Biology subjects

Jensen, E. D.

Publications and source records attributed to Jensen, E. D..

9 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↗

bioRxiv2024

Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands

Machine learning (ML) has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors (hGPCRs) in FDA-approved drugs, exhaustive in-distribution drug-target interaction (DTI) testing across all pairs of hGPCRs and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution (OOD) exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks (CSNN) that leverages network homophily and training-free graph neural networks (GNNs) with Labels as Features (LaF). We show that CSNNs ability to make accurate predictions strongly correlates with network homophily. Thus, LaFs strongly increase a ML models capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 DTIs, 539 compounds, 7 hGPCRs) to discover novel DTIs for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

bioinformatics↗

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↗

bioRxiv2021

Serotonin GPCR-based biosensing modalities in yeast

Serotonin is a key neurotransmitter involved in numerous physiological processes and serves as an important precursor for manufacturing bioactive indoleamines and alkaloids used in the treatment of human pathologies. In humans, serotonin sensing and signaling can occur by 12 G protein-coupled receptors (GPCRs) coupled to G proteins. To systematically assess serotonin GPCR signaling, we characterized reporter gene expression of a 144-sized library encoding all 12 human serotonin GPCRs in combination with 12 different G proteins in yeast exposed to serotonin. For the 5-HT4 receptor, we observe 25- and 64-fold changes in EC50 values and dynamic reporter gene outputs, respectively. Furthermore, we show that optimal biosensing designs enable high-resolution sensing of serotonin produced in yeast, as well as provide a platform for characterization of 19 serotonin GPCR polymorphisms found in human populations. Taken together, our study highlights serotonin biosensing modalities of relevance to both biotechnological and human health applications. HighlightsO_LIHuman serotonin G protein-coupled receptors display promiscuous G coupling in yeast C_LIO_LIG-coupled serotonin receptors display up to 64-fold changes in reporter expression output C_LIO_LIDifferences in G protein evokes 25- and 2-fold difference in EC50 and sensitivity, respectively C_LIO_LISerotonin receptor 5-HT4 and human SNP variants display physiologically relevant EC50 values in yeast C_LIO_LI5-HT4 can be applied for high-resolution biosensing of serotonin produced from yeast C_LI

bioengineering↗

bioRxiv2021

A synthetic RNA-mediated evolution system in yeast

Laboratory evolution is a powerful approach to search for genetic adaptations to new or improved phenotypes, yet either relies on labour-intensive human-guided iterative rounds of mutagenesis and selection, or prolonged adaptation regimes based on naturally evolving cell populations. Here we present CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE) of genomic loci using evolving chimeric donor gRNAs continuously delivered from an error-prone T7 RNA polymerase, and directly introduced as RNA repair donors into genomic targets under either Cas9 or dCas9 guidance. We validate CRAIDE by evolving novel functional variants of an auxotrophic marker gene, and by conferring resistance to a toxic amino acid analogue in bakers yeast Saccharomyces cerevisiae with a mutation rate >3,000-fold higher compared to spontaneous native rate, thus enabling the first demonstrations of in vivo delivery and information transfer from long evolving RNA donor templates into genomic context without the use of in vitro supplied and pre-programmed repair donors.

bioengineering↗

bioRxiv2020

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

synthetic biology↗

bioRxiv2019

Mapping the Host-Pathogen Space to Link Longitudinal and Cross-sectional Biomarker Data: Leptospira Infection in California Sea Lions (Zalophus californianus) as a Case Study

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a bacterial infection (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve both our individual-level and population-level understanding of this host-pathogen system. We create a \"host-pathogen space\" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to visualize and characterize changes in these values through time. We describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health. This mapping framework enables us to determine an individuals location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure, including severe acute disease, chronic subclinical infection, and recovery. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and in certain cases we can impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space and using quantitative biomarker data provides more nuanced approaches for understanding and modeling disease dynamics in a system, yielding benefits for the clinician who needs to triage patients and prevent transmission, and for the disease ecologist or epidemiologist wishing to develop appropriate risk management strategies and assess health impacts on a population scale.\n\nAuthor SummaryA pathogen can cause a range of disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. These facts complicate the work of epidemiologists and disease ecologists seeking to understand the factors governing disease spread, often working with cross-sectional data. Recognizing these facts also highlights the shortcomings of classical approaches to modeling infectious disease, which typically rely on discrete and well-defined disease states. Here we show that by analyzing multiple biomarkers of health and infection simultaneously, treating these values as quantitative rather than binary indicators, and including a modest amount of longitudinal sampling of hosts, we can create a map of the host-pathogen interaction that shows the full spectrum of disease presentations and opens doors for new insights and predictions. By accounting for individual variation and capturing changes through time since infection, this mapping framework enables more robust interpretation of cross-sectional data; e.g., to detect predictive relationships between biomarkers and key outcomes such as survival, or to assess whether observed disease is associated with the pathogen of interest. This approach can help epidemiologists, ecologists and clinicians to better study and manage the many infectious diseases that exhibit complex relationships with their hosts.

ecology↗