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. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure 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 establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes 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.

An intravenous pancreatic cancer therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic.

Clostridium novyi has demonstrated selective efficacy against solid tumors largely due to the microenvironment contained within dense tumor cores. The core of a solid tumor is typically hypoxic, acidic, and necrotic-impeding the penetration of current therapeutics. C. novyi is attracted to the tumor microenvironment and once there, can both lyse and proliferate while simultaneously re-activating the suppressed immune system. C. novyi systemic toxicity is easily mitigated by knocking out the phage DNA plasmid encoded alpha toxin resulting in C. novyi-NT; but, after intravenous injection spores are quickly cleared by phagocytosis before accomplishing significant tumor localization. C. novyi-NT could be designed to accomplish intravenous delivery with the potential to target all solid tumors and their metastases in a single dose. This study characterizes CRISPR/Cas9 modified C. novyi-NT to insert the gene for RGD, a tumor targeting peptide, expressed within the promoter region of a spore coat protein. Expression of the RGD peptide on the outer spore coat of C. novyi-NT indicates an increased capacity for tumor localization of C. novyi upon intravenous introduction based on the natural binding of RGD with the αvß3 integrin commonly overexpressed on the epithelial tissue surrounding a tumor, and lead to immune stimulation.

Alpha-1 antitrypsin inhibits fractalkine-mediated monocyte-lung endothelial cell interactions.

Chronic obstructive pulmonary disease (COPD) is characterized by nonresolving inflammation fueled by breach in the endothelial barrier and leukocyte recruitment into the airspaces. Among the ligand-receptor axes that control leukocyte recruitment, the full-length fractalkine ligand (CX3CL1)-receptor (CX3CR1) ensures homeostatic endothelial-leukocyte interactions. Cigarette smoke (CS) exposure and respiratory pathogens increase expression of endothelial sheddases, such as a-disintegrin-and-metalloproteinase-domain 17 (ADAM17, TACE), inhibited by the anti-protease α-1 antitrypsin (AAT). In the systemic endothelium, TACE cleaves CX3CL1 to release soluble CX3CL1 (sCX3CL1). During CS exposure, it is not known whether AAT inhibits sCX3CL1 shedding and CX3CR1+ leukocyte transendothelial migration across lung microvasculature. We investigated the mechanism of sCX3CL1 shedding, its role in endothelial-monocyte interactions, and AAT effect on these interactions during acute inflammation. We used two, CS and lipopolysaccharide (LPS) models of acute inflammation in transgenic Cx3cr1gfp/gfp mice and primary human endothelial cells and monocytes to study sCX3CL1-mediated CX3CR1+ monocyte adhesion and migration. We measured sCX3CL1 levels in plasma and bronchoalveolar lavage (BALF) of individuals with COPD. Both sCX3CL1 shedding and CX3CR1+ monocytes transendothelial migration were triggered by LPS and CS exposure in mice, and were significantly attenuated by AAT. The inhibition of monocyte-endothelial adhesion and migration by AAT was TACE-dependent. Compared with healthy controls, sCX3CL1 levels were increased in plasma and BALF of individuals with COPD, and were associated with clinical parameters of emphysema. Our results indicate that inhibition of sCX3CL1 as well as AAT augmentation may be effective approaches to decrease excessive monocyte lung recruitment during acute and chronic inflammatory states.NEW & NOTEWORTHY Our novel findings that AAT and other inhibitors of TACE, the sheddase that controls full-length fractalkine (CX3CL1) endothelial expression, may provide fine-tuning of the CX3CL1-CX3CR1 axis specifically involved in endothelial-monocyte cross talk and leukocyte recruitment to the alveolar space, suggests that AAT and inhibitors of sCX3CL1 signaling may be harnessed to reduce lung inflammation.

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.

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

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.

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.

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning.

The immune system governs key functions that maintain renal homeostasis through various effector cells that reside in or infiltrate the kidney. These immune cells play an important role in shaping adaptive or maladaptive responses to local or systemic stress and injury. We increasingly recognize that microenvironments within the kidney are characterized by a unique distribution of immune cells, the function of which depends on this unique spatial localization. Therefore, quantitative profiling of immune cells in intact kidney tissue becomes essential, particularly at a scale and resolution that allow the detection of differences between the various "nephro-ecosystems" in health and disease. In this review, we discuss advancements in tissue cytometry of the kidney, performed through multiplexed confocal imaging and analysis using the Volumetric Tissue Exploration and Analysis (VTEA) software. We highlight how this tool has improved our understanding of the role of the immune system in the kidney and its relevance in the pathobiology of renal disease. We also discuss how the field is increasingly incorporating machine learning to enhance the analytic potential of imaging data and provide unbiased methods to explore and visualize multidimensional data. Such novel analytic methods could be particularly relevant when applied to profiling immune cells. Furthermore, machine-learning approaches applied to cytometry could present venues for nonexhaustive exploration and classification of cells from existing data and improving tissue economy. Therefore, tissue cytometry is transforming what used to be a qualitative assessment of the kidney into a highly quantitative, imaging-based "omics" assessment that complements other advanced molecular interrogation technologies.

A reference tissue atlas for the human kidney.

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

The kidney protects against sepsis by producing systemic uromodulin.

Sepsis is a significant cause of mortality in hospitalized patients. Concomitant development of acute kidney injury (AKI) increases sepsis mortality through unclear mechanisms. Although electrolyte disturbances and toxic metabolite buildup during AKI could be important, it is possible that the kidney produces a protective molecule lost during sepsis with AKI. We have previously demonstrated that systemic Tamm-Horsfall protein (THP; uromodulin), a kidney-derived protein with immunomodulatory properties, falls in AKI. Using a mouse sepsis model without severe kidney injury, we showed that the kidney increases circulating THP by enhancing the basolateral release of THP from medullary thick ascending limb cells. In patients with sepsis, changes in circulating THP were positively associated with a critical illness. THP was also found de novo in injured lungs. Genetic ablation of THP in mice led to increased mortality and bacterial burden during sepsis. Consistent with the increased bacterial burden, the presence of THP in vitro and in vivo led macrophages and monocytes to upregulate a transcriptional program promoting cell migration, phagocytosis, and chemotaxis, and treatment of macrophages with purified THP increases phagocytosis. Rescue of septic THP-/- mice with exogenous systemic THP improved survival. Together, these findings suggest that through releasing THP, the kidney modulates the immune response in sepsis by enhancing mononuclear phagocyte function, and systemic THP has therapeutic potential in sepsis.NEW & NOTEWORTHY Specific therapies to improve outcomes in sepsis with kidney injury have been limited by an unclear understanding of how kidney injury increases sepsis mortality. Here, we identified Tamm-Horsfall protein, known to protect in ischemic acute kidney injury, as protective in preclinical sepsis models. Tamm-Horsfall protein also increased in clinical sepsis without severe kidney injury and concentrated in injured organs. Further study could lead to novel sepsis therapeutics.

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.

Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data.

Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4',6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.

Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians. OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression. METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME. RESULTS: A random forest algorithm accurately predicted tumor and stromal-rich areas with 87% accuracy using 31 markers and 77% accuracy using only five markers. Top tumor-predictive markers guided downstream analyses to identify immune populations effectively invading into the tumor, including dendritic cells, CD4+ T cells, and multiple immunoregulatory subtypes. CONCLUSIONS: Immunoprofiling of PDAC to identify differential distribution of immune cells in the TME is critical for understanding disease progression, response and/or resistance to treatment, and the development of new treatment strategies.

Label-free imaging of non-deparaffinized sections of the human kidney to determine tissue quality and signatures of disease.

Label-free fluorescence imaging of kidney sections can provide important morphological information, but its utility has not been tested in a histology processing workflow. We tested the feasibility of label-free imaging of paraffin-embedded sections without deparaffinization and its potential usefulness in generating actionable data. Kidney tissue specimens were obtained during percutaneous nephrolithotomy or via diagnostic needle biopsy. Unstained non-deparaffinized sections were imaged using widefield fluorescence microscopy to capture endogenous fluorescence. Some samples were also imaged with confocal microscopy and multiphoton excitation to collect second harmonic generation (SHG) signal to obtain high-quality autofluorescence images with optical sectioning. To adjudicate the label-free signal, the samples or corresponding contiguous sections were subsequently deparaffinized and stained with Lillie's allochrome. Label-free imaging allowed the recognition of various kidney structures and enabled morphological qualification for adequacy. SHG and confocal imaging yielded quantifiable high-quality images for tissue collagens and revealed specific patterns in glomeruli and various tubules. Disease specimens from patients with diabetic kidney disease and focal segmental glomerulosclerosis showed distinctive signatures compared to specimens from healthy controls with normal kidney function. Quantitative cytometry could also be performed when DAPI is added in situ before imaging. These results show that label-free imaging of non-deparaffinized sections provides useful information about tissue quality that could be beneficial to nephropathologists by maximizing the use of scarce kidney tissue. This approach also provides quantifiable features that could inform on the biology of health and disease.

VEGFR3 tyrosine kinase inhibition aggravates cisplatin nephrotoxicity.

Expansion of renal lymphatic networks, or lymphangiogenesis (LA), is well recognized during development and is now being implicated in kidney diseases. Although LA is associated with multiple pathological conditions, very little is known about its role in acute kidney injury. The purpose of this study was to evaluate the role of LA in a model of cisplatin-induced nephrotoxicity. LA is predominately regulated by vascular endothelial growth factor (VEGF)-C and VEGF-D, ligands that exert their function through their cognate receptor VEGF receptor 3 (VEGFR3). We demonstrated that use of MAZ51, a selective VEGFR3 inhibitor, caused significantly worse structural and functional kidney damage in cisplatin nephrotoxicity. Apoptotic cell death and inflammation were also increased in MAZ51-treated animals compared with vehicle-treated animals following cisplatin administration. Notably, MAZ51 caused significant upregulation of intrarenal phospho-NF-κB, phospho-JNK, and IL-6. Cisplatin nephrotoxicity is associated with vascular congestion due to endothelial dysfunction. Using three-dimensional tissue cytometry, a novel approach to explore lymphatics in the kidney, we detected significant vascular autofluorescence attributed to erythrocytes in cisplatin alone-treated animals. Interestingly, no such congestion was detected in MAZ51-treated animals. We found increased renal vascular damage in MAZ51-treated animals, whereby MAZ51 caused a modest decrease in the endothelial markers endomucin and von Willebrand factor, with a modest increase in VEGFR2. Our findings identify a protective role for de novo LA in cisplatin nephrotoxicity and provide a rationale for the development of therapeutic approaches targeting LA. Our study also suggests off-target effects of MAZ51 on the vasculature in the setting of cisplatin nephrotoxicity.NEW & NOTEWORTHY Little is known about injury-associated LA in the kidney and its role in the pathophysiology of acute kidney injury (AKI). Observed exacerbation of cisplatin-induced AKI after LA inhibition was accompanied by increased medullary damage and cell death in the kidney. LA inhibition also upregulated compensatory expression of LA regulatory proteins, including JNK and NF-κB. These data support the premise that LA is induced during AKI and lymphatic expansion is a protective mechanism in cisplatin nephrotoxicity.

Quantitative 3-dimensional imaging and tissue cytometry reveals lymphatic expansion in acute kidney injury.

The lymphatic system plays an integral role in physiology and has recently been identified as a key player in disease progression. Tissue injury stimulates lymphatic expansion, or lymphangiogenesis (LA), though its precise role in disease processes remains unclear. LA is associated with inflammation, which is a key component of acute kidney injury (AKI), for which there are no approved therapies. While LA research has gained traction in the last decade, there exists a significant lack of understanding of this process in the kidney. Though innovative studies have elucidated markers and models with which to study LA, the field is still evolving with ways to visualize lymphatics in vivo. Prospero-related homeobox-1 (Prox-1) is the master regulator of LA and determines lymphatic cell fate through its action on vascular endothelial growth factor receptor expression. Here, we investigate the consequences of AKI on the abundance and distribution of lymphatic endothelial cells using Prox1-tdTomato reporter mice (ProxTom) coupled with large-scale three-dimensional quantitative imaging and tissue cytometry (3DTC). Using these technologies, we describe the spatial dynamics of lymphatic vasculature in quiescence and post-AKI. We also describe the use of lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1) as a marker of lymphatic vessels using 3DTC in the absence of the ProxTom reporter mice as an alternative approach. The use of 3DTC for lymphatic research presents a new avenue with which to study the origin and distribution of renal lymphatic vessels. These findings will enhance our understanding of renal lymphatic function during injury and could inform the development of novel therapeutics for intervention in AKI.

Integration of spatial and single-cell transcriptomics localizes epithelial cell-immune cross-talk in kidney injury.

Single-cell sequencing studies have characterized the transcriptomic signature of cell types within the kidney. However, the spatial distribution of acute kidney injury (AKI) is regional and affects cells heterogeneously. We first optimized coordination of spatial transcriptomics and single-nuclear sequencing data sets, mapping 30 dominant cell types to a human nephrectomy. The predicted cell-type spots corresponded with the underlying histopathology. To study the implications of AKI on transcript expression, we then characterized the spatial transcriptomic signature of 2 murine AKI models: ischemia/reperfusion injury (IRI) and cecal ligation puncture (CLP). Localized regions of reduced overall expression were associated with injury pathways. Using single-cell sequencing, we deconvoluted the signature of each spatial transcriptomic spot, identifying patterns of colocalization between immune and epithelial cells. Neutrophils infiltrated the renal medulla in the ischemia model. Atf3 was identified as a chemotactic factor in S3 proximal tubules. In the CLP model, infiltrating macrophages dominated the outer cortical signature, and Mdk was identified as a corresponding chemotactic factor. The regional distribution of these immune cells was validated with multiplexed CO-Detection by indEXing (CODEX) immunofluorescence. Spatial transcriptomic sequencing complemented single-cell sequencing by uncovering mechanisms driving immune cell infiltration and detection of relevant cell subpopulations.

Molecular characterization of the human kidney interstitium in health and disease.

The gene expression signature of the human kidney interstitium is incompletely understood. The cortical interstitium (excluding tubules, glomeruli, and vessels) in reference nephrectomies (N = 9) and diabetic kidney biopsy specimens (N = 6) was laser microdissected (LMD) and sequenced. Samples underwent RNA sequencing. Gene signatures were deconvolved using single nuclear RNA sequencing (snRNAseq) data derived from overlapping specimens. Interstitial LMD transcriptomics uncovered previously unidentified markers including KISS1, validated with in situ hybridization. LMD transcriptomics and snRNAseq revealed strong correlation of gene expression within corresponding kidney regions. Relevant enriched interstitial pathways included G-protein coupled receptor. binding and collagen biosynthesis. The diabetic interstitium was enriched for extracellular matrix organization and small-molecule catabolism. Cell type markers with unchanged expression (NOTCH3, EGFR, and HEG1) and those down-regulated in diabetic nephropathy (MYH11, LUM, and CCDC3) were identified. LMD transcriptomics complements snRNAseq; together, they facilitate mapping of interstitial marker genes to aid interpretation of pathophysiology in precision medicine studies.