Biology subjects

Takats, Z.

Publications and source records attributed to Takats, Z..

14 recordsLinked to original sources

bioRxiv2026

Glutamine and GLS1 are key metabolic checkpoints that Limit T cell Infiltration and Function in HNSCC

Head and neck squamous cell carcinoma (HNSCC) is characterized by poor survival and limited benefit from immune checkpoint blockade (ICB), underscoring the need to define targetable mechanisms of immune escape. In a prospective, patient-centered study, we investigated how tumor metabolic reprogramming impairs T cell function and drives ICB resistance. Targeted metabolomics identified two dominant and distinct tumor states: the Warburg phenotype and glutamine deprivation and revealed increased tumor-specific glutamine to glutamate turnover. Integrating spatial metabolic imaging with immunohistochemistry linked these states to immune-cold and immune-excluded tumor architectures. Metabolic states selectively reduced the expression of T cell chemokine receptors highly abundant and prognostic in HNSCC. Accordingly, T cell migration into tumor spheroids was reduced at lower glutamine concentrations. Interfering with glutamine metabolism by pharmacologic inhibition or genetic deletion of glutaminase-1 markedly increased tumor susceptibility to immune cell-mediated cytotoxicity in vitro and ex vivo and sensitized patient-derived tumor fragments (PDTFs) to ICB. This effect extended beyond glutamine restoration and involved disruption of glutathione-dependent ROS defense, impaired tumor proliferation, and increased MHC expression in tumor cells. Consistently, high expression of genes involved in glutamine and glutathione metabolism associated with reduced ICB response in patients. In contrast to HNSCC, other tumor entities including breast cancer show low glutaminase-1 expression. Accordingly, breast cancer PDTFs were not responsive to glutaminase-1 inhibition. Together, these data identify glutaminase-1 as a metabolic checkpoint driving immune escape in HNSCC, and support guided patient stratification for metabolism-targeted immunotherapy, similar to current precision approaches for mutation targeted drugs.

cancer biology↗

bioRxiv2026

Widespread detection of Polyethylene glycol reveals chronic human exposure through pharmaceutical drugs

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

biochemistry↗

bioRxiv2026

DESI-MS-Based Analysis of Drug Distribution in Human Renal Cystic Tissue Using the Chorioallantoic Membrane (CAM) as a 3D In Vivo Model

The chorioallantoic membrane (CAM) model represents a promising three-dimensional in vivo platform for preclinical drug testing in human tissues. In this study, we investigated whether the tissue penetration and distribution of benzbromarone, a known inhibitor of the Ca2+ activated chloride channel TMEM16A and potential therapeutic agent for autosomal dominant polycystic kidney disease (ADPKD), can be successfully visualized in human renal cyst tissue cultured on the CAM. To this end, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) combined with an ultrahigh-resolution time-of-flight mass spectrometer was employed. We achieved spatially resolved molecular mapping of endogenous metabolites and lipids as well as the applied compound. MSI enabled clear differentiation between CAM and cystic tissue based on their distinct lipid profiles. Benzbromarone was reproducibly detected in the cyst specimens and exhibited selective accumulation along the cyst epithelium, which is considered the principal site of action. These observations were complemented by multivariate analyses including Uniform Manifold Approximation and Projection (UMAP), and sparse multinomial logistic zero-sum classification. The data-driven approach confirmed molecular differences between tissue types and allowed accurate classification of drug-treated and untreated regions. This study demonstrates that topically applied benzbromarone penetrates human renal cyst tissue in the CAM model and localizes to pharmacologically relevant tissue regions, notably the location of the Ca2+ activated chloride channel TMEM16A in the epithelial lining. The integration of high-resolution DESI-MSI with advanced statistical analysis provides a robust and label-free method to study drug distribution in human tissue grafts. Our findings contribute to the advancement of translational research in analytical chemistry and pharmacology.

biochemistry↗

bioRxiv2026

X-CODE: a dual RNA barcoding system for multi-platform clonal tracking and spatial phenotyping

Experimental dissection of clonal dynamics in complex tissues requires barcoding systems that are scalable, compatible with different analytical platforms, providing phenotypic and spatial resolution. Here we introduce X-CODE, a dual-expressed RNA barcoding system designed to enable high-complexity clonal tracking across sequencing-based, cytometric, and spatial imaging modalities within a unified experimental framework. X-CODE combines a combinatorial, probe-detectable long RNA barcode with a matched short sequencing barcode, enabling seamless integration of probe-based readouts with sequencing and barcode-guided clonal retrieval. We demonstrate robust X-CODE detection by mass cytometry and imaging-based platforms, including spatial RNA barcode readout using via a repurposed Akoya PhenoCycler-Fusion protocol. In addition, we show compatibility with MALDI mass spectrometry imaging for co-registration of clonal and metabolic information. We further demonstrate the feasibility of X-CODE detection within probe-based spatial transcriptomics using the 10x Genomics Xenium platform. Applied to an in vivo model of androgen deprivation in prostate cancer, X-CODE reveals clonal architecture, selection and clone-specific phenotypic and metabolic plasticity underlying castration resistance. Together, X-CODE provides a flexible and broadly accessible platform for integrated clonal analysis across spatial, phenotypic, and molecular dimensions.

cancer biology↗

bioRxiv2025

Multi-omic analysis of vaginal bacteria:host immune and metabolic interactions in adverse early pregnancy outcomes

Accurate and timely diagnosis of early pregnancy outcomes remains a clinical challenge, particularly in cases classified as pregnancy of unknown location, where ultrasound fails to confirm the location of the pregnancy. Delayed diagnosis can lead to serious complications, especially in ectopic pregnancies. This prospective cohort study aimed to characterise host-microbe interactions during early pregnancy in the vaginal mucosa by exploring the relationship between the bacterial community composition, immune response, and metabolic phenotype. We analyzed vaginal swabs from 91 women with pregnancy of unknown location, including 22 with viable intrauterine pregnancies and 69 with adverse outcomes. Metataxonomic profiling, immune assays, and direct on-swab metabolomics were performed. Adverse outcomes, especially ectopic pregnancies, were associated with reduced Lactobacillus abundance, increased bacterial diversity, mucosal inflammation, and elevated lipid species. Vaginal bleeding, a common symptom in PUL, significantly impacted immune and metabolic profiles and should be considered as a confounder in studies of the lower female reproductive tract. Multi-omic data integration highlighted metabolic phenotypes of immune response in the context of bacterial community composition, underscoring the influence of microbe-host interactions on mucosal health and pregnancy outcomes. IMPORTANCEEarly detection of complications in pregnancy is essential for improving maternal health outcomes, yet current diagnostic tools often fail to identify ectopic or failing pregnancies in a timely manner. This study highlights how changes in the vaginal microbiome, inflammation, and metabolic signals are closely linked to adverse outcomes in early pregnancy. By showing that metabolic profiling can predict microbial and immune states even in the presence of vaginal bleeding, the research provides a foundation for developing rapid, non-invasive diagnostic tools. These insights could pave the way for more personalized and effective early pregnancy care.

microbiology↗

bioRxiv2025

Multimodal imaging reveals a lysosomal drug reservoir that drives heterogeneous distribution of PARP inhibitors

For all drugs, effective target engagement requires sufficient intracellular concentrations of drug to be reached, but whether tumour heterogeneity impacts drug distribution and efficacy is poorly studied. PARP inhibitors have transformed treatment of high-grade serous ovarian carcinoma (HGSOC), but resistance remains a clinical hurdle in this highly heterogeneous tumour type. We developed a patient-derived explant multi-modal imaging pipeline, which demonstrated that cell-intrinsic PARP inhibitor accumulation is highly variable, both between patients and within tumours. Spatial transcriptomics revealed enrichment of apoptotic and lysosomal signatures in high-drug regions. Rucaparib, an intrinsically fluorescent PARP inhibitor, accumulates heterogeneously at the single-cell level, with rucaparib-high cells demonstrating increased drug response relative to rucaparib low. Mechanistically, lysosomal sequestration creates a rucaparib reservoir that determines drug levels in the nucleus. Perturbation of lysosomal content altered intracellular levels of weak base PARP inhibitors rucaparib and niraparib, but not olaparib. Together these data suggest that lysosomes act as a reservoir for a subset of PARP inhibitor drugs to improve drug response.

cancer biology↗

bioRxiv2024

Mitochondrial reverse electron transport in myeloid cells perpetuates neuroinflammation

Sustained smouldering, or low grade, activation of myeloid cells is a common hallmark of several chronic neurological diseases, including multiple sclerosis (MS)1. Distinct metabolic and mitochondrial features guide the activation and the diverse functional states of myeloid cells2. However, how these metabolic features act to perpetuate neuroinflammation is currently unknown. Using a multiomics approach, we identified a new molecular signature that perpetuates the activation of myeloid cells through mitochondrial complex II (CII) and I (CI) activity driving reverse electron transport (RET) and the production of reactive oxygen species (ROS). Blocking RET in pro-inflammatory myeloid cells protected the central nervous system (CNS) against neurotoxic damage and improved functional outcomes in animal disease models in vivo. Our data show that RET in myeloid cells is a potential new therapeutic target to foster neuroprotection in smouldering inflammatory CNS disorders3.

neuroscience↗

bioRxiv2023

Laser Desorption Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) demonstrates a direct impact of hypochlorous acid stress on PQS-mediated quorum sensing in Pseudomonas aeruginosa

To establish infections in human hosts, Pseudomonas aeruginosa must overcome innate immune generated oxidative stress, such as the hypochlorous acid (HOCl) produced by neutrophils. We set out to find specific biomarkers of oxidative stress through the development of a protocol for the metabolic profiling of P. aeruginosa cultures grown in the presence of different oxidants using a novel ionisation technique for mass spectrometry, laser desorption rapid evaporative ionisation mass spectrometry (LD-REIMS). We demonstrated the ability of LD-REIMS to classify samples as untreated or treated with a specific oxidant with 100 % accuracy and identified a panel of 54 metabolites with significantly altered concentrations after exposure to one or more of the oxidants. Key metabolic changes were conserved in P. aeruginosa clinical strains isolated from patients with cystic fibrosis lung infections. These data demonstrated that HOCl stress impacted the Pseudomonas Quinolone Signal (PQS) quorum sensing system. Ten 2-alkyl-4-quinolones (AHQs) associated with the PQS system were significantly lower in concentration in HOCl-stressed P. aeruginosa cultures, including 2-heptyl-3-hydroxy-4(1H)-quinolone (PQS), the most active signal molecule of the PQS system. The PQS system regulates the production of virulence factors, including pyocyanin and elastase, and their levels were markedly affected by HOCl stress. No pyocyanin was detectable and elastase concentrations were significantly reduced in cultures grown with sub-lethal concentrations of HOCl, suggesting that this neutrophil-derived oxidant may disrupt the ability of P. aeruginosa to establish infections through interference with production of PQS-associated virulence factors. IMPORTANCEThis work demonstrates that a high-throughput ambient ionisation mass spectrometry method can be used successfully to study a bacterial stress response. Its application to the opportunistic pathogen Pseudomonas aeruginosa led to the identification of specific oxidative stress biomarkers, and demonstrated that hypochlorous acid, an oxidant specifically produced by human neutrophils during infection, affects quorum sensing and markedly reduces production of the virulence factors pyocyanin and elastase in this bacterium. This approach has the potential to be widely applicable to the characterisation of the stress responses of bacteria.

microbiology↗

bioRxiv2023

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target

With colorectal cancer (CRC) being the second most common cause of cancer-related deaths worldwide1, there is an urgent need for better diagnostic tools and new, more targeted therapies. Here we used genetically engineered mouse models (GEMMs), and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that unsupervised metabolic profiling can stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging (MSI) to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-mutant CRC, and propose one of its enzymes, i.e. adenosylhomocysteinase (AHCY), as a new therapeutic target. Collectively, we show that the profound genotype-dependent alterations in both lipid and small molecule metabolism in CRC may be exploited for tissue classification with no need for ion identification, and we applied further data analysis to expose a novel metabolic vulnerability of CRC.

cancer biology↗

bioRxiv2023

Fiberbots: Robotic fibers for high-precision minimally invasive surgery

Technologies that rely on the fundamental principle of thermal expansion have demonstrated high-precision, a growing demand in fields driven by miniaturization. However, scalable production of high aspect ratio devices that harness this capability while facilitating flexibility in design and functionality remains a challenge. We employed the high-throughput fiber thermal drawing technique to readily fabricate multimaterial fiberbots that can precisely and omnidirectionally move by asymmetric thermal expansion. These millimeter-scale fibers (< 2 mm) show excellent repeatability and linearity, negligible hysteresis, and can achieve micron-level resolution over four orders of magnitude motion range. By integrating these robotic fibers with medical devices that can perform cellular-level tissue imaging, diagnosis, and manipulation, we showcase their versatility through benchtop and preclinical animal studies and their overall potential impact on medicine, biomedical engineering, robotics, and beyond. One Sentence SummaryScalable manufacturing and integration of robotic fibers that deliver high-precision motion when heated.

bioengineering↗

bioRxiv2021

Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions.

1BackgroundMass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their expected values. This phenomenon has a two-fold detrimental effect: a) it reduces the ability to interpret the results based on the observed signals, b) it can affect the quality of the observed signal spatial distributions. ResultsWe present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight (TOF) and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions. ConclusionThe presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.

bioinformatics↗

bioRxiv2020

Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods. One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

bioinformatics↗

bioRxiv2018

Co-localization features for classification of tumors using mass spectrometry imaging

Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.

bioinformatics↗

bioRxiv2017

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.\n\nSignificanceNetwork analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity.\n\nAuthor contributionsP.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H...E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H...E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

cancer biology↗