Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

Network-based analyses of multiomics data in biomedicine

Network representations of data are designed to encode relationships between concepts as sets of edges between nodes. Human biology is inherently complex and is represented by data that often exists in a hierarchical nature. One canonical example is the relationship that exists within and between various -omics datasets, including genomics, transcriptomics, and proteomics, among others. Encoding such data in a network-based or graph-based representation allows the explicit incorporation of such relationships into various biomedical big data tasks, including (but not limited to) disease subtyping, interaction prediction, biomarker identification, and patient classification. This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.

Dmrt2 and Hmx2 direct intercalated cell diversity in the mammalian kidney through antagonistic and supporting regulatory processes

Intercalated cells (ICs) in the mammalian kidney regulate circulatory pH through IC subtype-restricted actions of bicarbonate transporters: pH is elevated by Slc4a1 restricted to type A-ICs (A-ICs) and depressed by Slc26a4 in type B-IC (B-ICs). NonA-nonB-ICs (nA/nB-ICs) also produce Slc26a4 though their function is unclear. Though both nephron and ureteric progenitor lineages generate A-ICs, the former also generates nA/nB-ICs and the latter B-ICs. Lineage and cell type restricted transporter gene expression in the mouse and human kidney is preceded by expression of the transcriptional regulators Dmrt2/DMRT2 in A-ICs, and either, or both, Hmx2/HMX2 and Hmx3/HMX3 in B- and nA/nB ICs. CRISPR/Cas9-directed removal of Dmrt2 and the linked Hmx2/Hmx3 genes resulted in IC-subtype switching. A-ICs adopted an Hmx2+/Slc26a4+ B-IC cell fate on Dmrt2 removal while B-ICs initiated a Dmrt2+/Slc4a1+ A-IC program on Hmx2/Hmx3 removal. Triple knockout of Dmrt2, Hmx2, and Hmx3 resulted in hybrid ICs expressing both Slc4a1 and Slc26a4. Thus, restricted expression of these regulators is essential for specifying IC subtypes. To explore these mechanisms, Hmx2 and Dmrt2 were activated ectopically in ureteric organoid cultures. Introduction of Foxi1-a pan determinant of ICs-activated early Dmrt2+ A-IC development while cointroduction of Hmx2 silenced Foxi1-dependent Dmrt2 expression and led to an upregulation of Slc26a4. In contrast, coexpression of Foxi1 and Dmrt2 upregulated Slc4a1. These data support a model in which mutually repressive interactions between Dmrt2 and Hmx2/3 establish distinct IC identities and ongoing activity of these factors supports gene regulatory programs specific to each IC subtype.

Label-free multimodal optical biopsy reveals biomolecular and morphological features of diabetic kidney tissue in 2D and 3D

Kidney disease, the ninth leading cause of death in the United States, suffers from poor diagnostic efficiency (10%). Traditional biopsies use molecular reagents to enhance diagnostic power but are limited by overlapping spatial and chromatic signals, product quality variability, and additional processing. To address these challenges without disrupting routine diagnostics, we implement label-free imaging modalities-stimulated Raman scattering (SRS), second harmonic generation (SHG), and two-photon fluorescence (TPF)-within a single setup. We identify morphological, lipidomic, and metabolic biomarkers in control and diabetic kidney samples at subcellular resolution. Label-free Stimulated Raman Histology (SRH) reveals distinct collagen morphology, mesangial-glomerular volumes, lipid saturation, redox status, and lipid-protein concentrations previously unrecognized in kidney diseases. Using the same tissue section enhances diagnostic value without compromising limited tissue. These multimodal biomarkers broadly deepen the understanding of kidney disease progression by integrating lipidomic, fibrotic, and metabolic data.

Oculomics: Current concepts and evidence

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.

Online extraction-LC-MS/MS is an alternative imaging tool for spatial-resolved metabolomics: Mint leaf as a pilot study

An attempt was made here to a complemental analytical tool for classical MSI approach. OLE-LC-MS/MS imaging was proposed to plot the spatial-resolved metabolome through deploying mint leaf as a proof-of-concept. A dried leaf underwent chemical composition characterization using OLE-LC-Qtof-MS. Another dried leaf was cut into small pieces, and all pieces were successively packed into a suitable cartridge to undergo OLE-LC-SRM measurements. Fifty-two compounds were observed and identified. Special attention was paid onto isomeric identification using fragment ion intensity ranking style, e.g., 3-O-caffeoylquinic acid vs. 4-O-caffeoylquinic acid. Thereof, 23 abundant ones were involved for relatively quantitative analysis. Quantitative settings were optimized using online ER-MS program. Following spatial metabolome imaging, regioselective distributions were observed for most concerned metabolites. Particularly, isomer-specific occurrences were observed for luteolin-7-O-glucuronide and luteolin-3'-O-glucuronide. Together, OLE-LC-MS/MS is alternative for spatial metabolome imaging due to the advantages at isomeric separation, identification confidence, and quantitative accuracy.

Single-cell epigenetics and multiomics analysis in kidney research

The rapid evolution of single-cell sequencing technologies has significantly advanced our knowledge of cellular heterogeneity and the underlying molecular basis in healthy and diseased kidneys. While single-cell transcriptomic analysis excels in characterizing cell states in the heterogeneous population, the complex regulatory mechanisms governing the gene expressions are difficult to decipher using transcriptomic data alone. Single-cell sequencing technology has recently extended to include epigenome and other modalities, allowing single-cell multiomics analysis. Especially, the integrative analysis of epigenome and transcriptome dissects the cell-specific, gene-regulatory mechanisms driving cellular heterogeneity. An increasing number of single-cell multimodal atlases are being generated in nephrology research, offering novel insights into cellular diversity and the underpinning epigenetic regulation. This ongoing paradigm shift in kidney research accelerates the identification of new biomarkers and potential therapeutic targets, promoting clinical translation. In this era of transformative nephrology research, the basic knowledge of single-cell sequencing analysis and multiomics approach is valuable not only for basic science researchers but for all nephrologists. This review overview single-cell analysis, with a focus on emerging epigenomic and multiomics approaches and their application to kidney research.

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

Case Report: a novel variant in WT1 leads to focal segmental glomerulosclerosis and uterovaginal anomalies through exon skipping

Background

Podocytopathies are a varied set of renal diseases in which podocytes are unable to perform their typical filtration function within the glomerulus. This typically leads to edema, proteinuria, and hypoalbuminemia early in life. Among podocytopathies, focal segmental glomerulosclerosis (FSGS) is characterized by histology demonstrating segmental and focal sclerosis of the glomerular tuft. FSGS affects an estimated 1-20 per one million individuals and leads to significant morbidity and mortality related to renal failure. While FSGS can be attributed to many causes, such as drug reactions and infections, underlying pathogenic genetic variants play an increasingly well-recognized role in this disease.

Case

A 38-year-old 46,XX female patient of self-reported Cambodian ancestry was evaluated due to her history of atypical uterovaginal morphology. She had a history of hypertension and nephrotic range proteinuria that was diagnosed early in adulthood. A kidney biopsy at that time revealed FSGS. Following worsening renal function and subsequent end-stage renal disease (ESRD), she underwent a kidney transplant at 33 years of age. After kidney transplant, she presented with hematocolpos and was found to have distal vaginal atresia and an arcuate uterus. She underwent vaginoplasty and then had regular menses. She was noted to have persistently elevated follicle stimulating hormone levels, consistent with primary ovarian insufficiency, but with normal anti-Müllerian hormone levels. Assessment of her family history was suggestive of other individuals in her family with similar renal disease and uterine differences. Genetic analysis identified a WT1 variant (c.1338A>C; p. =) of uncertain significance that is also present in her similarly affected mother. To help clarify the potential impact of this variant, we completed a mini-gene assay to detect in vitro splicing changes in the presence of the WT1 variant sequence uncovered in this individual. This demonstrated resultant aberrant splicing that further supports the pathogenicity of the uncovered variant for this individual.

Conclusions

To our knowledge, this represents the first case of a podocytopathy with co-occurring uterovaginal anomalies due to exon skipping in WT1. The patient exhibited a severe course of chronic kidney dysfunction requiring a kidney transplant. Clinical RNA sequencing to clarify variants impacting splicing remains challenging due to tissue- specific gene expression for genes such as WT1, thus, research-based assays may be beneficial to understand the consequence of rare or previously uncharacterized variants.

Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq

The epigenome of a cell is tightly correlated with gene transcription, which controls cell identity and diverse biological activities. Recent advances in spatial technologies have improved our understanding of tissue heterogeneity by analyzing transcriptomics or epigenomics with spatial information preserved, but have been mainly restricted to one molecular layer at a time. Here we present procedures for two spatially resolved sequencing methods, spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, that co-profile transcriptome and epigenome genome wide. In both methods, transcriptomic readouts are generated through tissue fixation, permeabilization and in situ reverse transcription. In spatial-ATAC-RNA-seq, Tn5 transposase is used to probe accessible chromatin, and in spatial-CUT&Tag-RNA-seq, the tissue is incubated with primary antibodies that target histone modifications, followed by Protein A-fused Tn5-induced tagmentation. Both methods leverage a microfluidic device that delivers two sets of oligonucleotide barcodes to generate a two-dimensional mosaic of tissue pixels at near single-cell resolution. A spatial-ATAC-RNA-seq or spatial-CUT&Tag-RNA-seq library can be generated in 3-5 d, allowing researchers to simultaneously investigate the transcriptomic landscape and epigenomic landscape of an intact tissue section. This protocol is an extension of our previous spatially resolved epigenome sequencing protocol and provides opportunities in multimodal profiling.

Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an inflammatory response arising from lung and systemic injury with diverse causes and associated with high rates of morbidity and mortality. To date, no fully effective pharmacological therapies have been established and the relevant underlying mechanisms warrant elucidation, which may be facilitated by multi‑omics technology. The present review summarizes the application of multi‑omics technology in identifying novel diagnostic markers and therapeutic strategies of ALI/ARDS as well as its pathogenesis.

CerS6 links ceramide metabolism to innate immune responses in diabetic kidney disease

Ectopic lipid deposition, mitochondrial injury, and inflammatory responses contribute to the development of diabetic kidney disease (DKD); however, the mechanistic link between these processes remains unclear. In this study, we demonstrate that the ceramide synthase 6 (CerS6) is primarily localized in podocytes of the glomeruli and is upregulated in two different models of diabetic mice. Podocyte-specific CerS6 knockout ameliorates glomerular injury and inflammatory responses in male diabetic mice and in male mice with adriamycin-induced nephropathy. In contrast, podocyte-specific overexpression of CerS6 sufficiently induces proteinuria. Mechanistically, CerS6-derived ceramide (d18:1/16:0) can bind to the mitochondrial channel protein VDAC1 at Glu59 residue, initiating mitochondrial DNA (mtDNA) leakage, activating the cGAS-STING signaling pathway, and ultimately promoting an immune-inflammatory response in the kidney. Importantly, CERS6 expression is increased in podocytes from kidney biopsies of patients with DKD and focal segmental glomerulosclerosis (FSGS), and the expression level of CERS6 is correlated negatively with glomerular filtration rate and positively with proteinuria. Thus, our findings suggest that targeting CerS6 may be a potential therapeutic strategy for proteinuric kidney diseases.

Challenges in maturation and integration of kidney organoids for stem cell-based renal replacement therapy

Human pluripotent stem cell-derived kidney organoids hold promise for future applications in regenerative medicine. However, significant biological hurdles need to be overcome to enable their use as a transplantable stem cell-derived therapeutic graft. Current kidney organoid protocols do not recapitulate a complete integrated developing kidney, but embryonic kidney transplantations have provided clues for advancing maturation and functionality of kidney organoids. Transplantation, subsequent vascularization, and blood perfusion of kidney organoids improve nephron patterning and maturation, suggesting a role for angiocrine factors as well as metabolic wiring in these processes. Transplanted organoids exhibit filtration, but the resulting filtrate has no apparent exit path for excretion. Improved in vitro patterning of kidney organoids may be required such that a more structurally correct tissue is formed before transplant. Here we review current progress with transplantation of kidney organoids, as well as their engraftment and integration, and identify the key obstacles to therapeutic success and how these might be achieved.

Distinct cell types along thick ascending limb express pathways for monovalent and divalent cation transport

Kidney thick ascending limb cells reabsorb sodium, potassium, calcium, and magnesium and contribute to urinary concentration. These cells are typically viewed as of a single type that recycles potassium across the apical membrane and generates a lumen-positive transepithelial voltage driving calcium and magnesium reabsorption, although variability in potassium channel expression has been reported. Additionally, recent transcriptomic analyses suggest that different cell types exist along this segment, but classifications have varied and have not led to a new consensus model. We used immunolocalization, electrophysiology and enriched single nucleus RNA-Seq to identify thick ascending limb cell types in rat, mouse and human. We identified three major TAL cell types defined by expression of potassium channels and claudins. One has apical potassium channels, low basolateral potassium conductance, and is bordered by a sodium-permeable claudin. A second lacks apical potassium channels, has high basolateral potassium conductance and is bordered by calcium- and magnesium-permeable claudins. A third type also lacks apical potassium channels and has a high basolateral potassium conductance, but these cells are ringed by sodium-permeable claudins. The recognition of diverse cell types resolves longstanding questions about how solute transport can be modulated selectively and how disruption of these cells leads to human disease.

Single cell lipid biology

Lipids are major cell constituents endowed with astonishing structural diversity. The pathways responsible for the assembly and disposal of different lipid species are energetically demanding, and genes encoding lipid metabolic factors and lipid-related proteins comprise a sizable fraction of our coding genome. Despite the importance of lipids, the biological significance of lipid structural diversity remains largely obscure. Recent technological developments have enabled extensive lipid analysis at the single cell level, revealing unexpected cell-cell variability in lipid composition. This new evidence suggests that lipid diversity is exploited in multicellularity and that lipids have a role in the establishment and maintenance of cell identity. In this review, we highlight the emerging concepts and technologies in single cell lipid analysis and the implications of this research for future studies.

The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease

Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.

Reprogramming of Glucose Metabolism for Revisiting Hepatocellular Carcinoma Resistance to Transcatheter Hepatic Arterial Chemoembolization

Hepatocellular carcinoma (HCC) is recognized globally as one of the most lethal tumors, presenting a significant menace to patients' lives owing to its exceptional aggressiveness and tendency to recur. Transcatheter hepatic arterial chemoembolization (TACE) therapy, as a first-line treatment option for patients with advanced HCC, has been proven effective. However, it is disheartening that nearly 40 % of patients exhibit resistance to this therapy. Consequently, this review delves into the metabolic aspects of glucose metabolism to explore the underlying mechanisms behind TACE treatment resistance and to propose potentially fruitful therapeutic strategies. The ultimate objective is to present novel insights for the development of personalized treatment methods targeting HCC.

The Kidney Precision Medicine Project and Single-Cell Biology of the Injured Proximal Tubule

Single-cell RNA sequencing (scRNA-seq) has led to major advances in our understanding of proximal tubule subtypes in health and disease. The proximal tubule serves essential functions in overall homeostasis, but pathologic or physiological perturbations can affect its transcriptomic signature and corresponding tasks. These alterations in proximal tubular cells are often described within a scRNA-seq atlas as cell states, which are pathophysiological subclassifications based on molecular and morphologic changes in a cell's response to that injury compared with its native state. This review describes the major cell states defined in the Kidney Precision Medicine Project's scRNA-seq atlas. It then identifies the overlap between the Kidney Precision Medicine Project and other seminal works that may use different nomenclature or cluster proximal tubule cells at different resolutions to define cell state subtypes. The goal is for the reader to understand the key transcriptomic markers of important cellular injury and regeneration processes across this highly dynamic and evolving field.

Single-Cell Analysis Provides New Insights into the Roles of Tertiary Lymphoid Structures and Immune Cell Infiltration in Kidney Injury and Chronic Kidney Disease

Chronic kidney disease (CKD) is a global health concern with high morbidity and mortality. Acute kidney injury (AKI) is a pivotal risk factor for the progression of CKD, and the rate of AKI-to-CKD progression increases with aging. Intrarenal inflammation is a fundamental mechanism underlying AKI-to-CKD progression. Tertiary lymphoid structures (TLSs), ectopic lymphoid aggregates formed in nonlymphoid organs, develop in aged injured kidneys, but not in young kidneys, with prolonged inflammation and maladaptive repair, which potentially exacerbates AKI-to-CKD progression in aged individuals. Dysregulated immune responses are involved in the pathogenesis of various kidney diseases, such as IgA nephropathy, lupus nephritis, and diabetic kidney diseases, thereby deteriorating kidney function. TLSs also develop in several kidney diseases, including transplanted kidneys and renal cell carcinoma. However, the precise immunologic mechanisms driving AKI-to-CKD progression and development of these kidney diseases remain unclear, which hinders the development of novel therapeutic approaches. This review aims to describe recent findings from single-cell analysis of cellular heterogeneity and complex interactions among immune and renal parenchymal cells, which potentially contribute to the pathogenesis of AKI-to-CKD progression and other kidney diseases, highlighting the mechanisms of formation and pathogenic roles of TLSs in aged injured kidneys.

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co-sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups. In applying Perturb-DBiT, we conducted un-biased knockouts of tens of genes or at genome-wide scale across three cancer models. We mapped all gRNAs in individual colonies and corresponding transcriptomes in a human cancer metastatic colonization model, revealing clonal dynamics and cooperation. We also examined the effect of genetic perturbation on the tumor immune microenvironment in an immune-competent syngeneic model, uncovering differential and synergistic perturbations in promoting immune infiltration or suppression in tumors. Perturb-DBiT allows for simultaneously evaluating the impact of each knockout on tumor initiation, development, metastasis, histopathology, and immune landscape. Ultimately, it not only broadens the scope of genetic inquiry, but also lays the groundwork for developing targeted therapeutic strategies.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

Integrating Metabolomics and Transcriptomics to Characterize Differential Functional Capabilities of Kidney Proximal Tubule Cell Subtypes

The coupling between energy metabolism and transport processes is a key feature that defines the functional capability of proximal tubule cells. Recent studies using metabolomics and transcriptomics provide insights into the relationships between changes in single-cell transcriptomic profiles and energy metabolism during kidney development and in disease states. In this review, we describe insights from these studies and how mapping of metabolites to functional pathways within cells enables these insights. We also describe our analyses of fatty acid metabolism pathways from single-cell transcriptomic data obtained by the Kidney Precision Medicine Project, which indicate that proximal tubule cell subtypes can be divided into two major groups with high and low levels of mRNAs for fatty acid (beta) oxidation enzymes. On average, patients with CKD have higher levels of cells with low fatty acid oxidation capability. These cells also have lower levels of sodium transporters. Within each group of proximal tubule cell subtypes there is considerable variability between individual patients. Integrating these data with metabolomics analyses can provide insights into how the differential metabolic capabilities of proximal tubule cells are related to disease features in individual patients. Identifying such relationships can lead to development of precision medicine approaches in nephrology.

Spatially resolved metabolomics: From metabolite mapping to function visualising

Mass spectrometry imaging (MSI)-based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography-tandem mass spectrometry (LC-MS)-based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI-driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation. HIGHLIGHTS: MSI-driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.

Spatial Metabolomics in Acute Kidney Injury

Acute kidney injury (AKI) is a common condition linked to increased morbidity, mortality, and substantial health care costs both in the United States and globally. Early diagnosis, prompt intervention, and effective therapeutic management of AKI are vital for improving patient outcomes. Recent advancements in renal imaging and omics technologies have provided new perspectives and deeper insights into kidney injury while also presenting challenges in developing a comprehensive cellular and molecular atlas of the condition. This review focuses on the application of mass spectrometry imaging-based spatial metabolomics in studying ischemia- and toxin-induced AKI in animal models and human patients. Spatial metabolomics offers a deeper understanding of the pathophysiological connections between various processes, such as dysregulated lipid metabolism and the shift from the tricarboxylic acid cycle to glycolytic flux, which contribute to functional impairment and structural damage in AKI. Continued research in renal multimodal imaging and omics is essential to further our understanding of kidney injury from diagnostic, mechanistic, and therapeutic perspectives.

Label-Free Optical Biopsy Reveals Biomolecular and Morphological Features of Diabetic Kidney Tissue in 2D and 3D

Kidney disease, the ninth leading cause of death in the United States, has one of the poorest diagnostic efficiencies of only 10%1. Conventional diagnostic methods often rely on light microscopy analysis of 2D fixed tissue sections with limited molecular insight compared to omics studies. Targeting multiple features in a biopsy using molecular or chemical reagents can enhance molecular phenotyping but are limited by overlap of their spatial and chromatic properties, variations in quality of the products, limited multimodal nature and need additional tissue processing. To overcome these limitations and increase the breadth of molecular information available from tissue without an impact on routine diagnostic workup, we implemented label-free imaging modalities including stimulated Raman scattering (SRS) microscopy, second harmonic generation (SHG), and two photon fluorescence (TPF) into a single microscopy setup. We visualized and identified morphological, structural, lipidomic, and metabolic biomarkers of control and diabetic human kidney biopsy samples in 2D and 3D at a subcellular resolution. The label-free biomarkers, including collagen fiber morphology, mesangial-glomerular fractional volume, lipid saturation, redox status, and relative lipid and protein concentrations in the form of Stimulated Raman Histology (SRH), illustrate distinct features in kidney disease tissues not previously appreciated. The same tissue section can be used for routine diagnostic work up thus enhancing the power of cliniopathological insights obtainable without compromising already limited tissue. The additional multimodal biomarkers and metrics are broadly applicable and deepen our understanding of the progression of kidney diseases by integrating lipidomic, fibrotic, and metabolic data.

Protocol for multimodal profiling of human kidneys with simultaneous high-throughput ATAC and RNA expression with sequencing

Simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) profiles transcriptomics and chromatin accessibility in the same cells at high throughput. Here, we present a protocol for multimodal profiling of human kidneys with SHARE-seq. We describe steps for processing fixed nuclei for SHARE-seq split-pool barcoding and library preparation. We also detail how to determine the optimal working concentration of Tn5 transposase for transposition and tagmentation. This protocol allows researchers to generate large-scale single-cell multiomics data at low reagent cost. For complete details on the use and execution of this protocol, please refer to Li et al.1.

Chromatin conformation and histone modification profiling across human kidney anatomic regions

The three major anatomic regions of the human kidney include the cortex, medulla and papilla, with different functions and vulnerabilities to kidney diseases. Epigenetic mechanisms underlying these anatomic structures are incompletely understood. Here, we performed chromatin conformation capture with Hi-C and histone modification H3K4me3/H3K27me3 Cleavage Under Targets and Release Using Nuclease (CUT&RUN) sequencing on the kidney cortex, medulla and papilla dissected from one individual donor. Nuclear suspensions were generated from each region and split subjected to paired Hi-C and CUT&RUN sequencing. We evaluated the quality of next-generation sequencing data, Hi-C chromatin contact matrices and CUT&RUN peak calling. H3K4me3 and H3K27me3 histone modifications represent active and repressive gene transcription, respectively, and differences in chromatin conformation between kidney regions can be analyzed with this dataset. All raw and processed data files are publicly available, allowing researchers to survey the epigenetic landscape across regional human kidney anatomy.

Spatially resolved metabolomic dataset of distinct human kidney anatomic regions

Cortex, medulla and papilla are three major human kidney anatomic structures and they harbour unique metabolic functions, but the underlying metabolomic profiles are largely unknown at spatial resolution. Here, we generated a spatially resolved metabolomics dataset on human kidney cortex, medulla and papilla tissues dissected from the same donor. Matrix-Assisted Laser Desorption/Ionization-Imaging Mass Spectrometry (MALDI-IMS) was used to detect metabolite species over mass-to-charge ratios of 50 -1500 for each section at a resolution of 10 × 10 µm2 pixel size. We present raw data matrix of each sample, feature annotations, raw AnnData merged from three samples and processed AnnData files after quality control, dimensional reduction and data integration, which contains a total of 170,459 spatially resolved metabolomes with 562 features detected. This dataset can be either visualized through an interactive browser or further analyzed to study metabolomic heterogeneity across regional human kidney anatomy.

Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.