alterations in gene regulatory landscapes reflecting especially activation of proinflammatory pathways. We further generated single nucleus multiomic data from four human AKI samples including validation by genome-wide identification of NF-kB binding sites. A regularized regression analysis identifies key regulators involved in both successful and failed repair cell fate, identifying the transcription factor CREB5 as a regulator of both successful and failed tubular repair that also drives proximal tubule cell proliferation after injury. Our interspecies multiomic approach provides a foundation to comprehensively understand cell states in AKI.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to end-stage kidney disease. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single nucleus multimodal atlas of an orthologous mouse PKD model at early, mid and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalogue differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to end-stage kidney disease. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single nucleus multimodal atlas of an orthologous mouse PKD model at early, mid and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalogue differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.

Abstract Repeated alcohol experiences can produce long-lasting memories for sensory cues associated with intoxication. These memories can ultimately trigger relapse in individuals recovering from alcohol use disorder (AUD). The molecular mechanisms by which alcohol changes memories to become long-lasting and inflexible remain unclear. New methods to analyze gene expression within precise neuronal cell-types can provide further insight towards AUD prevention and treatment. Here, we employed genetic tools in Drosophila melanogaster to investigate the lasting consequences of ethanol on transcription in memory-encoding neurons. Drosophila rely on mushroom body (MB) neurons to make associative memories, including memories of ethanol-associated sensory cues. Differential expression analyses found that distinct transcripts, but not genes, in the MB were associated with experiencing ethanol alone compared to forming a memory of an odor cue associated with ethanol. These findings reveal the dynamic and highly context-specific regulation of splicing associated with encoding behavioral experiences. Our data thus demonstrate that alcohol can have lasting effects on transcription and RNA processing during memory formation, and identify new transcript targets for future AUD and addiction investigation.

Chronic disease processes are marked by cell-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We developed a regularized regression approach, RENIN, (Regulatory Network Inference) to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generated a single nucleus multiomic dataset from seven adult human kidney biopsies and applied RENIN to study drivers of a failed injury response associated with kidney disease. We demonstrate that RENIN is highly effective tool at predicting key cis- and trans-regulatory elements.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://www.github.com/nledru/renin