INTRODUCTION

Renal fibrosis is a critical pathological feature in the progression of chronic kidney diseases (CKD) and has significant implications for global health. Fibrosis, in general, is responsible for up to 45% of deaths in the developed world, underlining its severity and impact across various organ systems [2]. Renal fibrosis, in particular, represents a common endpoint for a multitude of kidney diseases, leading to the irreversible loss of kidney function and necessitating costly treatments like dialysis or transplantation. The global prevalence of CKD is alarmingly high, affecting approximately 10% of the worldwide population [3]. Renal fibrosis involves the formation and accumulation of extracellular matrix (ECM) mainly by tissue-resident mesenchymal cells in the tubulo-interstitial compartment. We recently revealed that distinct tissue-resident fibroblasts and pericytes contribute most to the myofibroblast cells in the human kidney [1]. Myofibroblast differentiation is a secondary response to various types of injuries and is closely interconnected with tissue remodelling and inflammation. The process is orchestrated by the cross-talk of multiple cell types, including epithelial, endothelial, and inflammatory cells, as well as mesenchymal cells like fibroblasts and myofibroblasts. The progression of kidney fibrosis is driven by repeated or sustained tissue injury, also identified as the driver of acute kidney injury (AKI)-CKD transition, which disrupts normal tissue architecture and organ function. No specific antifibrotic therapy is currently available, mainly because the underlying molecular and cellular mechanisms are not entirely understood. Recently developed single-cell and spatial multiomics methods offer an opportunity to holistically analyse these complex changes and dependencies, including all cell types of the kidney. These methods can be applied to biobanked human tissues, including fresh, frozen or formalin-fixed paraffin-embedded (FFPE) tissues, and offer exciting opportunities to uncover novel mechanistic insights. This review will highlight these recent developments and focus on how these methods can be applied to uncover novel disease insights regarding cell lineages, plasticity and cell-cell communication dynamics. 

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ANALYSIS OF CELL LINEAGES IN RENAL FIBROGENESIS

Prior work on finding the cellular origins of ECM-producing cells of the tubulointerstitial or glomerular compartment in kidney disease incorporated lineage tracing as an experimental technology to identify all progeny from a single cell or cell-type following injury. In particular, genetic lineage systems involving the heritable labelling of cells in transgenic mice by inducible recombination led to many insights into the mechanisms of kidney fibrosis and glomerulosclerosis [4–6]. However, these methods are limited in simultaneously revealing single-cell lineage and cell identification information, which is necessary to uncover the regulators and principles of cell fate choices in disease. Recently developed transgenic mouse models close this gap. In vivo systems like CARLIN (CRISPR-Actuated Recombinase Lineage tracing) [7], DARLIN (Doxycycline-Activated Recombinase Lineage tracing) [8▪▪], or intMEMOIRE (integrated Memory Of Inferred REgulation) [9] are among these approaches. CARLIN and DARLIN mice utilize CRISPR-Cas9 genetic barcoding in combination with site-specific recombinases, such as Cre-loxP or Flp-FRT systems, enabling the activation of fluorescent reporters and genetic markers in a doxycycline-dependent manner. This allows for precise temporal and spatial control over lineage tracing in mice, facilitating the study of complex developmental processes, tissue regeneration, and disease progression with unprecedented detail. While organoids and complex 3D multicellular constructs are becoming more widely available, the recently developed method called CellTag-multi expands the CRISPR-based lineage tracing toolkits for in-vitro systems [10▪▪]. Jindal et al. developed CellTag-multi by leveraging CellTags-heritable DNA barcodes expressed as polyadenylated transcripts that can be captured in both scRNA-seq and scATAC-seq enabling the clonal tracing of transcriptomic and epigenomic simultaneously [10▪▪].

Although these CRISPR-based barcoding systems are of interest for in-vitro and in-vivo experimental models they cannot be applied to human biobanked tissues. Cells in human tissues harbour their own ‘barcodes’ induced by e.g. somatic mutations. However, to uncover this information whole-genome sequencing of single cells would be necessary and thus not scalable due to increased overall costs. Since the mutation rate is much higher in the mitochondrial genome, researchers have established a lineage-tracing approach based on mitochondrial DNA mutations [11▪▪,12]. Mitochondrial DNA-based lineage tracing has been applied mainly to circulating cells in haematological malignancies and not solid tissues, but could potentially be leveraged and applied to human kidney tissues in health and CKD as well to shed insights on lineage trajectories of cells in fibrosis.

METHODS TO INVESTIGATE PLASTICITY OF RENAL FIBROGENESIS

Recent advances in the study of renal fibrogenesis have highlighted the pivotal role of epigenomic mechanisms and their contribution to cellular plasticity and kidney injury repair processes. Key techniques such as bulk epigenomics (including ATAC sequencing [13], Cut&Run [14], Cut&Tag [15] and Multi-Cut&Tag [16]) and more refined approaches like single-cell ATAC and multiome analysis, along with spatial multiomics (Cut&Tag with DBiT-Seq), have been instrumental in unravelling the complex epigenetic reprogramming during acute kidney injury (AKI) to chronic kidney disease (CKD) transition. These methodologies have uncovered how transcription factors and epigenomic changes, including DNA methylation, histone modification, and chromatin remodelling, dictate the cellular phenotype, thereby influencing the kidney's response to injury. Notably, studies by Muto et al.[17] and Gisch et al.[18▪▪] have provided insights into the epigenomic landscape of renal fibrogenesis, identifying key transcription factors such as CREB5, ELF3, KLF6, and KLF10 that regulate cellular responses and potentially offer new targets for preventing the progression from AKI to CKD. These findings underscore the importance of a multiomic approach to understanding and intervening in the pathogenesis of renal fibrosis. Direct reprogramming experiments described by Kaminski et al.[19] coupled with these novel multiomic approaches might shed further insights into actionable targets to reverse the AKI-to-CKD transition tubular cell phenotype.

SPATIAL ANALYSIS OF CELL-CELL COMMUNICATION

The factors driving fibrogenesis are complex, including the communication pathways between neighbouring cells and the effects of surrounding microenvironments [20,21]. Therefore, investigation of cell-cell communication is essential for a comprehensive understanding of the molecular mechanisms of renal fibrogenesis, which is not only restricted to the single-cell level but also needs to encompass other aspects such as ligand-receptor interactions and spatial context [22]. Since a variety of new techniques are available selecting an appropriate method according to the tissue type and the scientific question is important. In general spatial transcriptomic methodologies can be classified into imaging-based and sequencing-based approaches.

Sequencing-based spatial transcriptomics approaches enable whole-transcriptome capture in an unbiased manner. Most of the NGS-based approaches leverage spatially barcoded arrays or a microfluidic system to obtain the spatial location of the transcriptome or epigenome. Spatial Transcriptomic (ST) is one of the most well established sequencing-based techniques that utilise immobilised barcoded oligonucleotides on the surface of glass slides to capture mRNA. This approach has been commercialised as Visium by 10X Genomics and increased the spot resolution from 100 μm to 55 μm [23]. We recently used Visium to identify spatially resolved cell-type niches, neighbourhood dependencies and signalling pathways in myocardial infarction (MI) samples [24]. We integrated snRNA-seq, snATAC-seq and GWAS data that characterized the disease-specific cell-type enrichment, differentiation and regulation post-MI, thus revealing the molecular mechanisms of human cardiac remodelling processes with spatial resolution. Lake et al.[25▪▪] recently presented a multiomic atlas to comprehensively define cell phenotypes of the human kidney by integrating single-cell RNA and multiomic sequencing (Chromium v3 and SNARE2), spatial transcriptomics (Visium and Slide-seq2) and immunofluorescence staining plus 3D imaging data from distinct tissue regions and incorporated healthy, AKI and CKD specimens. They identified the colocalization of altered cell types, distinct cellular niches and the injury microenvironments, which further uncovered the transcriptional regulators of maladaptive repair across the AKI to CKD trajectory. The Visium assay has been applied widely to studying various aspects of kidney diseases. For instance, Ferreira et al.[26] exploited Visium to explore spatial gene expression and regional relationships between immune cells and proximal tubules in AKI models. Chen et al.[27] determined the co-localization of venous endothelial cells and fibroblast, with immune cells enriched in the fibrotic area of patients with diabetic kidney disease (DKD), and revealed cellular interactions and chemokines that participated in fibrosis development in DKD. Two other studies utilized Visium and integrated it with snRNA-seq data to characterize the spatially-resolved transcriptomics profiles of proximal tubule and papilla in renal calculi [28,29]. Lately, Cohen et al.[30] demonstrated that co-localization of FOLR2+ macrophages can be considered as a driving factor in the differentiation of inflammatory fibroblasts (CXCL-iFibro) to ECM-secreting myofibroblasts via WNT/ß-catenin-dependent pathway at early stages of CKD.

Collectively, the Visium platform is a proven technique that provides unprecedented opportunities for spatially resolved transcriptomics research, including the identification of domain-associated transcriptomics and cell-cell interactions. A marked advantage of Visium assay is the ability to obtain histological images coupled with spatial gene expression, thus enabling the combination of histopathological manifestations with gene expression at the lesion site. Furthermore, spatial isoform transcriptomic (SiT) was introduced based on the Visium platform, enabling spatial full-length RNA sequencing using long-read sequencing that offers the possibility of exploring isoform landscape and single nucleotide variation [31]. More recently, an advanced version of the Visium platform called whole transcriptome spatial discover in high definition (Visium HD) is about to be released, which utilises a slide with two 6.5 × 6.5 mm mapping area each containing ∼12 million 2 × 2 μm barcoded squares that binned to 8 × 8 μm as the visualisation resolution. Overlaying HE staining, Visium HD is capable of identifying cell types in fine anatomical structures, as well as immune cells that are difficult to detect by other spatial sequencing approaches. A more cost-effective assay could be the use of Illumina flow cells themselves, which have been already leveraged by several groups to generate high-resolution spatial transcriptomics at a fraction of the Visium HD costs [32,33].

Meanwhile, several sequencing-based high-resolution approaches have emerged in recent years reaching single-cell or even subcellular level. Slide-seq captures transcripts from tissue sections by randomly depositing 10 μm diameter DNA-barcoded beads to produce an indexed positioning array [34], whereas Slide-seq2 [35] improved the beads generation strategy upon Slide-seq, thereby greatly enhancing transcript recovery. The application of Slide-seq2 to healthy and diseased human kidney tissues (DKD and ischemia injury) unveiled cellular interactions between LYVE1+ macrophage populations and other cell types, including distal convoluted tubules and endothelial cells, as well as other immune cells, associated with renal fibrosis in injured medulla [36].

In contrast to array-based ST, DBiT-seq is based on a microfluidic system with resolutions ranging from 10 to 50 μm [37]. In DBiT-seq two perpendicular microfluidic chips containing parallel microchannels are used to generate spots with an individual barcode combination, which enables near-cellular high throughput and spatial multiomics profiling [38].

Imaging-based spatial transcriptomics relies on detecting transcripts by fluorescence in situ hybridization (FISH) or in-situ sequencing (ISS) (Fig. 1). While this usually requires the design of a gene panel and the multiplexing capabilities are limited by an increasing imaging time and cost as well as optical crowding effects, imaging-based approaches provide unparalleled subcellular spatial resolution, enable unambiguous allocation of transcripts to single cells when a cell boundary stain is included, and could even be used to measure 3D tissue sections [39,40]. Many approaches also allow for the use of FFPE tissue, which opens the possibility of incorporating many previously unused samples from pathology and biobanks to study renal fibrosis.

FIGURE 1:

Principles of imaging-based spatial transcriptomics. Imaging-based transcriptomic measurements can be carried out using either a smFISH (a) or ISS (b) approach. (a) Single-molecule fluorescence in situ hybridization (smFISH) approaches, such as MERFISH and seqFISH+, use a combinatorial barcoding approach to target transcripts of interest. Each transcript is hybridized with encoding probes containing a unique combination of oligonucleotide barcodes, which are detected in multiple rounds of imaging using complementary fluorescent imaging probes. (b) In-situ sequencing (ISS) approaches like STARmap, HybISS, or the commercialized Xenium assay use padlock probes to detect transcripts of interest. Bound padlock probes are ligated and amplified by rolling circle amplification to a cDNA nanoball containing the transcript-specific barcode, which is detected in multiple imaging rounds (as described above). (c) Each imaging cycle produces an image with multiple fluorescent spots. Depending on the assay, multiple fluorochromes are used and detected in the same imaging cycle to further increase multiplexing capabilities. Transcript identity can be computed from the combination of imaging cycles with detected fluorescence at the same spot, resulting in a microscopic image containing all detected transcripts with spatial coordinates.

The first publications demonstrate how imaging-based transcriptomics can interrogate cellular crosstalk and multicellular niches in kidney injury and fibrogenesis. A recent seqFISH study of AKI in mice identified injury-associated tissue niches that contained injured PT cells, fibroblasts and immune cells, showed a highly localized upregulation of Clcf1-Crfl1 signalling within those niches and different gene expression patterns in fibroblasts depending on the tissue microenvironment [41]. Notably, the imaging-based approach also allows the analysis of morphological features, e.g. correlating a nuclear flattening in PT cells with a higher expression of injury marker VCAM1. Kalhor et al.[42] developed an optimized ISS approach based on padlock-probe chemistry called DART-FISH, and analysed diseased kidneys. They detected several disease-associated cell states, found spatial colocalization patterns of myofibroblasts and altered thick ascending limb (TAL) cells and showed that ISS spatial data could reflect disease patterns on a morphological and molecular level. Currently, imaging-based methods like multiplexed error-robust fluorescence in-situ hybridisation (MERFISH) are being expanded to genomics [43], epigenomics [44] and multiomic approaches, including genome and transcriptome information [45]. The addition of proteomic and metabolomic information to multiomic data can be highly effective in better characterizing functional and metabolic changes in kidney disease, as well as performing precise phenotyping and cell segmentation in spatial transcriptomic datasets (Fig. 2). Spatial proteomic approaches can be combined with imaging-based transcriptomics on the same slide or consecutive sections, using multiplex immunofluorescence (IF) imaging or mass spectrometry imaging (MSI).

FIGURE 2:

Spatial multiomic datasets to study spatial patterns in kidney fibrosis. (a) MERFISH spatial transcriptomics dataset of kidney tissue. Each dot represents a detected transcript; transcript identity is coded by colour. (b) Spatial proteomics of the kidney by multiplex immunofluorescence. Protein stainings can be combined with imaging-based spatial transcriptomics for phenotyping cells or quantifying protein expression levels. Here, antibodies against some major cell types in the kidney were used: proximal tubule cells (green), thick ascending limb cells (orange), podocytes (purple), juxtaglomerular cells (pink), endothelial cells (yellow), vascular smooth muscle cells (red). (c) Protein stains of cell membrane components can be utilized to define cell borders more accurately for cell segmentation and consecutive computation analysis of cell-specific gene or protein expression. (d) Clustering of cellular gene and protein expression can identify the abundance and spatial distribution of cell types and cell states in the kidney, also detecting cell types that are typically underrepresented in single-cell data, like tissue-resident macrophages or neurons. (e) Spatial omic data enables the inspection of tissue microenvironments and multicellular spatial domains at different resolutions by integrating cellular gene expression profiles with their spatial interaction and colocalization to other cells.

Multiplex IF imaging approaches are based on multiple rounds of staining and imaging of fluorescently-labelled (CyCIF [46], IBEX [47], 4i [48]) or DNA-labelled antibodies (CODEX [49], immuno-SABER [50]). While this allows for a subcellular spatial resolution and is compatible with same-slide multiomic assays, the multiplexing capabilities are limited to 20–40 sometimes 100 s of antibodies by imaging time, costs, as well as antibody-cross reactivity or insufficient quenching in advanced imaging rounds.

Mass spectrometry imaging (MSI)-based spatial proteomics encompasses both antibody-based methods, such as MIBI-TOF using metal-labelled antibodies [51], and untargeted approaches, like Deep Visual Proteomics [52]. Untargeted MSI approaches allow for the measurement of a much higher number of analytes (including posttranslational modification of proteins) and MSI approaches can also measure metabolites or lipids. However, they generally have a lower spatial resolution and are incompatible with many same-slide multiomics assays [53,54].

The Kidney Precision Medicine Project (KPMP) used CODEX, IF-imaging, MALDI-MSI and single-cell sequencing data to build a comprehensive tissue atlas for human kidneys with cell-type specific gene, protein and metabolite information to be used as a reference for better characterizing cellular function and molecular changes in AKI and CKD [55]. The combination of MALDI-MSI and multiplex IF can be used to study the spatial distribution of cellular phenotypes and metabolic activity in the kidney. Wang et al.[56] found an altered metabolic profile in injured proximal tubule (PT) cells compared to healthy PT cells after acute kidney injury in mice (AKI). Interestingly, some metabolic alterations were also detectable in phenotypically healthy PT cells without expression of VCAM1. Another group combined MALDI-MSI and serum metabolomics to map spatial metabolic alterations in an AKI mouse model, demonstrating that changes in metabolite abundance in disease are localized to different kidney regions [57].

CONCLUSION

In conclusion, the advancements in single-cell and spatial multiomics methodologies are beginning to revolutionize the mechanistic understanding of fibrosis development in the human kidney. In the future, we anticipate novel insights and details on cell lineages, plasticity, and cell-cell communication in the context of fibrosis in chronic kidney disease. As these technologies continue to evolve, they offer promising routes for the development of tailored diagnostics and therapeutic strategies. This underscores the importance of interdisciplinary research and the need for ongoing investment in the development and application of novel research tools to address the complexities of kidney disease.

Acknowledgements

We would like to thank Daryna Pikulska and Paul Kießling for their help with the figures and discussion about the review.

Financial support and sponsorship

This work was supported by two grants of the German Research Foundation DFG, CRU-5011-445703531, and Emmy Noether EN-459969915 to C.K. by a grant from the European Research Council (ERC-StG-101040726), a grant from the Else Kroener Fresenius Foundation (EKFS), the Aventis Foundation, and by two BMBF grants Graphs4patients and AgedHeart (both to C.K.).

Conflicts of interest

The authors report no conflicts of interest.

REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

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