Biology subjects

Eadon, M. T.

Publications and source records attributed to Eadon, M. T..

At least 19 recordsLinked to original sources

bioRxiv2026

Shared and Specific miRNA-mRNA in Human and Murine Acute Kidney Injury

Acute kidney injury (AKI) is managed as a single clinical entity despite substantial biological heterogeneity. We established a cross-species framework to define etiology-specific molecular programs and determine how these programs relate to molecular heterogeneity and pathobiology in human AKI. We performed parallel sequencing of totalRNA (mRNA) and miRNA (kidney, whole blood) in 3 murine models (mild AKI: ischemia-reperfusion injury, IRI; cecal-ligation and puncture, CLP; endotoxemia, LPS); kidney mRNA and miRNA clearly distinguished the models, blood mRNA maintained model-specific separation, and blood miRNA was less distinct. IRI was characterized by epithelial injury/repair, and extracellular matrix remodeling (i.e., miR-125b-1-3p, Vim, Sprr2g); CLP, broader inflammatory, metabolic programs (i.e., miR-692, Pfkfb3, Cdo1); LPS, inflammatory signaling (i.e., miR-155-5p, Irf7, Gbp3). We then compared these signatures to human whole blood RNAseq. Human blood samples varied along a shared gene axis that correlated to differing degrees with all three murine models, consistent with the heterogeneous nature of clinical AKI rather than discrete assignment to a single experimental etiology. Orthologs were linked to immune processes (i.e., SIGLEC14, DAAM2, CD177, DEFA3), and the presence of tubular casts in human biopsies, reflecting that mild murine AKI models capture etiology-specific biology relevant to severe human AKI. Integration of injury etiology, severity, and time may provide a framework for developing circulating biomarkers that may resolve the biological heterogeneity of human AKI.

molecular biology↗

bioRxiv2026

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

bioinformatics↗

bioRxiv2026

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

pathology↗

bioRxiv2026

Spatially Resolved Banff Tubulitis and Glomerulitis Scoring in Kidney Allograft Biopsies via Artificial Intelligent-Based Structure Segmentation and Spatial Transcriptomics

BackgroundTubulitis is a defining histologic feature of T cell-mediated rejection (TCMR), while glomerulitis is often characteristic of antibody mediated rejection (AMR). Histologic quantification of tubulitis and glomerulitis using Banff criteria is subject to interobserver variability. Bulk transcriptomic assays (e.g., MMDx) have introduced molecular correlations of tubulitis with TCMR and glomerulitis with AMR, but lack spatial resolution. MethodsWe applied a web-based platform, FUSION (Functional Unit State Identification in Whole Slide Images), to a cohort of 8 cases (n=2 per condition) with kidney allograft biopsy samples acute TCMR, active AMR, chronic active AMR, and no rejection (control). The machine-learning (ML) platform enabled integrated visualization and analysis of spatial transcriptomics (10x Genomics Visium v2) together with high-resolution whole-slide histology. ResultsTranscriptomics-derived immune cell proportions within AI-segmented tubular and glomerular regions were used to generate spatial Banff t- and g-scores. Derived t-scores showed full concordance with pathologist scores in both acute TCMR cases; g-scores showed concordance in 2 of 4 AMR cases, with discordant cases characterized by low absolute immune signal near the classification boundary. ConclusionsWe demonstrate the feasibility of using AI-based FTU segmentation integrated with spatial transcriptomics-derived immune cell proportions to generate spatially informed t- and g-scores aligned with Banff criteria, with full concordance in severe rejection and partial concordance in mild rejection. This approach lays the foundation for validated, spatial transcriptomics-augmented t-scores and g-scores that enhance diagnostic precision, reduces inter-observer variability among renal pathologists, and support potential clinical adoption.

pathology↗

bioRxiv2026

A transcriptional patient map of systemic lupus erythematosus reveals disease-related multicellular immune programs conserved between blood and kidney

Systemic lupus erythematosus (SLE) shows marked clinical and molecular heterogeneity, yet patient stratification often relies on gene expression signatures lacking multicellular context. Here we construct a transcriptional patient map of SLE by analyzing 1,167 total samples (783 SLE, 384 healthy controls) across different resolutions, including single-cell and bulk blood as well as spatially resolved kidney tissue transcriptomes. Using an unsupervised approach we inferred patient-level transcriptomic immune programs from two independent single-cell RNA sequencing cohorts of peripheral blood mononuclear cells (PBMCs), capturing both differences between SLE and health as well as within-SLE heterogeneity. Specifically, we identified four conserved programs comprising two multicellular inflammatory programs driven by interferon and TNF/NFkB activity across immune cells, and two cell type-specific programs reflecting CD8 T cell cytotoxicity and a CD4 T cell naive-to-effector state. Functional analysis of these programs revealed a rewiring of both cell-to-cell interactions and task allocation across cell types during disease activation. In addition, mapping these programs onto an external longitudinal blood transcriptomic cohort predicted flare risk and identified candidate blood protein biomarkers detectable by proteomics. Finally, we showed that these blood programs were enriched in immune-infiltrated glomerular regions from kidney biopsies of individuals with lupus nephritis using spatially resolved transcriptomic data, thereby linking systemic immune programs to local tissue pathology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/721379v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1e75f00org.highwire.dtl.DTLVardef@10e0a63org.highwire.dtl.DTLVardef@cc194forg.highwire.dtl.DTLVardef@191b4d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

A cross-species single-cell epigenome kidney atlas identifies epithelial cells as a driver of epigenetic aging

Epigenetic aging is a hallmark of chronic diseases, arising from sustained injuries and unresolved repairs. To investigate cell-type-specific epigenetic alterations, we built a cross-species single-cell multi-omics atlas of DNA methylomes, chromatin accessibilities, and transcriptomes on healthy, injured (human) and aging (mouse) kidneys. We identified accelerated epigenetic aging dominated by tubular epithelia in diseased kidneys. The pathological state mirrors transcriptional trajectories observed in normal aging, driven by the preferential dysregulation of lineage-specific genes lacking CpG islands. Spatially, these epigenetic changes mapped to pathological niches of failed repair. Co-profiling of single-cell DNA methylation and 3D genome architecture revealed that epithelial repair states in disease undergo significant higher-order genome reorganizations, activating genes associated with renal decline. Our findings demonstrate that epithelial aging is driven by a collapse of 3D chromatin structure and local methylome integrity, which silences cell identity and promotes a non-resolving repair state.

genomics↗

bioRxiv2025

Cannabidiol exerts anti-inflammatory effects but maintains T effector memory cell differentiation: A Single-Cell Study in Humans

Cannabidiol is widely available and often used for pain management. Individuals with kidney disease or renal allografts have limited analgesia options. We conducted a Phase 1 human study to compare the peripheral immune cell distribution before (pre-cannabidiol) and after exposure to cannabidiol at steady state (post-cannabidiol). This ex vivo study included specimens from 23 participants who received oral cannabidiol (up to 5 mg/kg twice daily) for 11 days. Lymphocytes were isolated and stimulated with anti-CD3/CD28 antibodies, with or without tacrolimus. Pharmacodynamic responses were assessed via CellTiter-Glo(R) proliferation, scRNA-seq, cytokine assays, and flow cytometry. Steady-state plasma concentrations of CBD were quantified via tandem mass spectrometry. We identified an increased proportion of T effector memory (TEM) cells post-cannabidiol (22% increase, P-value of 3.2 x 10-32), which correlated with CBD plasma concentrations (Pearson Corr= 0.77, P-value < 0.01). Post-cannabidiol cytokine assays revealed elevated proinflammatory IL-6 protein levels and anti-inflammatory IL-10 levels (adjusted P-values < 0.0001). Cannabidiol reduced overall T and B lymphocyte proliferation with additive immunosuppressive effects to tacrolimus. In flow cytometry, the proportion of TEM and TEMRA increased post-cannabidiol with tacrolimus (P-values < 0.05). Cannabidiol exhibits mixed immunomodulatory effects with pro- and anti-inflammatory signals. Understanding the clinical safety of cannabidiol use is important given the paucity of pain control options available for immunocompromised transplant populations.

molecular biology↗

bioRxiv2025

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three- dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.

bioinformatics↗

bioRxiv2024

A MEF2C transcripton factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease

The maintenance of a healthy epithelial-endothelial juxtaposition requires cross-talk within glomerular cellular niches. We sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells. Data were culled from single nucleus RNA and ATAC sequencing and three orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the TF network accelerated the transition from healthy EC-GCs toward a degenerative (injury) endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. The MEF2C, MEF2A, and TRPS1 TF network carefully balances the fate of the EC-GC in DKD. When the TF network is "on" or over-expressed in DKD, EC-GCs may progress to a prEC state, while TF suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

cell biology↗

bioRxiv2024

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics

pathology↗

bioRxiv2024

Nicotinamide riboside activates renal metabolism and protects the kidney in a model of Alport syndrome

Chronic kidney disease (CKD) is associated with renal metabolic disturbances, including impaired fatty acid oxidation (FAO). Nicotinamide adenine dinucleotide (NAD+) is a small molecule that participates in hundreds of metabolism-related reactions. NAD+ levels are decreased in CKD, and NAD+ supplementation is protective. However, both the mechanism of how NAD+ supplementation protects from CKD, as well as the cell types involved, are poorly understood. Using a mouse model of Alport syndrome, we show that nicotinamide riboside (NR), an NAD+ precursor, stimulates renal peroxisome proliferator-activated receptor alpha signaling and restores FAO in the proximal tubules, thereby protecting from CKD in both sexes. Bulk RNA-sequencing shows that renal metabolic pathways are impaired in Alport mice and activated by NR in both sexes. These transcriptional changes are confirmed by orthogonal imaging techniques and biochemical assays. Single nuclei RNA-sequencing and spatial transcriptomics, both the first of their kind from Alport mice, show that NAD+ supplementation restores FAO in proximal tubule cells. Finally, we also report, for the first time, sex differences at the transcriptional level in this Alport model. In summary, we identify a nephroprotective mechanism of NAD+ supplementation in CKD, and we demonstrate that the proximal tubule cells substantially contribute to this benefit.

molecular biology↗

bioRxiv2023

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Emerging spatially resolved transcriptomics (SRT) technologies provide unprecedented opportunities to discover the spatial patterns of gene expression at the cellular or tissue levels. Currently, most existing computational tools on SRT are designed and tested on the ribbon-like brain cortex. Their present expressive power often makes it challenging to identify highly heterogeneous mosaic-like tissue architectures, such as tissues from kidney diseases. This demands heightened precision in discerning the cellular and morphological changes within renal tubules and their interstitial niches. We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, the proposed REGNN integrates equivariance to handle the rotational and translational symmetries of the spatial space, and Positional Encoding (PE) to identify and strengthen the relative spatial relations of the nodes uniformly distributed in the lattice. Our study finds that REGNN outperforms existing computational tools in identifying inherent mosaic-like heterogenous tissue architectures in kidney samples sourced from different kidney diseases using the 10X Visium platform. In case studies on acute kidney injury and chronic kidney diseases, the results identified by REGNN are also validated by experienced nephrology physicians. This proposed framework explores the expression patterns of highly heterogeneous tissues with an enhanced graph deep learning model, and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases. REGNN is publicly available at https://github.com/Mraina99/REGNN.

bioinformatics↗

bioRxiv2023

Inflammation primes the kidney for recovery by activating AZIN1 A-to-I editing

The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and Adenosine Deaminase isoform switching. We found that A-to-I editing of Antizyme Inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery.

molecular biology↗

bioRxiv2023

miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue

Acute kidney injury (AKI) is an important contributor to the development of chronic kidney disease (CKD). There is a need to understand molecular mediators that drive either recovery or progression to CKD. In particular, the role of miRNA and its regulatory role in AKI is poorly understood. We performed miRNA and mRNA sequencing on biobanked human kidney tissues obtained in the routine clinical care of patients with the diagnoses of AKI and minimal change disease (MCD), in addition to nephrectomized (Ref) tissue from individuals without known kidney disease. Transcriptomic analysis of mRNA revealed that Ref tissues exhibited a similar injury signature to AKI, not identified in MCD samples. The transcriptomic signature of human AKI was enriched with genes in pathways involved in cell adhesion and epithelial-to-mesenchymal transition (e.g., CDH6, ITGB6, CDKN1A). miRNA DE analysis revealed upregulation of miRNA associated with immune cell recruitment and inflammation (e.g., miR-146a, miR-155, miR-142, miR-122). These miRNA (i.e., miR-122, miR-146) are also associated with downregulation of mRNA such as DDR2 and IGFBP6, respectively. These findings suggest integrated interactions between miRNAs and target mRNAs in AKI-related processes such as inflammation, immune cell activation and epithelial-to-mesenchymal transition. These data contribute several novel findings when describing the epigenetic regulation of AKI by miRNA, and also underscores the importance of utilizing an appropriate reference control tissue to understand canonical pathway alterations in AKI.

cell biology↗

bioRxiv2023

Analysis of the Combined Effect of rs699 and rs5051 on Angiotensinogen Expression and Hypertension

Hypertension (HTN) involves genetic variability in the renin-angiotensin system and characterizing this variability will help advance precision antihypertensive treatments. We previously reported that angiotensinogen (AGT) mRNA is endogenously bound by mir-122-5p and that rs699 A>G significantly decreases reporter mRNA in the functional mirSNP assay PASSPORT-seq. The AGT promoter variant rs5051 C>T is in linkage disequilibrium (LD) with rs699 A>G and increases AGT transcription. We hypothesized that the increased AGT by rs5051 C>T counterbalances AGT decrease by rs699 A>G, and when these variants occur independently, would translate to HTN-related phenotypes. The independent effect of each of these variants is understudied due to their LD, therefore, we used in silico, in vitro, in vivo, and retrospective clinical and biobank analyses to assess HTN and AGT expression phenotypes where rs699 A>G occurs independently from rs5051 C>T. In silico, rs699 A>G is predicted to increase mir-122-5p binding strength by 3%. Mir-eCLIP assay results show that rs699 is 40-45 nucleotides from the strongest microRNA binding site in the AGT mRNA. Unexpectedly, rs699 A>G increases AGT mRNA in a plasmid cDNA HepG2 expression model. GTEx and UK Biobank analyses demonstrate that liver AGT expression and HTN phenotypes were not different when rs699 A>G occurs independently from rs5051 C>T, allowing us to reject the original hypothesis. However, both GTEx and our in vitro experiments suggest rs699 A>G confers cell-type specific effects on AGT mRNA abundance. We found that rs5051 C>T and rs699 A>G significantly associate with systolic blood pressure in Black participants in the UK Biobank, demonstrating a 4-fold larger effect than in White participants. Further studies are warranted to determine if the altered antihypertensive response in Black individuals might be due to rs5051 C>T or rs699 A>G. Studies like this will help clinicians move beyond the use of race as a surrogate for genotype.

genetics↗

bioRxiv2022

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

Kidney stone disease causes significant morbidity and increases health care utilization. The pathogenesis of stone disease is not completely understood, due in part to the poor characterization of the cellular and molecular makeup of the kidney papilla and its alteration with disease. We deciphered the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease compared to healthy subjects using single nuclear RNA sequencing, spatial transcriptomics and high-resolution large-scale multiplexed 3D and Co-Detection by indexing (CODEX) imaging. In addition to identifying cell types important in papillary physiology, we defined subtypes of immune, stromal and principal cells enriched in the papilla, and characterized an undifferentiated epithelial cell cluster that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncovered a global injury signature involving multiple cell types within the papilla, characterized by immune activation, oxidative stress and extracellular matrix remodeling. The microenvironment of mineral deposition had features of an immune synapse with antigen presenting inflammatory macrophages interacting with T cells, and an immune repertoire ranging from inflammation to fibrosis. The expression of MMP7 and MMP9 was associated with stone disease and mineral deposition, respectively. MMP7 and MMP9 were significantly increased in the urine of patients with CaOx stone disease compared to non-stone formers, and their levels correlated with disease activity in stone formers. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla, and identify potential urinary biomarkers.

genomics↗

bioRxiv2021

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 applied to human kidney tissue to create single cell resolution datasets that are both spatially large and multi-dimensional. These single cell resolution high-content imaging datasets have a great potential to uncover the complex spatial organization and cellular make-up of the human kidney. Tissue cytometry is a novel approach used for quantitative analysis of imaging data, but the scale and complexity of such datasets 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, VTEAs integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses for hyperdimensional large-scale imaging datasets. These novel capabilities enable the analysis of mesoscale two and three-dimensional multiplexed human kidney imaging datasets (such as CODEX and 3D confocal multiplexed fluorescence imaging). We demonstrate the utility of this approach in identifying cell subtypes in the kidney based on labels, spatial association and their microenvironment or neighborhood membership. VTEA provides integrated and intuitive approach to decipher the cellular and spatial complexity of the human kidney and complement other transcriptomics and epigenetic efforts to define the landscape of kidney cell types.

physiology↗

bioRxiv2021

An atlas of healthy and injured cell states and niches in the human kidney

Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of [~]1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.

genomics↗