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Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors…. These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac . We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC . Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry ) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger . Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

Journal Article↗

Exploring molecular networks directly in the cell.

The hierarchy of cell function comprises at least four distinct functional levels: genome, transcriptome, proteome, and toponome. The toponome is the entirety of all protein networks traced out directly as patterns on the single cell level in the natural environment of cells in situ (e.g. tissues). In this work a photonic microscopic robot technology (MELK) capable of tagging and imaging hundreds (and possibly thousands) of different molecular components (e.g. proteins) of morphologically-intact fixed cells and tissue have been developed. MELK data sets represent multidimensional vectors of the topologically determined arrangements of proteins within the cell. The data, assembled in a toponome dictionary of the cell, give rise to a new concept for target and drug lead discovery.

Eukaryotic Cells↗

Deciphering novel targets in salivary gland pleomorphic adenoma by integrating plasma proteomics and parotid transcriptomics analyses.

BACKGROUND/PURPOSE: Pleomorphic adenoma (PA) is the most common salivary gland benign tumor, with its molecular drivers elusive due to a lack of experimental models. This study aimed to decipher novel targets in PA by systematically integrating plasma protein quantitative trait loci (pQTL)-based Mendelian randomization (MR) with multi-omics profiling of parotid gland tissues. MATERIALS AND METHODS: We performed two-sample MR using 5450 plasma pQTLs and genome-wide association study summary for benign or broader salivary gland diseases from FinnGen consortium. Bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) comparing PA to normal tissue were used for transcriptomic validation. Immunohistochemistry (IHC) was applied for protein-level validation in human PA, adenoid cystic carcinoma (ACC), and murine inflammatory lesions. RESULTS: MR identified 12 plasma proteins associated with benign salivary gland tumor risk. Transmembrane serine protease 6 (TMPRSS6) was the only protein significantly risk-increasing for both benign and broader salivary gland diseases. Strikingly, mitogen-activated protein kinase kinase 4 (MAP2K4) showed opposite MR effects between benign and all-lesion outcomes. Bulk RNA-seq showed limited concordance with MR findings, while scRNA-seq revealed a unique plastic epithelium and partially validated candidates at cellular resolution. Critically, IHC confirmed MAP2K4 protein overexpression specifically in human PA, but not in ACC or inflammatory lesions, while TMPRSS6 was downregulated in established pathologies despite its genetic risk association. CONCLUSION: By integrating plasma proteome-based causal inference with parotid tissue multi-omics, this study unveils MAP2K4 as a potential PA-specific driver. This integrative framework provides novel, context-specific targets for further functional investigation in salivary gland tumorigenesis.

Gene expression profiling↗

Using Callus as an Ex Vivo System for Chromatin Analysis.

Next-generation sequencing has revolutionized epigenetics research, enabling a comprehensive analysis of DNA methylation and histone modification profiles to explore complex biological systems at unprecedented depth. Deciphering the intricate epigenetic mechanisms that regulate gene activity presents significant challenges, including the issue of analyzing heterogeneous cell populations in bulk. Bulk analysis introduces bias and can obscure crucial information by averaging readouts from distinct cells. Various approaches have been developed to address this issue, such as cell-type-specific enrichment or single-cell sequencing techniques. However, the need for transgenic lines with fluorescent markers, along with technical challenges such as efficient protoplast isolation and low yield, limits their widespread adoption and use in multi-omic studies. This review discusses the pros and cons of these approaches, providing a valuable basis for selecting the most suitable strategy to minimize heterogeneity. We will also highlight the use of cotyledon-derived callus as an ex vivo system as a simple, accessible, and robust platform for enabling high-throughput multi-omic analyses.

Chromatin↗

Application of Affymetrix array and Massively Parallel Signature Sequencing for identification of genes involved in prostate cancer progression.

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases.

Cell Line, Tumor↗

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

SUMMARY: Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors." These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac. We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC. Alevin-fry-atac is highly scalable and, when using 32 threads, is 2.8 times faster than Chromap (the second fastest approach) while using only 33% of the memory required by Chromap. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual color-enhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger. AVAILABILITY AND IMPLEMENTATION: Alevin-fry-atac is written in Rust and C++17, and is freely-available under a BSD 3-clause license. It is integrated into piscem (https://github.com/COMBINE-lab/piscem) and alevin-fry (https://github.com/COMBINE-lab/alevin-fry), and is also supported directly as part of simpleaf (https://github.com/COMBINE-lab/simpleaf).

Single-Cell Analysis↗

Pan-cancer characterization of HMGA1 reveals its oncogenic role in tumor microenvironment and stemness: functional validation in pancreatic cancer migration and invasion.

BACKGROUND: HMGA1 is a chromatin-associated oncogenic factor implicated in tumor progression, epithelial-mesenchymal transition (EMT), stemness, and metastasis. However, its pan-cancer expression and prognostic patterns, epigenetic activation, and relationship with malignant-cell stemness/plasticity and tumor microenvironment (TME) remodeling in pancreatic adenocarcinoma (PAAD) remain incompletely defined. This study aimed to systematically characterize HMGA1 across cancers and clarify its clinical and biological relevance in PAAD. METHODS: Pan-cancer transcriptomic, clinical, genetic, methylation, immune, and stemness data were integrated from multiple public databases. PAAD single-cell RNA sequencing data were analyzed to localize HMGA1 expression, infer malignant-cell pseudotime, calculate a stemness module score, and assess ligand-receptor communication using CellChat. Public HMGA1-knockdown RNA sequencing data were reanalyzed to evaluate transcriptional remodeling. The Cancer Genome Atlas (TCGA)-PAAD expression and methylation data were used to assess TME-remodeling, immune-suppression, stemness/plasticity, cytokine/chemokine, checkpoint, and promoter-methylation features. HMGA1 expression and function were further examined using immunohistochemistry (IHC), quantitative real-time polymerase chain reaction, Western blotting, wound-healing assays, and Transwell migration and invasion assays. RESULTS: HMGA1 was upregulated in most tumor types, and high expression was associated with unfavorable survival in multiple cancers, including PAAD. In PAAD, HMGA1 was enriched in malignant epithelial cells and positively correlated with pseudotime (Spearman's rho =0.594), while the stemness module score increased along pseudotime (rho =0.748). HMGA1-high malignant cells showed markedly stronger CellChat-inferred outgoing communication, predominantly involving extracellular matrix (ECM)-receptor, adhesion-related, and selected immunomodulatory ligand-receptor axes. HMGA1 knockdown was associated with broad remodeling of EMT, TGF-β, Hedgehog, IL6/JAK/STAT3, KRAS, and cancer stem cell/stemness-related programs rather than uniform suppression of these programs. HMGA1 promoter methylation was inversely correlated with HMGA1 expression (rho =-0.633) and the TME-remodeling score (rho =-0.347). HMGA1 was associated with selected mediators, including PPIA, PLAU, ANXA1, LGALS9, TGFB1, CD276, and CD47, but not with a generalized checkpoint-high phenotype. Functionally, HMGA1 knockdown significantly reduced pancreatic cancer (PC) cell migration and invasion. CONCLUSIONS: These findings support an association-based model in which promoter hypomethylation-associated HMGA1 activation is linked to malignant epithelial stemness/plasticity, ECM/adhesion-dominant TME remodeling, selected immunomodulatory programs, and aggressive PAAD phenotypes. Further mechanistic and clinical validation is required before HMGA1 can be used for therapeutic stratification or immunotherapy-response prediction.

HMGA1↗

A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito.

The female Aedes aegypti mosquito's remarkable ability to hunt humans and transmit pathogens relies on her unique biology. Here, we present the Aedes aegypti Mosquito Cell Atlas, a comprehensive single-nucleus RNA sequencing dataset of more than 367,000 nuclei from 19 dissected tissues of adult female and male Aedes aegypti, providing cellular-level resolution of mosquito biology. We identify novel cell types and expand our understanding of sensory neuron organization of chemoreceptors to all sensory tissues. Our analysis uncovers male-specific cells and sexually dimorphic gene expression in the antenna and brain. In female mosquitoes, we find that glial cells in the brain, rather than neurons, undergo the most extensive transcriptional changes following blood feeding. Our findings provide insights into the cellular basis of mosquito behavior and sexual dimorphism. The Aedes aegypti Mosquito Cell Atlas resource enables systematic investigation of cell type-specific expression across all mosquito tissues.

Aedes aegypti↗

Necroptosis in alveolar epithelium orchestrates lung ischemia-reperfusion injury: a multi-omics study.

BACKGROUND: Lung ischemia-reperfusion injury (LIRI) is a leading cause of early morbidity and mortality following lung transplantation and other cardiopulmonary procedures. It is characterized by acute sterile inflammation driven by regulated cell death (RCD). While various RCD modalities, including apoptosis, necroptosis, pyroptosis, and ferroptosis, have been implicated in lung injury, their relative contributions and distinct activation patterns in LIRI remain poorly defined. METHODS: We employed an integrated multi-omics approach combining transcriptomics and proteomics with histological and functional validations in a murine hilar clamping model of LIRI. Key findings were further corroborated using single-cell RNA sequencing (scRNA-seq) data from human lung transplant recipients. The functional role of necroptosis was validated using pharmacological inhibitors (Nec-1, GSK'872) and Mlkl-deficient (Mlkl-/-) mice. RESULTS: LIRI triggered acute, time-dependent lung injury peaking within 24 h of reperfusion. Although transcriptomic profiling suggested broad activation of multiple RCD pathways, proteomic and biochemical analyses revealed a distinct landscape in our experimental setting: markers of apoptosis, pyroptosis, and ferroptosis were either downregulated or showed no significant positive correlation with injury severity and inflammatory peaks. In contrast, the necroptotic pathway emerged as a highly activated modality. Specifically, necroptosis, marked by phosphorylated RIPK1, RIPK3, and MLKL, was localized primarily in alveolar epithelial cells, correlated strongly with cytokine release and histological lung injury, and preceded the inflammatory response. Pharmacological inhibition or genetic ablation of necroptosis significantly attenuated tissue damage and inflammation. This pronounced necroptotic signature appeared distinct from the broad multi-pathway activation observed in lipopolysaccharide (LPS)-induced lung injury. Translational analysis of human scRNA-seq data further confirmed the selective upregulation of necroptosis signatures in alveolar type 2 (AT2) cells following lung transplantation. CONCLUSION: Our multi-omics analysis identifies necroptosis, particularly in alveolar epithelial cells, as a critical driver of sterile inflammation and tissue injury in the early phase of LIRI. Targeting alveolar epithelial necroptosis may represent a precise and promising therapeutic strategy for lung transplantation and ischemia-reperfusion-associated pulmonary disorders.

Animals↗

Multi-step genomics on single cells and live cultures in sub-nanoliter capsules.

Single-cell sequencing methods uncover natural and induced variation between cells. Many functional genomic methods, however, require multiple steps that cannot yet be scaled to high throughput, including assays on living cells. Here we develop capsules with amphiphilic gel envelopes (CAGEs), which selectively retain cells and large analytes while being freely accessible to media, enzymes and reagents. Capsules enable high-throughput multi-step assays combining live-cell culture with genome-wide readouts. We establish methods for barcoding CAGE DNA libraries, and apply them to measure persistence of gene expression programs in cells by capturing the transcriptomes of tens of thousands of expanding clones in CAGEs. The compatibility of CAGEs with diverse enzymatic reactions will facilitate the expansion of the current repertoire of single-cell, high-throughput measurements and extension to live-cell assays.

Journal Article↗

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities with adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment. We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy, implicating them as malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.

Humans↗

Divergent PTEN-p53 interaction upon DNA damage in a human thyroid organoid model with germline PTEN mutations.

Germline mutations in the tumor suppressor phosphatase and tensin homolog (PTEN) cause PTEN hamartoma tumor syndrome (PHTS). PHTS is characterized by an elevated lifetime risk of differentiated thyroid cancer (DTC), 30 times higher than the general population. However, only 1 in 3 PHTS patients develop DTC, and it remains unknown whether specific PTEN variants are associated with an increased risk of DTC. PTEN antagonizes the phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway, a frequently affected pathway in sporadic DTC. PTEN also acts as a guardian of the genome by interacting with other tumor suppressors. Here, we report how ionizing radiation, an environmental tumorigenic contributor, modifies the DNA damage response based on the type of germline PTEN variants. We hypothesized that certain PTEN variants associated with DTC create a pro-oncogenic molecular signature upon radiation-induced DNA damage. DTC-associated (PTEN M134R ) or DTC-non-associated (PTEN G132D ) germline PTEN mutant alleles were introduced into a human induced pluripotent cell (hiPSC) line derived from a healthy donor utilizing CRISPR-Cas9 gene editing technology. We determined radiation-induced transcriptomic changes in functional thyroid organoids induced from wild-type and both heterozygous PTEN mutant hiPSCs. Both bulk and single-cell RNA sequencing data indicated that radiation upregulated the p53 network more potently in the thyroid organoids with PTEN WT/G132D than those with PTEN WT/M134R , which could be mediated by AKT-dependent MDM2 inactivation and PTEN-p53 physical interaction. Our data suggest that the lack of p53 pathway activation through PTEN-p53 network interactions explains why PTEN M134R is a DTC-susceptible variant.

Humans↗

Proteomic Characterization of 1000 Human and Murine Neutrophils Freshly Isolated From Blood and Sites of Sterile Inflammation.

Neutrophils are indispensable for defense against pathogens. Injured tissue-infiltrated neutrophils can establish a niche of chronic inflammation and promote degeneration. Studies investigated transcriptome of single-infiltrated neutrophils which could misinterpret molecular states of these post mitotic cells. However, neutrophil proteome characterization has been challenging due to low harvests from affected tissues. Here, we present a workflow to obtain proteome of 1000 murine and human tissue-infiltrated neutrophils. We generated spectral libraries containing ∼6200 mouse and ∼5300 human proteins from circulating neutrophils. 4800 mouse and 3400 human proteins were recovered from 1000 cells with 102-108 copies/cell. Neutrophils from stroke-affected mouse brains adapted to the glucose-deprived environment with increased mitochondrial activity and ROS-production, while cells invading inflamed human oral cavities increased phagocytosis and granule release. We provide an extensive protein repository for resting human and mouse neutrophils, identify proteins lost in low input samples, thus enabling the proteomic characterization of limited tissue-infiltrated neutrophils.

Proteomics↗

A new tool for rheumatology: large-scale analysis of gene expression.

Large-scale analysis of gene expression with cDNA arrays is spreading over many biological fields, including rheumatology. In this report, we wish to explain the principle and main advantages of this tool in the context of our discipline. Until 1995, analysis of gene expression was conducted for a few genes at a time but DNA chips now allow one to monitor the expression of thousands of genes in a single experiment and analyze the transcriptome, i.e. the whole of the transcripts in a given cell or tissue. Whatever the platform used (macro- or microarrays, oligo-chips), this technology rests upon the hybridization of i) a set of cDNA clones tethered to a solid support (nylon or glass) as probes, and ii) labelled cDNAs that are reverse-transcribed from bulk mRNAs extracted from a cell or tissue sample as a target. The end result is information on the relative abundance of every mRNA between two or more samples. The transcriptome analysis has two main objectives in rheumatology: i) identifying a gene expression profile that is a hallmark of a pathology and using it for a diagnostic or prognostic purpose, and ii) gathering genes with similar changes of expression, which allows one to specify the identity of novel proteins involved in a well-known intracellular cascade of regulation or even to identify new cascades.

Arthritis, Rheumatoid↗

Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering.

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

Crops, Agricultural↗

2025 Donald Seldin Lecture: Leveraging Diverse Population Genomics and Multiomics Integration for Gene Discovery of Cardiovascular and Kidney Diseases.

This review discusses the implications of frameworks leveraging genetic admixture and multiomics data for advancing gene discovery in cardiovascular and kidney disease research. By broadening gene discovery efforts to additional populations that have a disproportionately high risk of disease and leveraging genetic diversity in admixed populations, studies can identify population-enriched risk variants that traditionally have been missed in genome-wide association studies. The use of multiomics approaches, including the transcriptome, proteome, and metabolome, advances a mechanistic understanding of disease beyond associations. As single-cell omics technologies continue to improve, their integration into gene discovery may help uncover cell-type-specific regulatory pathways and more precise biological contexts. The full potential of these approaches depends on sustained investment in diverse, well-characterized omics data sets, methodological innovation in multiancestry statistical approaches, and interdisciplinary collaboration bridging genomics, epidemiology, and clinical medicine. These efforts will need to be translated into clinically actionable insights, including ancestry-informed risk stratification and targeted therapeutics, to improve outcomes for cardiovascular and kidney diseases.

Humans↗

Complement expression profiles in human glomerular mesangial cells, endothelial cells, podocytes and proximal tubular epithelial cells.

BACKGROUND: Local expression of complement components in the kidney has been reported sporadically in both diseased and normal kidneys. This study aimed to comprehensively characterize the expression of complement components in human glomerular mesangial cells (GMCs), glomerular endothelial cells (GECs), podocytes, and proximal tubular epithelial cells (PTECs) in non-diseased renal tissue. METHODS: Complement expression in cultured human renal intrinsic cells was initially evaluated using reverse transcription polymerase chain reaction and immunofluorescence staining. These findings were further examined using publicly available single-cell RNA-sequencing datasets and 10×Genomics single-cell RNA sequencing of non-diseased human kidney tissue. The analyses focused on complement components involved in the initiation of the classical, lectin, and alternative pathways, as well as components shared among these activation pathways, terminal pathway components, complement regulators, and complement receptors. RESULTS: Complement components unique to the initial phase for classical pathway (C1S, C1R, C2, C4), lectin pathway (MBL2, FCN1, MASP1), alternative pathway (CFB, CFD), and the C3 component shared by the three activation pathways were detected in these cells. The components shared by the terminal pathways including C5, C6, C7, C8 and C9 exhibited lower expression, while complement regulators (CFH, CFI, CD55/DAF, CD46/MCP, CD59, C4BPB, PROS1/Protein S) or receptors (CD93/C1QR1, CR1), particularly membrane-bound proteins, such as DAF, MCP and CD59, which inhibit complement activation and the formation of the membrane attack complex, showed relatively high expression. CONCLUSION: These results showed that all four types of intrinsic renal cells expressed multiple complement components associated with the classical, lectin, and alternative pathways. In non-diseased kidney tissue, complement regulatory molecules involved in the control of complement activation showed relatively higher expression, whereas components of the terminal complement pathway were expressed at relatively lower levels, suggesting that renal intrinsic cells maintain a locally poised but tightly regulated complement system.

Humans↗