Intranasal immunization with a vaccinia virus vaccine vector expressing pre-fusion stabilized SARS-CoV-2 spike fully protected mice against lethal challenge with the heavily mutated mouse-adapted SARS2-N501Y MA30 strain of SARS-CoV-2

An evolutionarily conserved olfactory receptor is required for sex differences in blood pressure

Sex differences in blood pressure are well-established, with premenopausal women having lower blood pressure than men by ~10 millimeters of mercury; however, the underlying mechanisms are not fully understood. We report here that sex differences in blood pressure are absent in olfactory receptor 558 knockout (KO) mice. Olfr558 localizes to renin-positive cells in the kidney and to vascular smooth muscle cells. Female KOs exhibit increased blood pressure and increased pulse wave velocity. In contrast, male KO mice have decreased renin expression and activity, altered vascular reactivity, and decreased diastolic pressure. A rare OR51E1 (human ortholog) missense variant has a statistically significant sex interaction effect with diastolic blood pressure, increasing diastolic blood pressure in women but decreasing it in men. In summary, our findings demonstrate an evolutionarily conserved role for OLFR558/OR51E1 to mediate sex differences in blood pressure.

Mosaic loss of Y chromosome is associated with aging and epithelial injury in chronic kidney disease

BACKGROUND

Mosaic loss of Y chromosome (LOY) is the most common chromosomal alteration in aging men. Here, we use single-cell RNA and ATAC sequencing to show that LOY is present in the kidney and increases with age and chronic kidney disease.

RESULTS

The likelihood of a cell having LOY varies depending on its location in the nephron. Cortical epithelial cell types have a greater proportion of LOY than medullary or glomerular cell types, which may reflect their proliferative history. Proximal tubule cells are the most abundant cell type in the cortex and are susceptible to hypoxic injury. A subset of these cells acquires a pro-inflammatory transcription and chromatin accessibility profile associated with expression of HAVCR1, VCAM1, and PROM1. These injured epithelial cells have the greatest proportion of LOY and their presence predicts future kidney function decline. Moreover, proximal tubule cells with LOY are more likely to harbor additional large chromosomal gains and express pro-survival pathways. Spatial transcriptomics localizes injured proximal tubule cells to a pro-fibrotic microenvironment where they adopt a secretory phenotype and likely communicate with infiltrating immune cells.

CONCLUSIONS

We hypothesize that LOY is an indicator of increased DNA damage and potential marker of cellular senescence that can be applied to single-cell datasets in other tissues.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

The aging kidney is characterized by tubuloinflammaging, a phenotype associated with MHC-II gene expression

Introduction

Even during physiologic aging, the kidney experiences a loss of mass and a progressive functional decline. This is clinically relevant as it leads to an increased risk of acute and chronic kidney disease. The kidney tubular system plays an important role in the underlying aging process, but the involved cellular mechanisms remain largely elusive.

Methods

Kidneys of 3-, 12- and 24-month-old male C57BL/6J mice were used for RNA sequencing, histological examination, immunostaining and RNA-in-situ-hybridization. Single cell RNA sequencing data of differentially aged murine and human kidneys was analyzed to identify age-dependent expression patterns in tubular epithelial cells. Senescent and non-senescent primary tubular epithelial cells from mouse kidney were used for in vitro experiments.

Results

During normal kidney aging, tubular cells adopt an inflammatory phenotype, characterized by the expression of MHC class II related genes. In our analysis of bulk and single cell transcriptional data we found that subsets of tubular cells show an age-related expression of Cd74, H2-Eb1 and H2-Ab1 in mice and CD74, HLA-DQB1 and HLADRB1 in humans. Expression of MHC class II related genes was associated with a phenotype of tubular cell senescence, and the selective elimination of senescent cells reversed the phenotype. Exposure to the Cd74 ligand MIF promoted a prosenescent phenotype in tubular cell cultures.

Discussion

Together, these data suggest that during normal renal aging tubular cells activate a program of 'tubuloinflammaging', which might contribute to age-related phenotypical changes and to increased disease susceptibility.

A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease

Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.

A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics.

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3 , KLF6 , and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1 . Further, combined perturbation of ELF3 , KLF6 , and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis

The human kidney is a complex organ with various cell types that are intricately organized to perform key physiological functions and maintain homeostasis. New imaging modalities, such as mesoscale and highly multiplexed fluorescence microscopy, are increasingly being applied to human kidney tissue to create single-cell resolution data sets that are both spatially large and multidimensional. These single-cell resolution high-content imaging data sets have great potential to uncover the complex spatial organization and cellular makeup of the human kidney. Tissue cytometry is a novel approach used for the quantitative analysis of imaging data; however, the scale and complexity of such data sets pose unique challenges for processing and analysis. We have developed the Volumetric Tissue Exploration and Analysis (VTEA) software, a unique tool that integrates image processing, segmentation, and interactive cytometry analysis into a single framework on desktop computers. Supported by an extensible and open-source framework, VTEA's integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses, for hyperdimensional large-scale imaging data sets. These novel capabilities enable the analysis of mesoscale 2- and 3-dimensional multiplexed human kidney imaging data sets (such as co-detection by indexing and 3-dimensional confocal multiplexed fluorescence imaging). We demonstrate the utility of this approach in identifying cell subtypes in the kidney on the basis of labels, spatial association, and their microenvironment or neighborhood membership. VTEA provides an integrated and intuitive approach to decipher the cellular and spatial complexity of the human kidney and complements other transcriptomics and epigenetic efforts to define the landscape of kidney cell types.

Cultured Renal Proximal Tubular Epithelial Cells Resemble a Stressed/Damaged Kidney While Supporting BK Virus Infection

BK virus (BKV; human polyomavirus 1) infections are asymptomatic in most individuals, and the virus persists throughout life without harm. However, BKV is a threat to transplant patients and those with immunosuppressive disorders. Under these circumstances, the virus can replicate robustly in proximal tubule epithelial cells (PT). Cultured renal proximal tubule epithelial cells (RPTE) are permissive to BKV and have been used extensively to characterize different aspects of BKV infection. Recently, lines of hTERT-immortalized RPTE have become available, and preliminary studies indicate they support BKV infection as well. Our results indicate that BKV infection leads to a similar response in primary and immortalized RPTE. In addition, we examined the patterns of global gene expression of primary and immortalized RPTE and compared them with uncultured PT freshly dissociated from human kidney. As expected, PT isolated from the healthy kidney express a number of differentiation-specific genes that are associated with kidney function. However, the expression of most of these genes is absent or repressed in cultured RPTE. Rather, cultured RPTE exhibit a gene expression profile indicative of a stressed or injured kidney. Inoculation of cultured RPTE with BKV results in the suppression of many genes associated with kidney stress. In summary, this study demonstrated similar global gene expression patterns and responses to BKV infection between primary and immortalized RPTE. Moreover, results from bulk transcriptome sequencing (RNA-seq) and SCT experiments revealed distinct transcriptomic signatures representing cell injury and stress in primary RPTE in contrast to the uncultured, freshly dissociated PT from human kidney. IMPORTANCE Cultured primary human cells provide powerful tools for the study of viral infectious cycles and host virus interactions. In the case of BKV-associated nephropathy, viral replication occurs primarily in the proximal tubule epithelia in the kidney. Consequently, cultured primary and immortalized renal proximal tubule epithelial cells (RPTE) are widely used to study BKV infection. In this work, using bulk and single-cell transcriptomics, we found that primary and immortalized RPTE responded similarly to BKV infection. However, both uninfected primary and immortalized RPTE have gene expression profiles that are markedly different from healthy proximal tubule epithelia isolated directly from human kidney without culture. Cultured RPTE are in a gene expression state indicative of an injured or stressed kidney. These results raise the possibility that BKV replicates preferentially in injured or stressed kidney epithelial cells during nephropathy.

SMDB: a Spatial Multimodal Data Browser

Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.

Longitudinal biomarkers and kidney disease progression after acute kidney injury

BACKGROUNDLongitudinal investigations of murine acute kidney injury (AKI) suggest that injury and inflammation may persist long after the initial insult. However, the evolution of these processes and their prognostic values are unknown in patients with AKI.METHODSIn a prospective cohort of 656 participants hospitalized with AKI, we measured 7 urine and 2 plasma biomarkers of kidney injury, inflammation, and tubular health at multiple time points from the diagnosis to 12 months after AKI. We used linear mixed-effect models to estimate biomarker changes over time, and we used Cox proportional hazard regressions to determine their associations with a composite outcome of chronic kidney disease (CKD) incidence and progression. We compared the gene expression kinetics of biomarkers in murine models of repair and atrophy after ischemic reperfusion injury (IRI).RESULTSAfter 4.3 years, 106 and 52 participants developed incident CKD and CKD progression, respectively. Each SD increase in the change of urine KIM-1, MCP-1, and plasma TNFR1 from baseline to 12 months was associated with 2- to 3-fold increased risk for CKD, while the increase in urine uromodulin was associated with 40% reduced risk for CKD. The trajectories of these biological processes were associated with progression to kidney atrophy in mice after IRI.CONCLUSIONSustained tissue injury and inflammation, and slower restoration of tubular health, are associated with higher risk of kidney disease progression. Further investigation into these ongoing biological processes may help researchers understand and prevent the AKI-to-CKD transition.FUNDINGNIH and NIDDK (grants U01DK082223, U01DK082185, U01DK082192, U01DK082183, R01DK098233, R01DK101507, R01DK114014, K23DK100468, R03DK111881, K01DK120783, and R01DK093771).

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.

Phosphatidylinositol metabolism of the renal proximal tubule S3 segment is disturbed in response to diabetes

Diabetes is a main risk factor for kidney disease, causing diabetic nephropathy in close to half of all patients with diabetes. Metabolism has recently been identified to be decisive in cell fate decisions and repair. Here we used mass spectrometry imaging (MSI) to identify tissue specific metabolic dysregulation, in order to better understand early diabetes-induced metabolic changes of renal cell types. In our experimental diabetes mouse model, early glomerular glycocalyx barrier loss and systemic metabolic changes were observed. In addition, MSI targeted at small molecule metabolites and glycero(phospho)lipids exposed distinct changes upon diabetes in downstream nephron segments. Interestingly, the outer stripe of the outer medullar proximal tubular segment (PT_S3) demonstrated the most distinct response compared to other segments. Furthermore, phosphatidylinositol lipid metabolism was altered specifically in PT_S3, with one of the phosphatidylinositol fatty acid tails being exchanged from longer unsaturated fatty acids to shorter, more saturated fatty acids. In acute kidney injury, the PT_S3 segment and its metabolism are already recognized as important factors in kidney repair processes. The current study exposes early diabetes-induced changes in membrane lipid composition in this PT_S3 segment as a hitherto unrecognized culprit in the early renal response to diabetes.

Fibrosis in Pathology of Heart and Kidney: From Deep RNA-Sequencing to Novel Molecular Targets

Diseases of the heart and the kidney, including heart failure and chronic kidney disease, can dramatically impair life expectancy and the quality of life of patients. The heart and kidney form a functional axis; therefore, functional impairment of 1 organ will inevitably affect the function of the other. Fibrosis represents the common final pathway of diseases of both organs, regardless of the disease entity. Thus, inhibition of fibrosis represents a promising therapeutic approach to treat diseases of both organs and to resolve functional impairment. However, despite the growing knowledge in this field, the exact pathomechanisms that drive fibrosis remain elusive. RNA-sequencing approaches, particularly single-cell RNA-sequencing, have revolutionized the investigation of pathomechanisms at a molecular level and facilitated the discovery of disease-associated cell types and mechanisms. In this review, we give a brief overview over the evolution of RNA-sequencing techniques, summarize most recent insights into the pathogenesis of heart and kidney fibrosis, and discuss how transcriptomic data can be used, to identify new drug targets and to develop novel therapeutic strategies.

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Single cell sequencing technologies have rapidly advanced in the last decade and are increasingly applied to gain unprecedented insights by deconstructing complex biology to its fundamental unit, the individual cell. First developed for measurement of gene expression, single cell sequencing approaches have evolved to allow simultaneous profiling of multiple additional features, including chromatin accessibility within the nucleus and protein expression at the cell surface. These multi-omic approaches can now further be applied to cells in situ, capturing the spatial context within which their biology occurs. To extract insights from these complex datasets, new computational tools have facilitated the integration of information across different data types and the use of machine learning approaches. Here, we summarize current experimental and computational methods for generation and integration of single cell multi-omic datasets. We focus on opportunities for multi-omic single cell sequencing to augment therapeutic development for kidney disease, including applications for biomarkers, disease stratification and target identification.

Lineage Tracing and Single-Nucleus Multiomics Reveal Novel Features of Adaptive and Maladaptive Repair after Acute Kidney Injury

SIGNIFICANCE STATEMENT

Understanding the mechanisms underlying adaptive and maladaptive renal repair after AKI and their long-term consequences is critical to kidney health. The authors used lineage tracing of cycling cells and single-nucleus multiomics (profiling transcriptome and chromatin accessibility) after AKI. They demonstrated that AKI triggers a cell-cycle response in most epithelial and nonepithelial kidney cell types. They also showed that maladaptive proinflammatory proximal tubule cells (PTCs) persist until 6 months post-AKI, although they decreased in abundance over time, in part, through cell death. Single-nucleus multiomics of lineage-traced cells revealed regulatory features of adaptive and maladaptive repair. These included activation of cell state-specific transcription factors and cis-regulatory elements, and effects in PTCs even after adaptive repair, weeks after the injury event.

BACKGROUND

AKI triggers a proliferative response as part of an intrinsic cellular repair program, which can lead to adaptive renal repair, restoring kidney structure and function, or maladaptive repair with the persistence of injured proximal tubule cells (PTCs) and an altered kidney structure. However, the cellular and molecular understanding of these repair programs is limited.

METHODS

To examine chromatin and transcriptional responses in the same cell upon ischemia-reperfusion injury (IRI), we combined genetic fate mapping of cycling ( Ki67+ ) cells labeled early after IRI with single-nucleus multiomics-profiling transcriptome and chromatin accessibility in the same nucleus-and generated a dataset of 83,315 nuclei.

RESULTS

AKI triggered a broad cell cycle response preceded by cell type-specific and global transcriptional changes in the nephron, the collecting and vascular systems, and stromal and immune cell types. We observed a heterogeneous population of maladaptive PTCs throughout proximal tubule segments 6 months post-AKI, with a marked loss of maladaptive cells from 4 weeks to 6 months. Gene expression and chromatin accessibility profiling in the same nuclei highlighted differences between adaptive and maladaptive PTCs in the activity of cis-regulatory elements and transcription factors, accompanied by corresponding changes in target gene expression. Adaptive repair was associated with reduced expression of genes encoding transmembrane transport proteins essential to kidney function.

CONCLUSIONS

Analysis of genome organization and gene activity with single-cell resolution using lineage tracing and single-nucleus multiomics offers new insight into the regulation of renal injury repair. Weeks to months after mild-to-moderate IRI, maladaptive PTCs persist with an aberrant epigenetic landscape, and PTCs exhibit an altered transcriptional profile even following adaptive repair.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

SGLT2 inhibitors mitigate kidney tubular metabolic and mTORC1 perturbations in youth-onset type 2 diabetes

The molecular mechanisms of sodium-glucose cotransporter-2 (SGLT2) inhibitors (SGLT2i) remain incompletely understood. Single-cell RNA sequencing and morphometric data were collected from research kidney biopsies donated by young persons with type 2 diabetes (T2D), aged 12 to 21 years, and healthy controls (HCs). Participants with T2D were obese and had higher estimated glomerular filtration rates and mesangial and glomerular volumes than HCs. Ten T2D participants had been prescribed SGLT2i (T2Di[+]) and 6 not (T2Di[-]). Transcriptional profiles showed SGLT2 expression exclusively in the proximal tubular (PT) cluster with highest expression in T2Di(-) patients. However, transcriptional alterations with SGLT2i treatment were seen across nephron segments, particularly in the distal nephron. SGLT2i treatment was associated with suppression of transcripts in the glycolysis, gluconeogenesis, and tricarboxylic acid cycle pathways in PT, but had the opposite effect in thick ascending limb. Transcripts in the energy-sensitive mTORC1-signaling pathway returned toward HC levels in all tubular segments in T2Di(+), consistent with a diabetes mouse model treated with SGLT2i. Decreased levels of phosphorylated S6 protein in proximal and distal tubules in T2Di(+) patients confirmed changes in mTORC1 pathway activity. We propose that SGLT2i treatment benefits the kidneys by mitigating diabetes-induced metabolic perturbations via suppression of mTORC1 signaling in kidney tubules.

Integrative single-cell characterization of frugivory adaptations in the bat kidney and pancreas

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we used integrative single-cell sequencing on insectivorous and frugivorous bat kidneys and pancreases and identified key cell population, gene expression and regulatory element differences associated with frugivorous adaptation that also relate to human disease, particularly diabetes. We found an increase in collecting duct cells and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the frugivore kidney. In the frugivorous pancreas, we observed an increase in endocrine and a decrease in exocrine cells and differences in genes and regulatory elements involved in insulin regulation. Combined, our work provides novel insights into frugivorous adaptation that also could be leveraged for therapeutic purposes.

Decoupling dedifferentiation and G2/M arrest in kidney fibrosis

Understanding the cellular mechanisms underlying chronic kidney disease (CKD) progression is required to develop effective therapeutic approaches. In this issue of the JCI, Taguchi, Elias, et al. explore the relationship between cyclin G1 (CG1), an atypical cyclin that induces G2/M proximal tubule cell cycle arrest, and epithelial dedifferentiation during fibrogenesis. While CG1-knockout mice were protected from fibrosis and had reduced G2/M arrest, protection was unexpectedly independent of induction of G2/M arrest. Rather, CG1 drove fibrosis by regulating maladaptive dedifferentiation in a CDK5-dependent mechanism. These findings highlight the importance of maladaptive epithelial dedifferentiation in kidney fibrogenesis and identify CG1/CDK5 signaling as a therapeutic target in CKD progression.

Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis

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.

Principles of human and mouse nephron development

The mechanisms underlying kidney development in mice and humans is an area of intense study. Insights into kidney organogenesis have the potential to guide our understanding of the origin of congenital anomalies and enable the assembly of genetic diagnostic tools. A number of studies have delineated signalling nodes that regulate positional identities and cell fates of nephron progenitor and precursor cells, whereas cross-species comparisons have markedly enhanced our understanding of conserved and divergent features of mammalian kidney organogenesis. Greater insights into the complex cellular movements that occur as the proximal-distal axis is established have challenged our understanding of nephron patterning and provided important clues to the elaborate developmental context in which human kidney diseases can arise. Studies of kidney development in vivo have also facilitated efforts to recapitulate nephrogenesis in kidney organoids in vitro, by providing a detailed blueprint of signalling events, cell movements and patterning mechanisms that are required for the formation of correctly patterned nephrons and maturation of physiologically functional apparatus that are responsible for maintaining human health.

Single-cell transcriptomics reveals common epithelial response patterns in human acute kidney injury

Background

Acute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood.

Methods

We performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1–2 h after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI.

Results

High-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs, and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar.

Conclusions

The study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01108-9.

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules–peritubular capillaries by screening for co-expression of ligand–receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p_adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand–receptor interactions were identified within glomeruli and regions of proximal tubules–peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

Listening in on Multicellular Communication in Human Tissue Immunology

Immune responses in human tissues rely on the concerted action of different cell types. Inter-cellular communication shapes both the function of the multicellular interaction networks and the fate of the individual cells that comprise them. With the advent of new methods to profile and experimentally perturb primary human tissues, we are now in a position to systematically identify and mechanistically dissect these cell-cell interactions and their modulators. Here, we introduce the concept of multicellular hubs, functional modules of immune responses in tissues. We outline a roadmap to discover multicellular hubs in human tissues and discuss how emerging technologies may further accelerate progress in this field.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Folic acid-mediated fibrosis is driven by C5a receptor 1-mediated activation of kidney myeloid cells

We previously reported that increased expression and activation of kidney cell complement components play an important role in the pathogenesis of renal scarring. Here we used floxed green fluorescent protein (GFP)-C5a receptor 1 (C5aR1) knock-in mice (GFP-C5ar1^fl/fl) and the model of Folic acid-induced kidney injury to define the cell types and potential mechanisms by which increased C5aR1 activation leads to fibrosis. Using flow cytometry and confocal microscopy we identified macrophages as the major interstitial cell type showing increased expression of C5aR1 in FA-treated mice. C5ar1^fl/fl.Lyz2Cre^+/- mice, in which C5aR1 has been specifically deleted in lysozyme M (LysM)-expressing myeloid cells, experienced reduced fibrosis when compared to control C5ar1^fl/fl mice. Examination of C5aR1-expressing macrophage transcriptomes by gene set enrichment analysis (GSEA) demonstrated that these cells were enriched in pathways corresponding to the complement cascade, collagen formation and the NABA matrisome, strongly pointing to their critical roles in tissue repair/scarring. Since C5aR1 was also detected in a small population of PDGFBR+ GFP+ cells we develop C5ar1^fl/fl.Foxd1Cre^+/- mice, in which C5aR1 is deleted specifically in pericytes, and found reduced FA-induced fibrosis. Primary cell cultures of platelet-derived growth factor receptor beta (PDGFRβ)+ pericytes isolated from FA-treated C5ar1^fl/fl.Foxd1Cre^+/- mice showed reduced secretion of several cytokines, including IL-6, and macrophage inflammatory proteins (MIP) 2, when compared to pericytes isolated from FA-treated control GFP-C5ar1^fl/fl mice. Collectively, these data imply that the C5a/C5aR1 axis activation primarily in interstitial cells contributes to the development of renal fibrosis.

Mapping the human kidney using single-cell genomics

The field of single-cell genomics and spatial technologies is rapidly evolving and has already provided unprecedented insights into complex tissues. Major advances have been made in dissecting the cellular composition and spatiotemporal interactions that mediate developmental processes in the fetal kidney. Single-cell technologies have also provided detailed insights into the heterogeneity of cell types within the healthy adult and shed light on the complex cellular mechanisms that contribute to kidney disease. The in-depth characterization of specific cell types associated with acute kidney injury and glomerular diseases has potential for the development of prognostic biomarkers and new therapeutics. Analyses of pathway activity in clear-cell renal cell carcinoma can predict the sensitivity of tumour cells to specific inhibitors. The identification of the cell of origin of renal cell carcinoma and of new cell types within the tumour microenvironment also has implications for the development of targeted therapeutics. Similarly, single-cell sequencing has provided new insights into the mechanisms underlying kidney fibrosis, specifically our understanding of myofibroblast origins and the contribution of cell crosstalk within the fibrotic niche to disease progression. These and future studies will enable the creation of a map to aid our understanding of the cellular processes and interactions in the developing, healthy and diseased kidney.

User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue

Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms. These approaches have been used for the analysis of cells in situ, within tissue, and confirmed existing and uncovered new models of cellular microenvironments in human disease. This has been achieved by the development of both imaging modality specific and multimodal solutions for cellular segmentation, thus addressing the fundamental requirement for high quality and reproducible cell segmentation in images from immunofluorescence, immunohistochemistry and histological stains. The expansive landscape of cell types-from a variety of species, organs and cellular states-has required a concerted effort to build libraries of annotated cells for training data and novel solutions for leveraging annotations across imaging modalities and in some cases led to questioning the requirement for single cell demarcation all together. Unfortunately, bleeding-edge approaches are often confined to a few experts with the necessary domain knowledge. However, freely available, and open-source tools and libraries of trained machine learning models have been made accessible to researchers in the biomedical sciences as software pipelines, plugins for open-source and free desktop and web-based software solutions. The future holds exciting possibilities with expanding machine learning models for segmentation via the brute-force addition of new training data or the implementation of novel network architectures, the use of machine and deep learning in cell and neighborhood classification for uncovering cellular microenvironments, and the development of new strategies for the use of machine and deep learning in biomedical research.

Digital Spatial Profiling of Collapsing Glomerulopathy

Collapsing glomerulopathy is a histologically distinct variant of focal and segmental glomerulosclerosis that presents with heavy proteinuria and portends a poor prognosis. Collapsing glomerulopathy can be triggered by viral infections such as HIV or SARS-CoV-2. Transcriptional profiling of collapsing glomerulopathy lesions is difficult since only a few glomeruli may exhibit this histology within a kidney biopsy and the mechanisms driving this heterogeneity are unknown. Therefore, we used recently developed digital spatial profiling (DSP) technology which permits quantification of mRNA at the level of individual glomeruli. Using DSP, we profiled 1,852 transcripts in glomeruli isolated from formalin fixed paraffin embedded sections from HIV or SARS-CoV-2-infected patients with biopsy-confirmed collapsing glomerulopathy and used normal biopsy sections as controls. Even though glomeruli with collapsing features appeared histologically similar across both groups of patients by light microscopy, the increased resolution of DSP uncovered intra- and inter-patient heterogeneity in glomerular transcriptional profiles that were missed in early laser capture microdissection studies of pooled glomeruli. Focused validation using immunohistochemistry and RNA in situ hybridization showed good concordance with DSP results. Thus, DSP represents a powerful method to dissect transcriptional programs of pathologically discernible kidney lesions.

Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics

The kidney is composed of heterogeneous groups of epithelial, endothelial, immune, and stromal cells, all in close anatomic proximity. Spatial transcriptomic technologies allow the interrogation of in situ expression signatures in health and disease, overlaid upon a histologic image. However, some spatial gene expression platforms have not yet reached single-cell resolution. As such, deconvolution of spatial transcriptomic spots is important to understand the proportion of cell signature arising from these varied cell types in each spot. This article reviews the various deconvolution strategies discussed in the 2021 Indiana O'Brien Center for Microscopy workshop. The unique features of Seurat transfer score methodology, SPOTlight, Robust Cell Type Decomposition, and BayesSpace are reviewed. The application of normalization and batch effect correction across spatial transcriptomic samples is also discussed.

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue

Spatial transcriptomic technologies capture genome-wide readouts across biological tissue space. Moreover, recent advances in this technology, including Slide-seqV2, have achieved spatial transcriptomic data collection at a near-single cell resolution. To-date, a repertoire of computational tools has been developed to discern cell type classes given the transcriptomic profiles of tissue coordinates. Upon applying these tools, we can explore the spatial patterns of distinct cell types and characterize how genes are spatially expressed within different cell type contexts. The kidney is one organ whose function relies upon spatially defined structures consisting of distinct cellular makeup. Thus, the application of Slide-seqV2 to kidney tissue has enabled us to elucidate spatially characteristic cellular and genetic profiles at a scale that remains largely unexplored. Here, we review spatial transcriptomic technologies, as well as computational approaches for cell type mapping and spatial cell type and transcriptomic characterizations. We take kidney tissue as an example to demonstrate how the technologies are applied, while considering the nuances of this architecturally complex tissue.

Anatomical structures, cell types and biomarkers of the Human Reference Atlas

The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA.