Analyzing cell-type-specific dynamics of metabolism in kidney repair

Spatial multi-omics analysis of metabolic heterogeneity in zebrafish exposed to microcystin-LR and its disinfection byproducts

Most studies on the biological effects of exogenous pollutants have focused on whole samples or cell populations, and lack spatial heterogeneity consideration due to technical limitations. Microcystin-LR (MC-LR) from cyanobacterial blooms threatens ecosystems and human health, while microcystin-LR disinfection by-products (MCLR-DBPs) in drinking water remain a concern for their toxin-like structure. This study introduces spatial multi-omics to investigate the disruptions caused by ingestion of MC-LR and MCLR-DBPs in zebrafish. The method integrates metabolomics, spatial metabolomics, and spatial transcriptomics to characterize the overall metabolic changes in whole zebrafish caused by MC-LR and MCLR-DBPs, then provides further insight into the variation of spatial distribution of metabolites and genes in MC-LR and MCLR-DBPs targeted organ. The results showed that MC-LR and MCLR-DBPs induced oxidative stress and metabolic imbalance, and disrupted the physiological homeostasis of zebrafish. Spatial multi-omics analysis further revealed that MC-LR and MCLR-DBPs exacerbate disruptions in energy and lipid metabolism, methylation processes, and immune pathways by modulating the expression of genes such as gatm, gnmt, cyp2p9, and tdo2b. In conclusion, this study developed a spatial multi-omics approach that not only enhances the understanding of the biological effects of MC-LR and MCLR-DBPs but also provides robust technical support for investigating other environmental pollutants.

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.

Comprehensive High-Spatial-Resolution Imaging Metabolomics Workflow for Heterogeneous Tissues

Mass spectrometry imaging is a developing technique that maps the molecular composition of samples in a label-free manner. However, highly heterogeneous samples, including bones, usually cannot be easily analyzed due to challenging sample preparation, particularly in minimizing cracks and maintaining flatness. To comprehensively address these issues, we developed a sample preparation method for the fresh frozen heterogeneous samples such as knee joint and skull of murine, which includes complex structures and tissue types, such as neuronal tissue, peripheral nerve, muscle, tracks, connective tissue, cartilage, mineralized bone, and bone marrow. By controlling the sample thickness and employing an optimized drying method, we achieved minimal cracking. We found that a combination of lyophilization and 5 μm section thickness, when attached to a cryofilm, was readily achievable and significantly reduced cracking in bone tissue. Additionally, we implemented a contactless spin-flattening technique to ensure surface uniformity. Centrifuging the section at 7000g improved surface flatness, bringing the height variation within the range typically observed in soft tissues while also removing excess mounting medium and bubbles. This approach enhances the sample quality and reliability without requiring complex manual skills, making it more practical and reproducible for routine analysis. High molecular coverage was achieved, including small metabolites, metals, and lipids, by using the N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC) matrix. To further explore the potential of our workflow, high-resolution MSI was performed on rat tibial growth plates at different growth stages. Numbers of N-acetyl disaccharide sulfate and PE (34:1) are found to be complementary expressed in growth plate cartilage and have different intensities at different growth stages. Our findings suggested the potential involvement of those metabolites in bone development. By addressing the challenges of sample preparation, including surface flatness, bubbles, and severe cracking, our approach significantly improves the quality of the MS imaging. Additionally, this method offers a broad detection range that encompasses both metal ions and metabolites. This advancement enables detailed and accurate molecular characterization of rigid biological samples, enhancing the potential for applications in biomedical research.

Improved MALDI-MS Imaging of Polar and 2H-Labeled Metabolites in Mouse Organ Tissues

Imaging small polar metabolites and analyzing their in vivo dynamics with stable isotope-labeled (SIL) tracing through various biochemical pathways, including the citric acid (TCA) cycle, glycolysis, and amino acid metabolism, have gained substantial interest over the years. However, imaging these small polar metabolites across different tissue types is limited due to their lower ionization efficiencies and ion suppression from larger abundant biomolecules. These challenges can be further exacerbated with SIL studies, which require improvements in sample preparation and method sensitivity. Solvent pretreatments before matrix application on a tissue section have the potential to improve the sensitivity of metabolite imaging; however, they are not yet widely optimized across tissue types. Furthermore, there is a recurring concern about metabolite delocalization from such wash treatments that require "spatial validation". Here, we optimized a simple "basic hexane" wash method that improved sensitivity up to several folds for a broad range of polar and 2H-labeled metabolites across five different mouse organ tissues (kidney, heart, brain, liver, and brown adipose tissue). Notably, we provided region-specific quantification of 51 metabolites using laser microdissection (LMD)-LC-MS/MS to validate their localization observed in MALDI-MSI analysis after the basic hexane wash. Overall, we reported an improved MALDI-MSI sample pretreatment method with a "spatial validation" workflow for sensitive and robust imaging of polar metabolite distributions in mouse organs.

Deep MALDI-MS spatial omics guided by quantum cascade laser mid-infrared imaging microscopy

In spatial'omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry imaging (MSI)-based spatial 'omics must compromise between data acquisition speed and biochemical profiling depth. Here, we introduce fast, label-free quantum cascade laser mid-infrared imaging microscopy (QCL-MIR imaging) to guide MSI to high-interest tissue regions as small as kidney glomeruli, cultured multicellular spheroid cores or single motor neurons. Focusing on smaller tissue areas enables extensive spatial lipid identifications by on-tissue tandem-MS employing imaging parallel reaction monitoring-Parallel Accumulation-Serial Fragmentation (iprm-PASEF). QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. Taken together, widely applicable and fast QCL-MIR imaging-based guidance of MSI ensures that more time is available for exploration and validation of new biology by default on-tissue tandem-MS analysis.

Dynamic single-cell metabolomics reveals cell-cell interaction between tumor cells and macrophages

Single-cell metabolomics reveals cell heterogeneity and elucidates intracellular molecular mechanisms. However, general concentration measurement of metabolites can only provide a static delineation of metabolomics, lacking the metabolic activity information of biological pathways. Herein, we develop a universal system for dynamic metabolomics by stable isotope tracing at the single-cell level. This system comprises a high-throughput single-cell data acquisition platform and an untargeted isotope tracing data processing platform, providing an integrated workflow for dynamic metabolomics of single cells. This system enables the global activity profiling and flow analysis of interlaced metabolic networks at the single-cell level and reveals heterogeneous metabolic activities among single cells. The significance of activity profiling is underscored by a 2-deoxyglucose inhibition model, demonstrating delicate metabolic alteration within single cells which cannot reflected by concentration analysis. Significantly, the system combined with a neural network model enables the metabolomic profiling of direct co-cultured tumor cells and macrophages. This reveals intricate cell-cell interaction mechanisms within the tumor microenvironment and firstly identifies versatile polarization subtypes of tumor-associated macrophages based on their metabolic signatures, which is in line with the renewed diversity atlas of macrophages from single-cell RNA-sequencing. The developed system facilitates a comprehensive understanding single-cell metabolomics from both static and dynamic perspectives.

Spatial mapping of the brain metabolome lipidome and glycome

Metabolites, lipids, and glycans are fundamental but interconnected classes of biomolecules that form the basis of the metabolic network. These molecules are dynamically channeled through multiple pathways that govern cellular physiology and pathology. Here, we present a framework for the simultaneous spatial analysis of the metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. This workflow integrates a computational platform, the Spatial Augmented Multiomics Interface (Sami), which enables multiomics integration, high-dimensional clustering, spatial anatomical mapping of matched molecular features, and metabolic pathway enrichment. To demonstrate the utility of this approach, we applied Sami to evaluate metabolic diversity across distinct brain regions and to compare wild-type and Ps19 Alzheimer's disease (AD) mouse models. Our findings reveal region-specific metabolic demands in the normal brain and highlight metabolic dysregulation in the Ps19 model, providing insights into the biochemical alterations associated with neurodegeneration.

Endothelial cell metabolism in cardiovascular physiology and disease

Endothelial cells are multifunctional cells that form the inner layer of blood vessels and have a crucial role in vasoreactivity, angiogenesis, immunomodulation, nutrient uptake and coagulation. Endothelial cells have unique metabolism and are metabolically heterogeneous. The microenvironment and metabolism of endothelial cells contribute to endothelial cell heterogeneity and metabolic specialization. Endothelial cell dysfunction is an early event in the development of several cardiovascular diseases and has been shown, at least to some extent, to be driven by metabolic changes preceding the manifestation of clinical symptoms. Diabetes mellitus, hypertension, obesity and chronic kidney disease are all risk factors for cardiovascular disease. Changes in endothelial cell metabolism induced by these cardiometabolic stressors accelerate the accumulation of dysfunctional endothelial cells in tissues and the development of cardiovascular disease. In this Review, we discuss the diversity of metabolic programmes that control endothelial cell function in the cardiovascular system and how these metabolic programmes are perturbed in different cardiovascular diseases in a disease-specific manner. Finally, we discuss the potential and challenges of targeting endothelial cell metabolism for the treatment of cardiovascular diseases.

Spatial multi-omics reveals the potential involvement of SPP1+ fibroblasts in determining metabolic heterogeneity and promoting metastatic growth of colorectal cancer liver metastasis

This study investigates key microscopic regions involved in colorectal cancer liver metastasis (CRLM), focusing on the crucial role of cancer-associated fibroblasts (CAFs) in promoting tumor progression and providing molecular- and metabolism-level insights for its diagnosis and treatment using multi-omics. We followed 12 fresh surgical samples from 2 untreated CRLM patients. Among these, 4 samples were used for spatial transcriptomics (ST), 4 for spatial metabolomics, and 4 for single-cell RNA sequencing (scRNA-seq). Additionally, 92 frozen tissue samples from 40 patients were collected. Seven patients were used for immunofluorescence and RT-qPCR, while 33 patients were used for untargeted metabolomics. ST revealed that the spatial regions of CRLM consists of 7 major components, with fibroblast-dominated regions being the most prominent. These regions are characterized by diverse cell-cell interactions, and immunosuppressive and tumor growth-promoting environments. scRNA-seq identified that SPP1+ fibroblasts interact with CD44+ tumor cells, as confirmed through immunofluorescence. Spatial metabolomics revealed suberic acid and tetraethylene glycol as specific metabolic components of this structure, which was further validated by untargeted metabolomics. In conclusion, an SPP1+ fibroblast-rich spatial region with metabolic reprogramming capabilities and immunosuppressive properties was identified in CRLM, which potentially facilitates metastatic outgrowth through interactions with tumor cells.

Single-Cell RNA Sequencing Analysis Reveals the Role of Macrophage-Mediated CD44-AKT-CCL2 Pathways in Renal Tubule Injury during Calcium Oxalate Crystal Formation

Oxalate-induced crystalline kidney injury is a common form of crystal nephropathy. The accumulation of calcium oxalate (CaOx) crystal could lead to renal epithelium injury and inflammation. The underlying cellular events in kidney after CaOx crystal formation are largely unknown. This study was aimed to gain a better understanding of mouse kidney function in the development of renal CaOx formation. The study utilized a mouse CaOx model to analyze the cellular response at 5 time points using single-cell RNA sequencing and investigate the interaction of different cells during renal CaOx crystal formation. Additionally, the study investigated the communication between these cells and macrophages, as well as the role of chemokines in recruiting infiltrating macrophages. RNA velocity analysis uncovered an alternative differentiation pathway for injured and S1 proximal tubule cells, which mainly communicate with macrophages through the SPP1-CD44 pair, along with the expression of proinflammatory factors and stone matrix genes during renal CaOx crystal formation. Furthermore, resident Fn1 macrophages were found to express chemokines, such as CCL2, which recruited infiltrating macrophages. The CCL2 secretion was mediated by the CD44-AKT pathway. Blocking CCL2 decreased the expression of injury markers in the kidney, including CLU, LCN2, and KIM-1, and inhibited CaOx crystal deposition. The study identified potential cell types and target genes involved in renal tubule injury in oxalate-related crystal nephropathy. The findings shed light on the cellular processes that contribute to the formation and damage caused by CaOx crystals within the kidney, which could lead to the development of potential cell types and target genes for treating this condition.

Epithelial-Immune-Stromal Interactions Define Divergent Repair and Fibrosis Pathways After Acute Kidney Injury in Human Renal Transplants

Acute kidney injury (AKI) is a major cause of early graft dysfunction after kidney transplantation, particularly in recipients of high-risk donor kidneys prone to ischemia-reperfusion injury. However, the cellular mechanisms dictating whether injury resolves or progresses to fibrosis remain unclear. This study combines single-nucleus RNA sequencing and imaging mass cytometry (IMC) analysis of human kidney allograft biopsies collected within eight weeks posttransplant, stratified by long-term functional outcomes. Grafts that recovered function were enriched in regenerative proximal tubular (PT) cells co-expressing PROM1, CD24, and injury markers, consistent with scattered tubular cells (STCs). In contrast, non-recovering grafts contained a unique subpopulation of transitional proximal tubule cells (tPT4) characterized by dedifferentiation, loss of epithelial identity, and acquisition of fibroblast-like features. Fibroblast trajectory analysis revealed a profibrotic lineage, progressing from stromal progenitors to myofibroblasts, exclusive to nonrecovery grafts. Immune profiling showed divergent macrophage (MΦ) polarization, with reparative MΦ2 cells and regulatory dendritic cell (DC)-like signatures in recovering grafts, versus inflammatory MΦ1 and pro-fibrotic DCs in non-recovery. IMC confirmed spatial colocalization of injured tubules, activated fibroblasts, and immune cells in fibrotic regions, validated in an independent cohort. Functional assays demonstrated that ischemic epithelial injury activated monocyte-derived MΦs with mixed inflammatory/reparative profiles and induced fibroblast-related gene expression, while PAX8 knockdown impaired epithelial proliferation and promoted pro-inflammatory signaling. These findings reveal epithelial cell plasticity as a central driver of divergent repair outcomes following renal transplant AKI and highlight epithelial-immune-stromal crosstalk as a therapeutic target to promote recovery and prevent chronic graft injury.

One Sentence Summary

Single-cell and spatial mapping of human kidney transplants reveal regenerative and fibrotic cell programs across tubular, immune, and stromal compartments that determine whether acute injury resolves or progresses to chronic allograft injury.

Large-Scale Metabolite Imaging Gallery of Mouse Organ Tissues to Study Spatial Metabolism

Mass spectrometry imaging (MSI) has revolutionized the study of tissue metabolism by enabling the visualization of small molecule metabolites (SMMs) with high spatial resolution. However, comprehensive SMM imaging databases for different organ tissues are lacking, hindering our understanding of spatial organ metabolism. To address this resource gap, we present a large-scale SMM imaging gallery for mouse brain, kidney, and liver, capturing SMMs spanning eight chemical super classes and encompassing over 40 metabolic pathways. Manual curation and display of these imaging data sets unveil spatial patterns of metabolites that are less documented in the reported organs. Specifically, we identify 65 SMMs in brain coronal sections and 71 in sagittal tissue sections, including spatial patterns for neurotransmitters. Furthermore, we map 98 SMMs in kidneys and 66 SMMs in liver, providing insights into their amino acid and glutathione metabolism. Our insightful SMM imaging gallery serves as a critical resource for the spatial metabolism research community, filling a significant resource gap. This resource is freely available for download and can be accessed through the BioImage Archive and METASPACE repositories, providing high-quality annotated images for potential future computational models and advancing our understanding of tissue metabolism at the spatial level.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Metabolic regulation in adult and aging skeletal muscle stem cells

Adult stem cells maintain homeostasis and enable regeneration of most tissues. Quiescence, proliferation, and differentiation of stem cells and their progenitors are tightly regulated processes governed by dynamic transcriptional, epigenetic, and metabolic programs. Previously thought to merely reflect a cell's energy state, metabolism is now recognized for its critical regulatory functions, controlling not only energy and biomass production but also the cell's transcriptome and epigenome. In this review, we explore how metabolic pathways, metabolites, and transcriptional and epigenetic regulators are functionally interlinked in adult and aging skeletal muscle stem cells.

Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI

Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incorporate partial or coarse biological contexts to improve segmentation results and enable more effective three-dimensional MSI segmentation with reduced computational requirements. These are facilitated by two optional enhanced modes: scribble-interactive and knowledge-transfer. Numerous results demonstrate the robustness of these two modes, ensuring that GraphMSI consistently retains its capability to identify biologically relevant sub-regions in complex practical applications. It is anticipated that GraphMSI will become a powerful tool for spatial heterogeneity analysis in MSI data.

Spatiotemporal transcriptome and metabolome landscapes of cotton somatic embryos

Somatic embryogenesis (SE) is a developmental process related to the regeneration of tissue-cultured plants, which serves as a useful technique for crop breeding and improvement. However, SE in cotton is difficult and elusive due to the lack of precise cellular level information on the reprogramming of gene expression patterns involved in somatic embryogenesis. Here, we investigate the spatial and single-cell expression profiles of key genes and the metabolic patterns of key metabolites by integrated single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics (ST), and spatial metabolomics (SM). To evaluate the results of these analyses, we functionally characterized the potential roles of two representative marker genes, AATP1 and DOX2, in the regulation of cotton somatic embryo development. A publicly available web-based resource database ( https://cotton.cricaas.com.cn/somaticembryo/ ) in this study provides convenience for future studies of the expression patterns of marker genes at specific developmental stages during the process of SE in cotton.

Spatial and Single-Cell Analyses Reveal Heterogeneity of DNAM-1 Receptor-Ligand Interactions That Instructs Intratumoral γδT-cell Activity

The dynamic interplay between tumor cells and γδT cells within the tumor microenvironment significantly influences disease progression and immunotherapy outcome. In this study, we delved into the modulation of γδT-cell activation by tumor cell ligands CD112 and CD155, which interact with the activating receptor DNAM-1 on γδT cells. Spatial and single-cell RNA sequencing, as well as spatial metabolomic analysis, from neuroblastoma revealed that the expression levels and localization of CD112 and CD155 varied across and within tumors, correlating with differentiation status, metabolic pathways, and ultimately disease prognosis and patient survival. Both in vivo tumor xenograft experiments and in vitro coculture experiments demonstrated that a high CD112/CD155 expression ratio in tumors enhanced γδT cell-mediated cytotoxicity, whereas a low ratio fostered tumor resistance. Mechanistically, CD112 sustained DNAM-1-mediated γδT-cell activation, whereas CD155 downregulated DNAM-1 expression via E3 ubiquitin ligase tripartite motif-containing 21-mediated ubiquitin proteasomal degradation. By interacting with tumor cells differentially expressing CD112 and CD155, intratumoral γδT cells exhibited varying degrees of activation and DNAM-1 expression, representing three major functional subsets. This study underscores the complexity of tumor-immune cross-talk, offering insights into how tumor heterogeneity shapes the immune landscape. Significance: Tumor cells in different intratumoral neighborhoods display divergent patterns of ligands that regulate γδT-cell activation, highlighting multilevel regulation of antitumor immunity resulting from the heterogeneity of intercellular interactions in the tumor microenvironment.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

To image or not to image: Use of imaging mass spectrometry in biomedical lipidomics

Lipid imaging mass spectrometry (LIMS) allows for establishing the bidimensional distribution of lipid species within a tissue section. One of the main advantages is the generation of spatial information on lipid species distribution at a spatial (lateral) resolution bordering on single-cell resolution with no need to isolate cells. Thus, LIMS images demonstrate, with a level of detail never described before, that lipid profiles are highly sensitive to cell type and pathophysiological state. The wealth and relevance of the information conveyed by LIMS makes up for the lack of a separation stage before sample injection into the mass analyzer, which can somehow be circumvented by other means. Hence, the possibility of describing the lipidome at the cellular level while preserving the microenvironment offers an incomparable opportunity to investigate physiological and pathological contexts. However, to fully grasp the biological implications of the lipid profiles, it is essential to contextualize LIMS data within the broader multiscale 'omic' landscape, entailing genomics, epigenomics, and proteomics, each offering a unique window into the regulatory layers of the cell. In this line, the number of techniques that can be combined with LIMS to delve into the molecular mechanisms underlying differential lipid profiles is continuously increasing. Herein, we aim to describe the key features of LIMS analyses, from sample preparation to data interpretation, as well as the current methodologies to enrich and complete the final outcome. While the field is rapidly advancing, we consider there is solid evidence to foresee the incorporation of LIMS into clinical environments.

In Situ Isotope Tracing at Single-Cell Resolution Using Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) allows for label-free spatial molecular interrogation of tissues. With advances in the field over recent years, the spatial resolution at which MSI data can be recorded has reached the single-cell level. This makes MSI complementary to other single-cell omics technologies. As metabolism is a highly dynamic process, capturing the metabolic turnover adds a valuable layer of information. Here, we describe how to set up in situ stable isotope tracing followed by MSI-enabled spatial metabolomics to perform dynamic metabolomics at the single-cell level.

A Newly Identified Protective Role of C5a Receptor 1 in Kidney Tubules against Toxin-Induced Acute Kidney Injury

Acute kidney injury (AKI) remains a major reason for hospitalization with limited therapeutic options. Although complement activation is implicated in AKI, the role of C5a receptor 1 (C5aR1) in kidney tubular cells is unclear. Herein, aristolochic acid nephropathy (AAN) and folic acid nephropathy (FAN) models were used to establish the role of C5aR1 in kidney tubules during AKI in germline C5ar1-/-, myeloid cell-specific, and kidney tubule-specific C5ar1 knockout mice. After aristolochic acid and folic acid injection, C5ar1-/- mice had increased AKI severity and a higher degree of tubular injury. Macrophage depletion in C5ar1-/- mice or myeloid cell-specific C5ar1 deletion did not affect the outcomes of aristolochic acid-induced AKI. RNA-sequencing data from renal tubular epithelial cells (RTECs) showed that C5ar1 deletion was associated with the down-regulation of mitochondrial metabolism and ATP production transcriptional pathways. Metabolic studies confirmed reduced mitochondrial membrane potential at baseline and increased mitochondrial oxidative stress after injury in C5ar1-/- RTECs. Moreover, C5ar1-/- RTECs had enhanced glycolysis, glucose uptake, and lactate production on injury, corroborated by metabolomics analysis of kidneys from AAN mice. Kidney tubule-specific C5ar1 knockout mice recapitulated exacerbated AKI observed in C5ar1-/- mice in AAN and FAN. These data indicate that C5aR1 signaling in kidney tubules exerts renoprotective effects against toxin-induced AKI by limiting overt glycolysis and maintaining mitochondrial function, thereby revealing a novel link between the complement system and tubular cell metabolism.

(Pre)Clinical Metabolomics Analysis

Metabolomics is the scientific field with the eager goal to comprehensively analyze the entirety of all small molecules of a biological system, i.e., the metabolome. Over the last few years, metabolomics has matured to become an analytical cornerstone of life science research across diverse fields, from fundamental biochemical applications to preclinical studies, including biomarker discovery and drug development. In this chapter, we provide an introduction to (pre)clinical metabolomics. We define key metabolomics aspects and provide the basis to thoroughly understand the relevance of this field in a biological and clinical context. We present and explain state-of-the-art analytical technologies devoted to metabolomic analysis as well as emerging technologies, discussing both strengths and weaknesses. Given the ever-increasing demand for handling complex datasets, the role of bioinformatics approaches in the context of metabolomic analysis is also illustrated.

Metabolism at the crossroads of inflammation and fibrosis in chronic kidney disease

Chronic kidney disease (CKD), defined as persistent (>3 months) kidney functional loss, has a growing prevalence (>10% worldwide population) and limited treatment options. Fibrosis driven by the aberrant accumulation of extracellular matrix is the final common pathway of nearly all types of chronic repetitive injury in the kidney and is considered a hallmark of CKD. Myofibroblasts are key extracellular matrix-producing cells that are activated by crosstalk between damaged tubules and immune cells. Emerging evidence indicates that metabolic alterations are crucial contributors to the pathogenesis of kidney fibrosis by affecting cellular bioenergetics and metabolite signalling. Immune cell functions are intricately connected to their metabolic characteristics, and kidney cells seem to undergo cell-type-specific metabolic shifts in response to damage, all of which can determine injury and repair responses in CKD. A detailed understanding of the heterogeneity in metabolic reprogramming of different kidney cellular subsets is essential to elucidating communication processes between cell types and to enabling the development of metabolism-based innovative therapeutic strategies against CKD.

Age-related TFEB downregulation in proximal tubules causes systemic metabolic disorders and occasional apolipoprotein A4-related amyloidosis

With the aging of society, the incidence of chronic kidney disease (CKD), a common cause of death, has been increasing. Transcription factor EB (TFEB), the master transcriptional regulator of the autophagy/lysosomal pathway, is regarded as a promising candidate for preventing various age-related diseases. However, whether TFEB in the proximal tubules plays a significant role in elderly patients with CKD remains unknown. First, we found that nuclear TFEB localization in proximal tubular epithelial cells (PTECs) declined with age in both mice and humans. Next, we generated PTEC-specific Tfeb-deficient mice and bred them for up to 24 months. We found that TFEB deficiency in the proximal tubules caused metabolic disorders and occasionally led to apolipoprotein A4 (APOA4) amyloidosis. Supporting this result, we identified markedly decreased nuclear TFEB localization in the proximal tubules of elderly patients with APOA4 amyloidosis. The metabolic disturbances were accompanied by mitochondrial dysfunction due to transcriptional changes involved in fatty acid oxidation and oxidative phosphorylation pathways, as well as decreased mitochondrial clearance. This decreased clearance was reflected by the accumulation of mitochondria-lysosome-related organelles, which depended on lysosomal function. These results shed light on the presumptive mechanisms of APOA4 amyloidosis pathogenesis and provide a therapeutic strategy for CKD-related metabolic disorders and APOA4 amyloidosis.

The Impact of Gestational Diabetes on Kidney Development: is There an Epigenetic Link?

PURPOSE OF REVIEW

This review explores the mechanisms through which gestational diabetes mellitus GDM impacts fetal kidney development, focusing on epigenetic alterations as mediators of these effects. We examine the influence of GDM on nephrogenesis and kidney maturation, exploring how hyperglycemia-induced intrauterine stress can reduce nephron endowment and compromise renal function via dysregulation of normal epigenetic mechanisms.

RECENT FINDINGS

In addition to metabolic impacts, emerging evidence suggests that GDM exerts its influence through epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNA expression, which disrupt gene expression patterns critical for kidney development. Recently, specific epigenetic modifications observed in offspring exposed to GDM were implicated in aberrant activation or repression of genes essential for kidney development. Key pathways influenced by these epigenetic changes, such as oxidative stress response, inflammatory regulation, and metabolic pathways, are discussed to illustrate the broad molecular impact of GDM on renal development. Finally, we consider potential intervention strategies that could mitigate the adverse effects of GDM on kidney development. These include optimizing maternal glycemic control, dietary modifications, dietary supplementation, and pharmacological agents targeting epigenetic pathways. Through a comprehensive synthesis of current research, this review underscores the importance of early preventive strategies to reduce the burden of kidney disease in individuals exposed to GDM and highlights key epigenetic mechanisms altered during GDM that impact kidney development.

Machine Learning and Metabolomics Predict Mesenchymal Stem Cell Osteogenic Differentiation in 2D and 3D Cultures

Stem cells have been widely used to produce artificial bone grafts. Nonetheless, the variability in the degree of stem cell differentiation is an inherent drawback of artificial graft development and requires robust evaluation tools that can certify the quality of stem cell-based products and avoid source-tissue-related and patient-specific variability in outcomes. Omics analyses have been utilised for the evaluation of stem cell attributes in all stages of stem cell biomanufacturing. Herein, metabolomics in combination with machine learning was utilised for the benchmarking of osteogenic differentiation quality in 2D and 3D cultures. Metabolomics analysis was performed with the use of gas chromatography-mass spectrometry (GC-MS). A set of 11 metabolites was used to train an XGboost model which achieved excellent performance in distinguishing between differentiated and undifferentiated umbilical cord blood mesenchymal stem cells (UCB MSCs). The model was benchmarked against samples not present in the training set, being able to efficiently capture osteogenesis in 3D UCB MSC cultures with an area under the curve (AUC) of 82.6%. On the contrary, the model did not capture any differentiation in Wharton's Jelly MSC samples, which are well-known underperformers in osteogenic differentiation (AUC of 56.2%). Mineralisation was significantly correlated with the levels of fumarate, glycerol, and myo-inositol, the four metabolites found most important for model performance (R2 = 0.89, R2 = 0.94, and R2 = 0.96, and p = 0.016, p = 0.0059, and p = 0.0022, respectively). In conclusion, our results indicate that metabolomics in combination with machine learning can be used for the development of reliable potency assays for the evaluation of Advanced Therapy Medicinal Products.

The roles of hyaluronan in kidney development, physiology and disease

The hyaluronan (HA) matrix in the tissue microenvironment is crucial for maintaining homeostasis by regulating inflammatory signalling, endothelial-mesenchymal transition and cell migration. During development, covalent modifications and osmotic swelling of HA create mechanical forces that initiate midgut rotation, vascular patterning and branching morphogenesis. Together with its main cell surface receptor, CD44, HA establishes a physicochemical scaffold at the cell surface that facilitates the interaction and clustering of growth factors and receptors that is required for normal physiology. High-molecular-weight HA, tumour necrosis factor-stimulated gene 6, pentraxin 3 and CD44 form a stable pericellular matrix that promotes tissue regeneration and reduces inflammation. By contrast, breakdown of high-molecular-weight HA into depolymerized fragments by hyaluronidases triggers inflammatory signalling, leukocyte migration and angiogenesis, contributing to tissue damage and fibrosis in kidney disease. Targeting HA metabolism is challenging owing to its dynamic regulation and tissue-specific functions. Nonetheless, modulating HA matrix functions by targeting its binding partners holds promise as a therapeutic strategy for restoring tissue homeostasis and mitigating pathological processes. Further research in this area is warranted to enable the development of novel therapeutic approaches for kidney and other diseases characterized by dysregulated HA metabolism.

Spatial metabolomics reveals key features of hippocampal lipid changes in rats with postoperative cognitive dysfunction

Postoperative cognitive dysfunction (POCD) is a common complication after cardiac surgery. Numerous evidence suggest that dysregulation of lipid metabolism is associated with cognitive impairment; however, its precise role in the development of POCD is still obscure. In this study, we established a cardiopulmonary bypass (CPB) model in rats and employed the Barnes maze to assess cognitive function, selecting POCD rats for subsequent experimentation. Utilizing mass spectrometry imaging, we detected plenty of lipids accumulates within the hippocampal CA1in the POCD group. Immunofluorescence staining revealed a significant reduction in the fluorescence intensity of calcium-independent phospholipases A2 (iPLA2) in the POCD group compared to the control, while serine palmitoyl transferase (SPT) was markedly increased in the POCD group. Transmission electron microscopy revealed that the number of synapses in hippocampal CA1decreased significantly and postsynaptic density became thinner in POCD group. Furthermore, after reversing the metabolic disorders of iPLA2 and SPT in the rat brain with docosahexaenoic acid and myriocin, the incidence of POCD after CPB was significantly reduced and the disrupted lipid metabolism in the hippocampus was also normalized. These findings may offer a novel perspective for exploring the etiology and prevention strategies of POCD after CPB.

Cell-specific oxidative metabolism of the renal cortex

The metabolic health of the kidney is a primary determinant of the risk of progressive kidney disease. Our understanding of the metabolic processes that fuel kidney functions is limited by the kidney's structural and functional heterogeneity. As the kidney contains many different cell types, we hypothesize that intra-renal mitochondrial heterogeneity contributes to cell-specific metabolism. To interrogate this, we utilized a recently developed mitochondrial tagging technique to isolate kidney cell-type specific mitochondria. Here, we investigate mitochondrial functional capacities and the metabolomes of the early and late proximal tubule (PT) and the distal convoluted tubule (DCT). The conditional MITO-Tag allele was combined with Slc34a1-CreERT2 , Ggt1-Cre , or Pvalb-Cre alleles to generate mouse models capable of cell-specific isolation of hemagglutinin (HA)-tagged mitochondria from the early PT, late PT, or the DCT, respectively. Functional assays measuring mitochondrial respiratory and fatty acid oxidation (FAO) capacities and metabolomics were performed on anti-HA immunoprecipitated mitochondria from kidneys of ad libitum fed and 24-hour fasted male mice. The renal MITO-Tag models targeting the early PT, late PT, and DCT revealed differential mitochondrial respiratory and FAO capacities which dynamically changed during fasting conditions. Changes with mitochondrial metabolomes induced by fasting suggest that the late PT significantly increases FAO during fasting. The renal MITO-Tag model captured differential mitochondrial metabolism and functional capacities across the early PT, late PT, and DCT at baseline and in response to fasting.

Translational Statement

While the renal cortex is often considered a single metabolic compartment, we discovered significant diversity of mitochondrial metabolomes and functional capacities across the proximal tubule and the distal convoluted tubule. As mitochondrial dysfunction is a major biochemical pathway related to kidney disease progression, understanding the differences in mitochondrial metabolism across distinct kidney cell populations is thus critical in the development of effective and targeted therapeutic therapies for acute and chronic kidney disease.

An artificial metabzyme for tumour-cell-specific metabolic therapy

Metabolic dysregulation constitutes a pivotal feature of cancer progression. Enzymes with multiple metal active sites play a major role in this process. Here we report the first metabolic-enzyme-like FeMoO4 nanocatalyst, dubbed 'artificial metabzyme'. It showcases dual active centres, namely, Fe2+ and tetrahedral Mo4+, that mirror the characteristic architecture of the archetypal metabolic enzyme xanthine oxidoreductase. Employing spatially dynamic metabolomics in conjunction with the assessments of tumour-associated metabolites, we demonstrate that FeMoO4 metabzyme catalyses the metabolic conversion of tumour-abundant xanthine into uric acid. Subsequent metabolic adjustments orchestrate crosstalk with immune cells, suggesting a potential therapeutic pathway for cancer. Our study introduces an innovative paradigm in cancer therapy, where tumour cells are metabolically reprogrammed to autonomously modulate and directly interface with immune cells through the intervention of an artificial metabzyme, for tumour-cell-specific metabolic therapy.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

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.

Challenges in Spatial Metabolomics and Proteomics for Functional Tissue Unit and Single-Cell Resolution

In the last decade, advanced developments of mass spectrometry-based assays have made spatial measurements of hundreds of metabolites and thousands of proteins not only possible, but routine. The information obtained from such mass spectrometry imaging experiments traces metabolic events and helps decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein we overview developments in the field over the past decade, highlighting several case studies demonstrating direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues at the level of functional tissue units, approaching single-cell levels. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspectives on paths forward and the challenges that persist with adoption and application of these spatial omics techniques at the single-cell level on mammalian kidneys. Data analysis and reanalysis still pose issues that plague spatial omics, but many mass spectrometry imaging platforms are commercially available. With greater harmonization across platforms and rigorous quality control, greater adoption of these platforms will undoubtedly provide major insights in complex diseases.

Multi-Omics Integration in Nephrology: Advances, Challenges, and Future Directions

Omics technologies have transformed nephrology, providing deep insights into molecular mechanisms of kidney disease and enabling more precise diagnostic tools, therapeutic strategies, and prognostic markers. Multi-omics data integration, spanning bulk, single-cell, and spatial omics, offers a comprehensive view of kidney biology in health and disease. In this review, we explore methods and challenges for integrating transcriptomic, epigenomic, and spatial data. By combining omics layers, researchers can uncover novel molecular interactions and spatial tissue organization, advancing our understanding of diseases like diabetic kidney disease and autosomal polycystic kidney disease. This integrated approach is reshaping diagnostic and therapeutic strategies in nephrology and is critical for optimizing insights available from spatial and multi-omics analysis.

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.

Spatial Metabolomics and Lipidomics in Kidney Disease

Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex and dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney, MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques, such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning the localization of changes in various disease states. Additionally, when combined with pathology, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights into disease mechanisms.

Spatial multi-omics in whole skeletal muscle reveals complex tissue architecture

Myofibers are large multinucleated cells that have long thought to have a rather simple organization. Single-nucleus transcriptomics, spatial transcriptomics and spatial metabolomics analysis have revealed distinct transcription profiles in myonuclei related to myofiber type. However, the use of local tissue collection or dissociation methods have obscured the spatial organization. To elucidate the full tissue architecture, we combine two spatial omics, RNA tomography and mass spectrometry imaging. This enables us to map the spatial transcriptomic, metabolomic and lipidomic organization of the whole murine tibialis anterior muscle. Our findings on heterogeneity in fiber type proportions are validated with multiplexed immunofluorescence staining in tibialis anterior, extensor digitorum longus and soleus. Our results demonstrate unexpectedly strong regionalization of gene expression, metabolic differences and variable myofiber type proportion along the proximal-distal axis. These new insights in whole-tissue level organization reconcile sometimes conflicting results coming from previous studies relying on local sampling methods.

Conjugate Polymer Anchor Enhancing Matrix Vacuum Stability and Improving MALDI MSI via Ion Bond

Poor vacuum stability limits the application of many matrices in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) that requires long-term measurement duration in high vacuum. In this study, a new approach using conjugate polymer anchor to protect unstable matrix from volatilizing in MALDI source based on ion bond is provided. Unlike strong covalent bonds which often introduce unnecessary groups, the weaker ion bonds are more conducive to breaking under laser radiation while effectively preventing matrix volatilization in a vacuum environment. The results confirm that conjugate polymer anchor will neither introduce additional ion peaks nor affect signal intensity, yet maintains comparable quantification properties. Vacuum stability of three kinds of typical matrices is enhanced using polymer anchors, and the in situ MALDI MS imaging of mouse brain and liver cancer is improved significantly.

Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease

Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.

Metabolism and bioenergetics in the pathophysiology of organ fibrosis

Fibrosis is the tissue scarring characterized by excess deposition of extracellular matrix (ECM) proteins, mainly collagens. A fibrotic response can take place in any tissue of the body and is the result of an imbalanced reaction to inflammation and wound healing. Metabolism has emerged as a major driver of fibrotic diseases. While glycolytic shifts appear to be a key metabolic switch in activated stromal ECM-producing cells, several other cell types such as immune cells, whose functions are intricately connected to their metabolic characteristics, form a complex network of pro-fibrotic cellular crosstalk. This review purports to clarify shared and particular cellular responses and mechanisms across organs and etiologies. We discuss the impact of the cell-type specific metabolic reprogramming in fibrotic diseases in both experimental and human pathology settings, providing a rationale for new therapeutic interventions based on metabolism-targeted antifibrotic agents.

Renal tubular epithelial cells response to injury in acute kidney injury

Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid and significant decrease in renal function that can arise from various etiologies, and is associated with high morbidity and mortality. The renal tubular epithelial cells (TECs) represent the central cell type affected by AKI, and their notable regenerative capacity is critical for the recovery of renal function in afflicted patients. The adaptive repair process initiated by surviving TECs following mild AKI facilitates full renal recovery. Conversely, when injury is severe or persistent, it allows the TECs to undergo pathological responses, abnormal adaptive repair and phenotypic transformation, which will lead to the development of renal fibrosis. Given the implications of TECs fate after injury in renal outcomes, a deeper understanding of these mechanisms is necessary to identify promising therapeutic targets and biomarkers of the repair process in the human kidney.

Host-pathogen interactions from a metabolic perspective: methods of investigation

Metabolism shapes immune homeostasis in health and disease. This review presents the range of methods that are currently available to investigate the dialog between metabolism and immunity at the systemic, tissue and cellular levels, particularly during infection.

Spatial metabolomics in tissue injury and regeneration

Tissue homeostasis is intricately linked to cellular metabolism and metabolite exchange within the tissue microenvironment. The orchestration of adaptive cellular responses during injury and repair depends critically upon metabolic adaptation. This adaptation, in turn, shapes cell fate decisions required for the restoration of tissue homeostasis. Understanding the nuances of metabolic processes within the tissue context and comprehending the intricate communication between cells is therefore imperative for unraveling the complexity of tissue homeostasis and the processes of injury and repair. In this review, we focus on mass spectrometry imaging as an advanced platform with the potential to provide such comprehensive insights into the metabolic instruction governing tissue function. Recent advances in this technology allow to decipher the intricate metabolic networks that determine cellular behavior in the context of tissue resilience, injury, and repair. These insights not only advance our fundamental understanding of tissue biology but also hold implications for therapeutic interventions by targeting metabolic pathways critical for maintaining tissue homeostasis.

Spatiotemporal Landscape of Kidney Tubular Responses to Glomerular Proteinuria

Key Points

Glomerular proteinuria induces large-scale changes in gene expression along the nephron. Increased protein uptake in the proximal tubule results in axial remodeling and injury. Increased protein delivery to the distal tubule causes dedifferentiation of the epithelium.

Background

Large increases in glomerular protein filtration induce major changes in body homeostasis and are associated with a higher risk of kidney functional decline and cardiovascular disease. We investigated how elevated protein exposure modifies the landscape of tubular function along the entire nephron, to understand the cellular changes that mediate these important clinical phenomena.

Methods

We conducted single-nucleus RNA sequencing, functional intravital imaging, and antibody staining to spatially map transport processes along the mouse kidney tubule. We then delineated how these were altered in a transgenic mouse model of inducible glomerular proteinuria (POD-ATTAC) at 7 and 28 days.

Results

Glomerular proteinuria activated large-scale and pleiotropic changes in gene expression in all major nephron sections. Extension of protein uptake from early (S1) to later (S2) parts of the proximal tubule initially triggered dramatic expansion of a hybrid S1/2 population, followed by injury and failed repair, with the cumulative effect of loss of canonical S2 functions. Proteinuria also induced acute injury in S3. Meanwhile, overflow of luminal proteins to the distal tubule caused transcriptional convergence between specialized regions and generalized dedifferentiation.

Conclusions

Proteinuria modulated cell signaling in tubular epithelia and caused distinct patterns of remodeling and injury in a segment-specific manner.

Podcast

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2024_05_01_ASN0000000000000357.mp3

High-Specificity Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) enables highly multiplexed, untargeted tissue mapping for a broad range of molecular classes, facilitating in situ biological discovery. Yet, challenges persist in molecular specificity, which is the ability to discern one molecule from another, and spatial specificity, which is the ability to link untargeted imaging data to specific tissue features. Instrumental developments have dramatically improved IMS spatial resolution, allowing molecular observations to be more readily associated with distinct tissue features across spatial scales, ranging from larger anatomical regions to single cells. High-performance mass analyzers and systems integrating ion mobility technologies are also becoming more prevalent, further improving molecular coverage and the ability to discern chemical identity. This review provides an overview of recent advancements in high-specificity IMS that are providing critical biological context to untargeted molecular imaging, enabling integrated analyses, and addressing advanced biomedical research applications.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

New tools to study renal fibrogenesis

PURPOSE OF REVIEW

Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development.

RECENT FINDINGS

We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis.

SUMMARY

As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.

Spatio-Temporal Study of Galactolipid Biosynthesis in Duckweed Using Mass Spectrometry Imaging and in vivo Isotope Labeling

Isotope labeling coupled with mass spectrometry imaging (MSI) presents a potent strategy for elucidating the dynamics of metabolism at cellular resolution, yet its application to plant systems is scarce. It has the potential to reveal the spatio-temporal dynamics of lipid biosynthesis during plant development. In this study, we explore its application to galactolipid biosynthesis of an aquatic plant, Lemna minor, with D2O labeling. Specifically, matrix-assisted laser desorption/ionization-MSI data of two major galactolipids in L. minor, monogalactosyldiacylglycerol and digalactosyldiacylglycerol, were studied after growing in 50% D2O media over a 15-day time period. When they were partially labeled after 5 d, three distinct binomial isotopologue distributions were observed corresponding to the labeling of partial structural moieties: galactose only, galactose and a fatty acyl chain and the entire molecule. The temporal change in the relative abundance of these distributions follows the expected linear pathway of galactolipid biosynthesis. Notably, their mass spectrometry images revealed the localization of each isotopologue group to the old parent frond, the intermediate tissues and the newly grown daughter fronds. Besides, two additional labeling experiments, (i) 13CO2 labeling and (ii) backward labeling of completely 50% D2O-labeled L. minor in H2O media, confirm the observations in forward labeling. Furthermore, these experiments unveiled hidden isotopologue distributions indicative of membrane lipid restructuring. This study suggests the potential of isotope labeling using MSI to provide spatio-temporal details in lipid biosynthesis in plant development.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.