Biology subjects

Schiesaro, G.

Publications and source records attributed to Schiesaro, G..

4 recordsLinked to original sources

bioRxiv2026

Mycol: A user-friendly app for automating analysis of microscopy images

Microscopy image analysis is central to modern biology, yet many available platforms remain inaccessible to non-specialist users because they require advanced technical expertise, code-based workflows, extensive setup, or paid access. This creates a barrier for researchers who need reliable and fast image quantification but lack dedicated computational support. Here, we introduce Mycol, an open-source, machine-learning-assisted image analysis platform designed to be accessible and run on standard laptops with minimal setup. Mycol supports end-to-end workflows in which users annotate microscopy images, perform human-in-the-loop fine-tuning of machine learning models for automated segmentation and classification, deploy machine learning models, quality control predictions and quantitatively compare morphological and class frequency descriptors through a single intuitive interface. By combining machine-learning analysis with efficient quality control by humans, Mycol makes rapid and high-quality image quantification available to biologists without requiring specialist training. We demonstrate the utility of Mycol in diverse workflows using two economically important organisms, the crop pathogen (Fusarium oxysporum) and the blue mussel (Mytilus edulis). Through Mycol, curated training sets were generated and high quality segmentation and classification models were obtained in each case. Deploying these models through Mycol decreased the time requirements and increased traceability of established cell counting workflows and facilitated a quantitative comparison of morphological parameters that reveals new patterns in early M. edulis larval development.

bioinformatics↗

bioRxiv2024

A synthetic platform for multiplex screening of cell-cell communication between soil fungi

Fungi are essential members across ecosystems, yet phytopathogenic fungi pose an increasing risk to crop yields. Despite their ecologic importance, cell-cell communication in fungi is underexplored, partly due to the lack of high-throughput techniques. Here we developed a Yeast Mating Platform (YeMaP) to investigate the interaction between fungal G protein-coupled receptors (GPCRs) and pheromone peptides. We used YeMaP for high- throughput screening of 8,000 pheromone sequences and identified new peptides with improved agonism or antagonism action. We found that these peptides can be applied in a native fungal system such as the plant pathogen Fusarium oxysporum, to control hyphal chemotropism and reduce plant root penetration. Additionally, we utilized YeMaP in a one- pot assay to investigate how abiotic factors influence the communication of multiple pheromone-GPCR combinations and found that the cell-cell communication mediated by the GPCR Ste2 from F. oxysporum signalled robustly across different abiotic factors, while other fungal GPCR-pheromone interactions were more sensitive to changes. Taken together, YeMaP accelerates the identification of fungal GPCR-peptide interactions by enabling one- pot assays, and serves as model system for studying fungal cell-cell communication.

synthetic biology↗

bioRxiv2023

Engineered yeast cells simulating CD19+ cancers to control CAR T cell activation

Chimeric antigen receptor (CAR) T cells have become an established immunotherapy and show promising results for the treatment of hematological cancers. However, modulation of surface levels of the targeted antigen in cancer cells affects the quality and safety of CAR T cell therapy. Here we present the Synthetic Cellular Advanced Signal Adapter (SCASA) system, based on successful engineering of yeast to simulate cancer cells with tunable surface-antigen densities, as a tool for controlled activation of CAR T cell responses and assessment of antigen density effects. Specifically, we demonstrate I) controllable antigen-densities of CD19 on yeast using G protein-coupled receptors (GPCRs), II) a customizable system allowing choice of signal input and modular pathway engineering for precise fine-tuning of the output, III) synthetic cell-cell communication with CAR T cells and the application of CD19-displaying yeast in the characterization of CAR designs, and IV) more efficient and robust activational control of clinically-derived CAR T cells in comparison to the NALM6 cancer cell line. Based on this yeast-based antigen-presenting cell system, we envision efficient assessment of how varying antigen densities in cancer cells affect CAR T cell responses and ultimately support development of safer and better quality of personalized cancer therapies.

synthetic biology↗

bioRxiv2022

Engineered cell differentiation and sexual reproduction in probiotic and mating yeasts

G protein-coupled receptors (GPCRs) enable cells to sense environmental cues and are indispensable for coordinating vital processes including quorum sensing, proliferation, and sexual reproduction. GPCRs comprise the largest class of cell surface receptors in eukaryotes, and for more than three decades the pheromone-induced mating pathway in bakers yeast Saccharomyces cerevisiae has served as a model for studying heterologous GPCRs (hGPCRs). Here we report transcriptome profiles following mating pathway activation in native and hGPCR-signaling yeast, and use a model-guided approach to correlate gene expression to morphological changes. From this we demonstrate mating between haploid cells armed with hGPCRs and endogenous biosynthesis of their cognate ligands. Furthermore, we devise a ligand-free screening strategy for hGPCR compatibility with the yeast mating pathway and enable hGPCR-signaling in the probiotic yeast Saccharomyces boulardii. Combined, our findings enable new means to study mating, hGPCR-signaling, and cell-cell communication in a model eukaryote and yeast probiotics. One-sentence summaryEnabling sensing in probiotic yeast and synthetic yeast mating with human and fungal G protein-coupled receptors.

cell biology↗