Biology subjects

Susztak, K.

Publications and source records attributed to Susztak, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

Virtual spatial transcriptomics (ST) methods predict gene expression or cell types from histology images, extending molecular readouts beyond the limited regions or samples directly measured by ST platforms. However, the statistical reliability of these predictions remains unclear. Here, we present UTOPIA, a model-agnostic framework for multiscale confidence quantification in virtual ST. UTOPIA assigns statistically calibrated confidence scores to predictions across spatial resolutions and biological granularities, ranging from single genes to metagenes and from specific cell types to broader cell classes. UTOPIA controls false discovery rates for detecting genes, metagenes, or cell types while accounting for local tissue context. We show that prediction confidence depends critically on both spatial resolution and biological granularity, with reliable inference often emerging only at coarser, biologically meaningful scales. Across multiple ST platforms and in both in-sample and out-of-sample settings, UTOPIA enhances interpretability, prevents false biological conclusions, and enables more trustworthy downstream analyses of virtual ST.

genomics↗

bioRxiv2026

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Advances in spatial transcriptomics (ST) have fundamentally transformed our understanding of tissue biology by enabling gene expression profiling within intact spatial contexts and uncovering tissue organization and microenvironmental interactions. However, current high-resolution ST platforms remain constrained by high costs, limited tissue coverage, and technical artifacts, often yielding noisy, sparse, and incomplete data that compromise analytical accuracy, biological interpretation, and clinical utility. To address these challenges, we introduce Pixel2Gene, a deep learning framework that integrates co-registered histology images with ST data to enable histology-guided reconstruction and prediction of spatial gene expression. Pixel2Gene enhances existing expression measurements by denoising low-confidence data and reconstructing coherent expression patterns, while also predicting gene expression in unmeasured tissue regions and new samples lacking direct transcriptomic profiling. We systematically evaluated Pixel2Gene across multiple high-resolution ST platforms, including Visium HD, Xenium, and CosMx, spanning diverse tissue types and disease contexts using downsampling simulations and cross-platform comparisons in clinical samples. Across all settings, Pixel2Gene consistently improved data consistency, mitigated dropout effects, restored biologically meaningful spatial structure, and enabled accurate downstream analyses. By leveraging the scalability and ubiquity of routine histology, Pixel2Gene facilitates comprehensive, cost-effective ST profiling at whole-tissue scale, supporting large cohort studies, translational research, and next-generation biomarker discovery.

genomics↗

bioRxiv2026

HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics

High-resolution histology images are indispensable for pathology and increasingly serve as the structural backbone for spatial omics. Yet whole-slide images (WSIs) frequently contain artifacts, acellular voids, and background regions that, when included in computational workflows, introduce noise, degrade model accuracy, and compromise biological interpretation. Existing tools provide only coarse foreground-background separation, leaving a gap in fine-grained quality control (QC). Here we present HistoSweep, a scalable framework that generates morphology-aware tissue masks at cellular resolution. By integrating density filtering, texture descriptors, and adaptive thresholding, HistoSweep systematically removes non-informative tissue regions while preserving biologically meaningful microstructures. It processes billion-pixel WSIs in minutes on standard CPUs, requiring no GPU acceleration, and is deployable across research and clinical settings. Across 25 WSIs spanning distinct tissues, disease states, and spatial omics platforms, HistoSweep consistently outperformed existing methods. It enhanced visualization and segmentation, improved virtual cell type predictions, and safeguarded spatial transcriptomics integrity by detecting transcript leakage and transcript-histology misalignment. By enabling fine-grained, scalable QC, HistoSweep provides a foundational preprocessing step for reliable and reproducible digital pathology and spatial omics analyses.

bioinformatics↗

bioRxiv2025

Nephrobase Cell+: Multimodal Single-Cell Foundation Model for Decoding Kidney Biology

BackgroundLarge foundation models have revolutionized single-cell analysis, yet no kidney-specific model currently exists, and it remains unclear whether organ-focused models can outperform generalized models. The kidneys complex cellular architecture and dynamic microenvironments further complicate integration of large-scale single-cell and spatial omics data, where current frameworks trained on limited datasets struggle to correct batch effects, capture cross-modality variation, and generalize across species. MethodsWe developed Nephrobase Cell+, the first kidney-focused large foundation model, pretrained on ~100 billion tokens from ~39.5 million single-cell and single-nucleus profiles across 4,319 samples, four mammalian species (human, mouse, rat, pig), and multiple assay modalities (scRNA-seq, snRNA-seq, snATAC-seq, spatial transcriptomics). Nephrobase Cell+ uses a transformer-based encoder-decoder architecture with gene-token cross-attention and a mixture-of-experts module for scalable representation learning. ResultsNephrobase Cell+ sets a new benchmark for kidney single-cell analysis. It produces tightly clustered, biologically coherent embeddings in human and mouse kidneys, far surpassing previous foundation models such as Geneformer, scGPT, and UCE, as well as traditional methods such as PCA and autoencoders. It achieves the highest cluster concordance and batch-mixing scores, effectively removing donor/assay batch effects while preserving cell-type structure. Cross-species evaluation shows superior alignment of homologous cell types and >90% zero-shot annotation accuracy for major kidney lineages in both human and mouse. Even its 1B-parameter and 500M variants consistently outperform all existing models. ConclusionsWith organ-scale multimodal pretraining and a specialized transformer architecture, Nephrobase Cell+ delivers a unified, high-fidelity representation of kidney biology that is robust, cross-species transferable, and unmatched by current single-cell foundation models, offering a powerful resource for kidney genomics and disease research.

genetics↗

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

Single-Cell Spatial Mapping of Human Kidney Development Reveals the Critical Role of the Local Microenvironment in Cell Fate Decisions

Cell-cell interactions play a pivotal role in organ development, yet these communications have previously been studied one interaction at a time in model organisms, leaving a gap in our understanding of the cellular interplay in human development. To address this, we investigated human kidney development using single-cell RNA sequencing and spatial transcriptomics, analyzing over 500,000 cells. By mapping gene expression and differentiation trajectories in histologic space, we define the spatial organization of kidney development. Our analysis revealed newfound plasticity, showing that nephron progenitor cells undergo an early fate decision between renal corpuscle and tubular lineages. However, this choice is later reversed with some mature tubule cells transitioning back to a renal corpuscle fate. Further, through a genome-wide, spatially-aware cell-cell interaction analysis, we identified specific ligands and neighboring cell signals that create biologically meaningful cellular neighborhoods and mediate cell fate choices, offering a blueprint to understand the coordination of human development at scale.

genomics↗

bioRxiv2024

Single-Cell multiomics reveals ENL mutation perturbs kidney developmental trajectory by rewiring gene regulatory landscape

Cell differentiation during organogenesis relies on precise epigenetic and transcriptional control. Disruptions to this regulation can result in developmental abnormalities and malignancies, yet the underlying mechanisms are not well understood. Wilms tumors, a type of embryonal tumor closely linked to disrupted organogenesis, harbor mutations in epigenetic regulators in 30-50% of cases. However, the role of these regulators in kidney development and pathogenesis remains unexplored. By integrating mouse modeling, histological characterizations, and single-cell transcriptomics and chromatin accessibility profiling, we show that a Wilms tumor-associated mutation in the chromatin reader protein ENL disrupts kidney development trajectory by rewiring the gene regulatory landscape. Specifically, the mutant ENL promotes the commitment of nephron progenitors while simultaneously restricting their differentiation by dysregulating key transcription factor regulons, particularly the HOX clusters. It also induces the emergence of abnormal progenitor cells that lose their chromatin identity associated with kidney specification. Furthermore, the mutant ENL might modulate stroma-nephron interactions via paracrine Wnt signaling. These multifaceted effects caused by the mutation result in severe developmental defects in the kidney and early postnatal mortality in mice. Notably, transient inhibition of the histone acetylation binding activity of mutant ENL with a small molecule displaces transcriptional condensates formed by mutant ENL from target genes, abolishes its gene activation function, and restores developmental defects in mice. This work provides new insights into how mutations in epigenetic regulators can alter the gene regulatory landscape to disrupt kidney developmental programs at single-cell resolution in vivo. It also offers a proof-of-concept for the use of epigenetics-targeted agents to rectify developmental defects.

cancer biology↗

bioRxiv2024

Genetic Studies Highlight the Role of TET2 and INO80 in DNA Damage Response and Kidney Disease Pathogenesis

Genome-wide association studies (GWAS) have identified over 800 loci associated with kidney function, yet the specific genes, variants, and pathways involved remain elusive. By integrating kidney function GWAS, human kidney expression and methylation quantitative trait analyses, we identified Ten-Eleven Translocation (TET) DNA demethylase 2: TET2 as a novel kidney disease risk gene. Utilizing single-cell chromatin accessibility and CRISPR-based genome editing, we highlight GWAS variants that influence TET2 expression in kidney proximal tubule cells. Experiments using kidney-tubule-specific Tet2 knockout mice indicated its protective role in cisplatin-induced acute kidney injury, as well as chronic kidney disease and fibrosis, induced by unilateral ureteral obstruction or adenine diet. Single-cell gene profiling of kidneys from Tet2 knockout mice and TET2-knock-down tubule cells revealed the altered expression of DNA damage repair and chromosome segregation genes, notably including INO80, another kidney function GWAS target gene itself. Remarkably both TET2-null and INO80-null cells exhibited an increased accumulation of micronuclei after injury, leading to the activation of cytosolic nucleotide sensor cGAS-STING. Genetic deletion of cGAS or STING in kidney tubules or pharmacological inhibition of STING protected TET2 null mice from disease development. In conclusion, our findings highlight TET2 and INO80 as key genes in the pathogenesis of kidney diseases, indicating the importance of DNA damage repair mechanisms.

genetics↗

bioRxiv2023

Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis

Lipids may influence cellular penetrance by pathogens and the immune response that they evoke. Here we find a broad based lipidomic storm driven predominantly by secretory (s) phospholipase A2 (sPLA2) dependent eicosanoid production occurs in patients with sepsis of viral and bacterial origin and relates to disease severity in COVID-19. Elevations in the cyclooxygenase (COX) products of arachidonic acid (AA), PGD2 and PGI2, and the AA lipoxygenase (LOX) product, 12-HETE, and a reduction in the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients, correlate with the inflammatory response and link to disease severity. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflect disease severity in COVID-19. AA and LA metabolites and LPC-O-16:0 linked variably to the immune response. These studies yield prognostic biomarkers and therapeutic targets for patients with sepsis, including COVID-19. An interactive purpose built interactive network analysis tool was developed, allowing the community to interrogate connections across these multiomic data and generate novel hypotheses.

systems biology↗

bioRxiv2023

HIV viral protein R induces loss of DCT1-type renal tubules

Hyponatremia and salt wasting is a common occurance in patients with HIV/AIDS, however, the understanding of its contributing factors is limited. HIV viral protein R (Vpr) contributes to HIV-associated nephropathy. To investigate the effects of Vpr on the expression level of the Slc12a3 gene, encoding the Na-Cl cotransporter, which is responsible for sodium reabsorption in distal nephron segments, we performed single-nucleus RNA sequencing of kidney cortices from three wild-type (WT) and three Vpr-transgenic (Vpr Tg) mice. The results showed that the percentage of distal convoluted tubule (DCT) cells was significantly lower in Vpr Tg mice compared with WT mice (P < 0.05), and that in Vpr Tg mice, Slc12a3 expression was not different in DCT cell cluster. The Pvalb+ DCT1 subcluster had fewer cells in Vpr Tg mice compared with WT (P < 0.01). Immunohistochemistry demonstrated fewer Slc12a3+ Pvalb+ DCT1 segments in Vpr Tg mice. Differential gene expression analysis comparing Vpr Tg and WT in the DCT cluster showed Ier3, an inhibitor of apoptosis, to be the most downregulated gene. These observations demonstrate that the salt-wasting effect of Vpr in Vpr Tg mice is mediated by loss of Slc12a3+ Pvalb+ DCT1 segments via apoptosis dysregulation.

pathology↗

bioRxiv2022

Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression

Kidneys possess one of the most intricate three-dimensional cellular structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. Here, we have generated high-quality datasets for 81 samples, including single cell (sc), single nuclear (sn), spot level (Visium) and single cell resolution (CosMx) spatial (sp)-RNA expression, and sn open chromatin, capturing cells from healthy, diabetic, and hypertensive diseased human kidneys. By combining the snRNA, snATAC and scRNA sequencing we identify cell types and map these cell types to their locations within the tissue. Unbiased deconvolution of the spatial data identifies 4 distinct spatial microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex, heterogenous cellular and spatial organization of human microenvironments in health and disease. Further, we find that the fibrotic microenvironment spatial gene signature is not only able to molecularly classify human kidneys, but it also offers an improved prognosis prediction compared to traditional histopathological analysis. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

genetics↗

bioRxiv2020

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

The functional interpretation of GWAS remains challenging due to cell-type dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTL) for 659 microdissected human kidney samples and identified cell-type eQTLs by mapping interactions between cell type abundance and genotype. Separately, we generated single cell open chromatin maps (by snATAC-seq) for human kidney samples. We highlight critical enrichment of proximal tubules in kidney function and endothelial cells and distal tubule segments in blood pressure by partitioning heritability using stratified LD-score regression to integrate GWAS with scRNA-seq and snATAC-seq data. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of the most commonly used antihypertensive and renal protective drugs and identifies drug repurposing opportunities for kidney disease. One Sentence SummaryWe define causal cell types, genes and mechanism for kidney dysfunction.

genetics↗

bioRxiv2020

Renal proximal tubule cell state and metabolism are coupled by nuclear receptors

Kidney disease development is poorly understood due to the complex cellular interactions of more than 25 different cell types, each with specialized functions. Here, we used single cell RNA-sequencing to resolve cell type-specific and cell fraction changes in kidney disease. Whole kidney RNA-sequencing results were strongly influenced by cell fraction changes, but minimally informative to detect cell type-specific gene expression changes. Cell type-specific differential expression analysis identified proximal tubule cells (PT cells) as the key vulnerable cell type in diseased kidneys. Through unbiased cell trajectory analyses, we show that PT cell differentiation state is altered in disease state. Lipid metabolism (fatty acid oxidation and oxidative phosphorylation) in PT cells showed the strongest and reproducible association with PT cells state. The coupling of cell state and metabolism is established by nuclear receptors such as PPARA and ESRRA that not only control cellular metabolism but also the expression of PT cell-specific genes in mice and patient samples.

cell biology↗

bioRxiv2020

Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease

A subset of individuals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), which is the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this investigation was to assess differences in blood-derived DNA methylation patterns between individuals with T1DM-ESKD and individuals with long-duration T1DM but no evidence of kidney disease upon repeated testing. Blood-derived DNA from individuals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illuminas Infinium MethylationEPIC BeadChip arrays (n=862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p[≤]x10-8 and fold change {+/-}2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM. Top-ranked genes within which several dmCpGs were located and supported by in silico functional data, and replication where possible, include; AFF3, ARID5B, CUX1, ELMO1, FKBP5, HDAC4, ITGAL, LY9, PIM1, RUNX3, SEPTIN9, and UPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-{beta} signalling and Th17 cell differentiation. Epigenetic alterations provide a dynamic link between an individuals genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped individuals, has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in individuals with T1DM.

molecular biology↗

bioRxiv2020

Defining the lineage of thermogenic perivascular adipose tissue

SummaryBrown adipose tissue can expend large amounts of energy and thus increasing its amount or activity is a promising therapeutic approach to combat metabolic disease. In humans, major deposits of brown fat cells are found intimately associated with large blood vessels, corresponding to perivascular adipose tissue (PVAT). However, the cellular origins of PVAT are poorly understood. Here, we applied single cell transcriptomic analyses, ex vivo adipogenesis assays, and genetic fate mapping in vivo to determine the identity of perivascular adipocyte progenitors. We found that thoracic PVAT initially develops from a fibroblastic lineage, comprising progenitor cells (Pdgfra+;Ly6a+;Pparg−) and preadipocytes (Pdgfra+;Ly6a−;Pparg+). Progenitors and preadipocytes in PVAT shared transcriptional similarity with analogous cell types in white adipose tissue, pointing towards a conserved adipose cell lineage hierarchy. Interestingly, the aortic adventitia of adult animals contained a novel population of adipogenic smooth muscle cells (Myh11+; Pdgfra−; Pparg+) possessing the capacity to generate adipocytes in vitro and in vivo. Taken together, these studies define distinct populations of fibroblastic and smooth muscle progenitor cells for thermogenic PVAT, providing a crucial foundation for developing strategies to augment brown fat activity.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗

bioRxiv2020

Single cell resolution regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets

Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profiled open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We define key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrated that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells followed a more complex differentiation, where Hfn4a was associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease identified critical cell types, developmental stages, genes, and regulatory mechanisms. We provide a global single cell resolution view of chromatin accessibility of kidney development. The dataset is available via interactive public websites.

developmental biology↗

bioRxiv2020

Renal tubule Cpt1a overexpression mitigates kidney fibrosis by restoring mitochondrial homeostasis

Chronic kidney disease (CKD) remains a major epidemiological, clinical and biomedical challenge. During CKD, renal tubular epithelial cells (TECs) suffer a persistent inflammatory and profibrotic response. Fatty acid oxidation (FAO), the main source of energy for TECs, is reduced in kidney fibrosis and contributes to its pathogenesis. To determine if FAO gain-of-function (FAO-GOF) could protect from fibrosis, we generated a conditional transgenic mouse model with overexpression of the fatty acid shuttling enzyme carnitine palmitoyl-transferase 1 A (CPT1A) in TECs. Cpt1a knock-in (CPT1A KI) mice subjected to three different models of renal fibrosis (unilateral ureteral obstruction, folic acid nephropathy-FAN and adenine induced nephrotoxicity) exhibited decreased expression of fibrotic markers, a blunted pro-inflammatory response and reduced epithelial cell damage and macrophage influx. Protection from fibrosis was also observed when Cpt1a overexpression was induced after FAN. FAO-GOF restituted oxidative metabolism and mitochondrial number and enhanced bioenergetics increasing palmitate oxidation and ATP levels, changes also recapitulated in TECs exposed to profibrotic stimuli. Studies in patients evidenced decreased CPT1 levels and increased accumulation of short and middle chain acyl-carnitines, reflecting impaired FAO in human CKD. We propose that strategies based on FAO-GOF may constitute powerful alternatives to combat fibrosis inherent to CKD. Graphical abstractOverexpression of CPT1A impedes tubular epithelial renal cell dedifferentiation and a pro-fibrotic phenotype by restoring mitochondrial metabolic bioenergetics. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biochemistry↗

bioRxiv2020

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis

An important step in single-cell RNA-seq (scRNA-seq) analysis is to cluster cells into different populations or types. Here we describe ItClust, an Iterative Transfer learning algorithm with neural network for scRNA-seq Clustering. ItClust learns cell type knowledge from well-annotated source data, but also leverages information in the target data to make it less dependent on the source data quality. Through extensive evaluations using datasets from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust significantly improves clustering and cell type classification accuracy compared to popular unsupervised clustering and supervised cell type classification algorithms.

genomics↗