Biology subjects

Catalano, M.

Publications and source records attributed to Catalano, M..

5 recordsLinked to original sources

bioRxiv2026

Fluorogenic labelling for tracking extracellular vesicle nanocarriers in the brain

BackgroundReliable tracking of extracellular vesicles (EVs), key biological nanocarriers in nanomedicine, remains a major technical challenge due to the limitations of conventional lipophilic dyes, including aggregation, micelle formation, and nonspecific background signals that compromise biodistribution analyses. MethodsHere, we present a fluorogenic labeling strategy based on Aco-600, a water-soluble probe exhibiting a "light-on" activation in hydrophobic environments. Medium/large EVs (m/lEVs) derived from murine BV2 microglial cells were labeled and intranasally administered to adult C57BL/6 mice. EV biodistribution and brain uptake were quantitatively assessed by ex vivo fluorescence imaging on brain cryosections at multiple time points (5-1440 min), focusing on the cortex and hippocampus. ResultsAco-600 labeling enabled high signal-to-noise detection with minimal background and no evidence of dye aggregation artifacts. Quantitative analysis revealed a consistent spatiotemporal distribution profile across brain regions, with peak signal intensity at 60 minutes post-administration, followed by progressive clearance. This approach provided reproducible and sensitive tracking of EV biodistribution following a clinically relevant intranasal delivery route. ConclusionsOur findings establish fluorogenic labeling as a robust and artifact-minimizing strategy for in vivo EV tracking. This method enhances the accuracy of biodistribution studies and supports the development of EV-based nanomedicine platforms, particularly for central nervous system delivery applications.

neuroscience↗

bioRxiv2026

Cross-Attention Over RNA And Protein Sequences Enables Generalizable Interaction Prediction

Computational predictions are essential to characterize the RNA-protein interaction landscape, yet a persistent gap between benchmark performance and practical utility suggests that current models have limited generalization capabilities. To address this issue, we present CORAL (Cross-attention for RNA-protein Association Learning), a deep learning framework for the prediction of RNA-protein interactions that integrates pretrained protein (ESM-2) and RNA (DNABERT2) language models through bidirectional cross-attention with Low-Rank Adaptation fine-tuning. We also introduce a benchmarking framework that rigorously addresses the problem of data redundancy between training and test sets, which greatly inflates model performances reported in the literature. To this end we adopt three partitioning strategies of increasing stringency: conventional random splits, pairwise non-redundant splits, and component-wise non-redundant splits. CORAL maintains an F1 score of 0.65 under the most stringent component-wise evaluation, compared to 0.47 for the next-best method retaining discriminative behavior. Interpretability analyses further reveal that the cross-attention mechanism captures biologically meaningful features of molecular recognition at two complementary structural scales: at atomic resolution, specific attention heads systematically attend to structurally defined contact positions, showing 26% elevated attention at interface residues across 309 experimentally resolved complexes (p < 0.001); at the domain level, protein-side attention localizes to annotated RNA-binding domains across 94 % of 462 proteins examined (median within-protein Cohens d {approx} 0.94). Together, these findings establish that current RPI prediction benchmarks substantially inflate performance estimates and demonstrate that cross-modal attention architectures yield improved generalization alongside mechanistically interpretable representations.

bioinformatics↗

bioRxiv2021

Rapid, multianalyte detection of opioid metabolites in wastewater

By monitoring opioid metabolites, wastewater-based epidemiology (WBE) could be an excellent tool for real-time information on consumption of illicit drugs. A key limitation of WBE is the reliance on costly laboratory-based techniques that require substantial infrastructure and trained personnel, resulting in long turnaround times. Here, we present an aptamer-based graphene field effect transistor (AptG-FET) platform for simultaneous detection of three different opioid metabolites. This platform provides a reliable, rapid, and inexpensive method for quantitative analysis of opioid metabolites in wastewater (WW). The platform delivers a limit of detection (LOD) 2-3 orders of magnitude lower than previous reports, but in line with the concentrations range (pg/ml to ng/ml) of these opioid metabolites present in real samples. To enable multianalyte detection we developed a facile, reproducible, and high yield fabrication process producing twenty G-FETs with integrated side gate platinum (Pt) electrodes on a single chip. Our devices achieved the simultaneous and selective multianalyte detection of three different metabolites: Noroxycodone (NX), 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), and Norfentanyl (NF) in wastewater.

biophysics↗

bioRxiv2020

Engineering murine GITRL for antibody-mediated delivery to tumor-associated blood vessels

Preclinical evidence has suggested that the Glucocorticoid-Induced TNFR-related protein (GITR) may be valuable a target for the development of anticancer therapeutics, but clinical studies with GITR ligand (GITRL) have been disappointing. Here, we report the development of a fusion protein featuring GITR ligand (GITRL) fused to the F8 antibody which targets the alternatively-spliced EDA domain of fibronectin, a tumor-associated antigen often found around the tumor neovasculature. Five different formats for F8-GITRL fusion proteins were cloned and characterized, but quantitative biodistribution studies failed to evidence a preferential accumulation at the tumor site. The in vivo tumor targeting properties of F8-GITRL could be substantially improved by enzymatic deglycosylation or site-directed mutagenesis of the N-glycosylation consensus sequence. However, therapy studies in a murine model of cancer with the glycoengineered F8-GITRL N74S and N157T variant failed to elicit a durable anti-tumor response, both in monotherapy and in combination with PD-1 blockade. HIGHLIGHTSO_LIDifferent formats of fusion proteins featuring Glucocorticoid-induced TNFR-related protein ligand (GITRL) fused to a tumor-targeting antibody were produced. C_LIO_LIThe tumor uptake of the fusion proteins could be increased by enzymatic deglycosylation of the fusion protein or by site-directed mutagenesis of the N-glycosylation consensus sequences. C_LIO_LIThe fusion protein developed in this study failed to show any anti-tumor activity either alone or in combination with PD-1 inhibition. C_LI

cancer biology↗

bioRxiv2019

Rapid, selective and single cell electrical detection of antibiotic resistant bacteria

The rapid increase in antibiotic resistant pathogenic bacteria has become a global threat, which besides the development of new drugs, requires rapid, cheap, scalable, and accurate diagnostics. Label free biosensors relying on electrochemical, mechanical, and mass based detection of whole bacterial cells have attempted to meet these requirements. However, the trade-off between selectivity and sensitivity of such sensors remains a key challenge. In particular, point-of-care diagnostics that are able to reduce and/or prevent unneeded antibiotic prescriptions require highly specific probes with sensitive and accurate transducers that can be miniaturized and multiplexed, and that are easy to operate and cheap. Towards achieving this goal, we present a number of advances in the use of graphene field effect transistors (G-FET) including the first use of peptide probes to electrically detect antibiotic resistant bacteria in a highly specific manner. In addition, we dramatically reduce the needed concentration for detection by employing dielectrophoresis for the first time in a G-FET, allowing us to monitor changes in the Dirac point due to individual bacterial cells. Specifically, we realized rapid binding of bacterial cells to a G-FET by electrical field guiding to the device to realize an overall 3 order of magnitude decrease in cell-concentration enabling a single-cell detection limit, and 9-fold reduction in needed time to 5 minutes. Utilizing our new biosensor and procedures, we demonstrate the first selective, electrical detection of the pathogenic bacterial species Staphylococcus aureus and antibiotic resistant Acinetobacter baumannii on a single platform.

microbiology↗