BMC Methods

SignificanceStandard methods to characterize peripheral blood mononuclear cells (PBMCs) are often destructive, lack metabolic information, or do not provide single-cell resolution. Label-free tools that non-destructively measure single-cell metabolism within PBMCs can provide new layers of information to characterize disease state and cell therapy potential. AimDetermine whether non-destructive fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolic co-factors NAD(P)H and FAD, or optical metabolic imaging (OMI), can identify immune cell subsets and activation state within heterogeneous PBMC cultures. ApproachOMI measured single-cell metabolism of PBMCs from 3 different human donors in the quiescent or activated (phorbol 12-myristate 13-acetate and ionomycin) state. Fluorescent antibodies were used as ground truth labels for single-cell classifiers of immune cell subtypes. ResultsOMI identified quiescent vs. activated PBMCs with 93% accuracy at only 2 hours post-stimulation, identified monocytes within quiescent and activated PBMCs with 96% and 88% accuracy, respectively, and identified NK cells within quiescent and activated PBMCs with 74% accuracy. ConclusionOMI identifies activation state and immune cell subpopulations within PBMCs, enabling single-cell and label-free measurements of metabolic heterogeneity within complex PBMC samples. Therefore, OMI could enhance PBMC immunophenotyping for diagnostic and therapeutic applications. Statement of DiscoveryWe demonstrate that autofluorescence lifetime imaging can resolve functional and phenotypic metabolic subpopulations within a mixed culture of immune cells from human blood. This provides a new technique to characterize metabolic activity within immune cells from the peripheral blood of patients, which could improve disease diagnostics and the production of cell therapies.

Light sheet fluorescence microscopy (LSFM) is increasingly appreciated as the gold standard for gentle, volumetric imaging with fast acquisition speeds and/or long imaging durations. However, the often-constrained sample space of these microscopes has precluded a specific class of biological specimens from being studied with these tools: those requiring an air-liquid interface (ALI). Here, we present a device for robust imaging at ALI on an upright light sheet microscope with dipping objectives. We demonstrate the system using three relevant use-cases: ex vivo embryonic mouse salivary glands, human epidermal equivalent cultures, and in vivo adult Drosophila melanogaster brains. While the device presented is engineered for one specific light sheet microscope design, it provides a blueprint for easy adaptation to other systems. In doing so, it can potentially spur the use of LSFM for model systems that have so far been unable to take advantage of this powerful technology.

SignificanceDiffuse in vivo flow cytometry (DiFC) is an emerging technique for enumerating rare, fluorescentlylabeled circulating tumor cells (CTCs) in small animals without drawing blood samples. DiFC uses detection of transient fluorescent peaks in time-series data. Previously, we used a simple amplitude threshold-based method for identifying peak candidates, but it ignores potentially useful information in peak shape that could reduce false-positive detections from instrument noise and increase detection efficiency of lower-amplitude peaks. AimTo develop a machine learning (ML)-integrated signal processing approach for improved CTC enumeration using DiFC by distinguishing CTC peaks from artifacts. ApproachWe developed an ML-integrated approach that incorporates a convolutional neural network (CNN) classifier. The CNN was trained to distinguish CTC peaks from artifacts by analyzing peak amplitude and temporal shape characteristics. Performance was validated on in-silico, control, and CTC-bearing mouse datasets. ResultsThe CNN classifier achieved accuracy, precision, sensitivity, and specificity exceeding 98% on test data. Compared with our previously published threshold-based approach, the ML-integrated method increased the number of correctly identified CTCs and their flow direction while reducing false detections across validation datasets. ConclusionsThe ML-integrated approach significantly improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.

Electron Microscopy (EM) is widely used in many scientific fields, particularly in life sciences, offering high-resolution information on the ultrastructure of biological organisms. Accurate characterization of EM image quality is important for assessing the EM tool performance, in addition to sample preparation protocol, imaging conditions, etc. This paper provides an overview of tools we developed as plugins for the popular image processing package Fiji (ImageJ) (1). These tools include signal-to-noise ratio analysis, contrast evaluation, and resolution analysis, as well as the capability to import images acquired on custom FIB-SEM instruments (2). We have also made these tools available in Python, with both versions available on GitHub.

Currently there is a lack of high-throughput, low material-input methods to screen early-stage product quality of viral and non-viral gene therapy products. Here we propose using multiplex droplet digital PCR (dPCR) to screen and characterize vector sequences. We describe the adaptation of a Poisson-multinomial model to quantitate integrity of any combination of 4 targets in multiplexed ddPCR. We show the success and limitations of model employment and provide some suggested best practices.

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

To properly respond to their environment, cells adjust the activity of key regulatory proteins and rates of gene expression. Methods to detect and quantify these forms of regulatory dynamics in living cells are of central importance for understanding cellular signaling events in both physiological and pathological conditions. Current technologies in this field make use of fluorescent probes to track cell signaling dynamics. Although these technologies have been used for decades, challenges remain. In particular, the segmentation, tracking, and interpretation of single cell dynamic data are time-consuming, prone to subjective errors, and often lacking in standardization across experiments. Here, we present SPIFEE, a data pipeline that uses experiment-dependent parameters to smooth noise and quantify key features of fluorescence data from time-lapse imaging studies. Processing data in this manner enhances and accelerates quantification of live-cell gene and protein expression, simplifies data analysis, and facilitates hypothesis generation. Author SummaryCells adjust protein activity and gene expression levels over time to respond to changes in their environment, a process referred to as cell signaling dynamics. Quantifying cell signaling dynamics in living cells often uses fluorescent probes, such as green fluorescent protein (GFP) and its spectral variants, to track changes in gene expression or protein activity over time. Challenges inherent in analyzing fluorescence data from single cells stem from biological and experimental noise, time-consuming quantification, and subjective errors. To address these challenges, we developed a computational tool called Signal Processing and Integrated Feature Extraction (SPIFEE). The pipeline improves the quality of fluorescence data analysis by reducing noise and extracting signal features in a way that is both intuitive and objective. The pipeline provides more accurate, rapid, and unbiased quantification of time-lapse microscopy data.

Quantifying transcript abundance at single-cell resolution is important for understanding gene regulation in intact multicellular organisms. In Caenorhabditis elegans, RNA fluorescence in situ hybridization has been widely used to visualize transcripts, but conventional smFISH approaches can be limited by low signal-to-noise, poor performance with short transcripts, and workflows that do not readily support absolute transcript counting in identified cells. Here, we present an integrated experimental and computational pipeline for quantitative transcript analysis in whole-mount C. elegans embryos, larvae, and adults. The pipeline combines Hybridization Chain Reaction (HCR), confocal microscopy, RS-FISH spot detection, manual cell annotation in FIJI, and custom MATLAB-based spot assignment to quantify individual transcripts within defined cells. We show that this approach enables sensitive, specific, and multiplexed detection of transcripts, including short insulin-like peptide mRNAs, with single-cell resolution. Known spatial expression patterns were resolved in embryos, larvae, and adults, and probe specificity was validated. Applying this pipeline to ins-6 in ASI and ASJ sensory neurons revealed cell-specific regulatory relationships across multiple mutant backgrounds. This workflow provides an accessible method for absolute transcript counting in anatomically intact C. elegans and should support mechanistic studies of gene regulation, cellular heterogeneity, and transcriptional network function.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Motion is an essential component of any living system. It is rich with information, but it is often challenging to quantitatively extract biologically informative results from the motion apparent in microscopy images. This challenge is exacerbated by the wide variety in biological movement, which often takes the form of difficult-to-segment amorphous structures undergoing complex motion. An image processing technique known as optical flow can capture motion at each pixel in an image, thus bypassing the need for object segmentation or a priori definition of motion types. This makes it a powerful tool for quantitative assessment of biological systems from the protein to organism scale. However, despite its flexibility and strengths for analyzing fluorescence microscopy images, its adoption in the bioimaging community has been limited by the availability of easy-to-use tools and guidance in results interpretation. Here we describe an optical flow tool, OpticalFlow3D, that can be run in Python or MATLAB and is compatible with three-dimensional microscopy images. Using biological examples across length scales, we illustrate how OpticalFlow3D can enable new biological insight.

We report an improved version of the open-source optical autofocus module ("openAF") for light microscopy using a light emitting diode (LED), together with a method to independently quantify the performance of optical autofocus systems using 2D autocorrelation analysis of astigmatic imaging of fluorescent nanobeads. We apply the latter for both the LED-based and the previous super luminescent diode (SLD) based implementations of the openAF optical autofocus approach used in conjunction with a 100x 1.4 NA oil-immersion objective lens. The new approach accounts for power variations in the autofocus light source and we demonstrate that the convenient LED-based system can provide axial stability with a standard deviation <10 nm over at least 45 minutes when switched on from cold, during which the LED power varies as it reaches thermal equilibrium.

DNA labelling fluorescent dyes such as ethidium bromide have long been considered to be highly mutagenic during DNA replication. While recent studies have pushed back on this narrative, the intercalative nature of these dyes continues to raise the possibility that these dyes can induce mutations. The iconPCR instrument by n6tec uses fluorescent dyes to measure amplification in real time and to adjust cycling conditions. However, since this use of qPCR is preparative and not analytical, mutations introduced by fluorescent dyes would be propagated into the sequencing reaction. To address the impact of these dyes on downstream analyses, we have performed routine mutation calling as well as mutational signature analysis on samples amplified using the iconPCR in the presence of either SYBR or EvaGreen. Sequence analysis revealed very minimal impacts of dyes on the reactions, largely within the noise regimen with only subtle changes in mutation rates seen. Mutational signature analysis was unable to identify any key signatures assignable to the dyes in either substitutions or indel domains. The mutational impact of intercalating dyes during fluorescence-guided amplification is therefore minimal and can be disregarded in all but the most sensitive NGS applications.

BackgroundHigh-content assays (HCAs) have problems distinguishing biologically significant effects from the incidental effects of non-repeatable technical factors. Non-repeatable results are attributed to variations in the cell culture environment and the numerous, heterogeneous descriptors evaluated. The aim here was to determine whether preprocessing operations impacted the reproducibility of class assignments of experimental data. MethodsBatch effects that could affect reproducibility, i.e., signal/noise ratio, instrumental conditions, and segmentation, were controlled variables. The remaining batch effects, variations in materials, personnel, and culture environment could not be controlled. Descriptors values were measured directly from images. Exploratory factor analysis was used to solve the identifiable and interpretable feature, factor 4. In each of five trials, one sample was treated with the same chemical mixture (EXP) and another with the solvent vehicle alone (CON). ResultsRepeated CON and EXP samples showed significant differences among factor 4 means in data regularized within each trial. The mean of Trial 3 CON differed significantly from all other CON samples. These differences disappeared upon regularization to comprehensive databases. Among repeated EXPs, the Trial 2 mean differed from three other EXPs, but regularization to comprehensive databases had little effect. However, classification patterns were unchanged after regularization to any comprehensive database derived by the same protocol. After regularization to datasets derived by two different protocols, the classification pattern differed but only reflected elevation of differences that had been marginal to statistical significance. Outlier removal was deleterious. Even with the most sparing definition of outliers, over 3% of a single samples contents were removed from most trials. Elimination based on the overall within-trial distributions caused type I and type II errors. ConclusionsNon-repeatable factor 4 means in repeated trials had negligible influence on classification outcomes, so repeatability may not be a good indicator of assay quality. Irreducible batch effects, combined with small sample sizes and skewed distributions of descriptors values, may account for non-repeatability. As the current results are based on real-world data, they suggest that non-repeatability is an uncorrectable feature of these assays. Classification patterns are not affected by several irreducible technical factors, namely materials, personnel, and non-repeatable environmental variables.

Determining spatial and temporal gene expression is crucial for understanding animal development and physiology. Promoter reporters are powerful tools used to dissect how cis-regulatory elements and trans-acting factors control gene expression. Many fluorescent proteins used in promoter reporters, however, have long half-lives (>24 hr) which limit the study of dynamic expression. Destabilizing sequences like PEST reduce the half-life of reporter proteins to provide a more representative readout of gene expression. mlt-11 is a putative protease inhibitor known to oscillate in expression at the mRNA and protein level, yet a mlt-11p::mNeonGreen::3xFLAG::PEST::tbb-2 3UTR promoter reporter did not detectably oscillate. We systematically dissected this transgene, finding that placement of a 3xFLAG tag adjacent to a PEST sequence severely hampered oscillatory expression of a mNeonGreen promoter reporter. Surprisingly, reporter designs that effectively oscillate with a GFP or mNeonGreen fluorophore fail to oscillate when mStayGold is used, with these reporters remaining detectable over 24 hours following promoter inactivation. In addition, other tested epitopes (Myc, ALFA, OLLAS) did not hamper PEST-dependent destabilization but led to varying levels of reporter expression. This study details key considerations for designing destabilized fluorescent promoter reporters.

Stimulated emission depletion (STED) microscopy is a super-resolution fluorescence imaging technique that achieves high spatial and temporal resolution by exploiting stimulated emission to induce fluorescence depletion (FD) and is expected to have substantial utility for imaging applications using fluorescent proteins. However, the compatibility of fluorescent proteins with STED microscopy systems has been understood primarily through empirical observations, and there is no established methodology for the rational selection of fluorescent proteins for STED microscopy. In this study, we systematically evaluated the compatibility of commonly used fluorescent proteins with STED microscopy systems by measuring FD properties using transient absorption spectroscopy and fluorescence dip spectroscopy, both of which are classified as two-color spectroscopy (TCS). Fluorescent proteins identified as compatible with the STED microscopy system based on the TCS measurements were employed for three-dimensional STED imaging of cellular samples expressing each protein. In all samples, three-dimensional spatial resolution was improved relative to confocal laser microscopy, with particularly marked improvements in z-axis resolution. These findings demonstrate that measurements of FD properties via TCS provide a robust approach for evaluating the compatibility of fluorescent proteins with the STED microscopy system and for selecting suitable fluorescent proteins for STED imaging.

Microscopy-based analysis of red blood cell (RBC) morphology is widely used to study phenotypes in sickle cell disease (SCD). Although AI models have been developed to automate classification, most are trained on pre-cropped single-cell images and thus struggle with full-scope microscopic images containing densely packed cells and diverse morphologies, which require both accurate detection and fine-grained classification. We propose an end-to-end computational framework to identify individual RBCs in full-scope microscopy images and classify them into five morphological categories: discocytes (DO), echinocytes (E), elongated and sickle-shaped cells (ES), granular cells (G), and reticulocytes (R). We first evaluate advanced detection-classification models, including You Only Look Once (YOLO) and Detection Transformers (DETR), and demonstrate that while these models effectively detect cells, their classification performance falls short of specialized classifiers trained on single-cell images, particularly for minority phenotypes. To address this limitation, we introduce a two-step framework in which a YOLO-based detector localizes and crops individual cells from full-scope images, followed by a fine-tuned DenseNet121 ensemble classifier that assigns each cell to one of the five morphological categories. The proposed framework achieves a detection-level F1-score of 0.9661 and a weighted-average classification F1-score of 0.9708, with an overall classification accuracy of 97.06%. Compared with the single-step YOLO26n baseline, the two-step pipeline yields a macro-average F1-score improvement of +0.1675, with particularly substantial gains for minority classes (E: +0.1623; G: +0.2774; R: +0.2603). Overall, this hybrid framework demonstrates a practical strategy for adapting fast, general-purpose detection models to domain-specific biomedical tasks by combining them with specialized classifiers, delivering both efficiency and high accuracy for scientific and clinical image analysis.

SignificanceAbnormalities in potassium ion concentrations across subregions of the hippocampus have been implicated in seizures and other pathologies. Direct measurements of potassium ion concentrations are largely made using invasive electrodes, which do not allow for wide spatial coverage. This fluorescent nanoparticle potassium sensor enables direct visualization of potassium dynamics and represents a minimally invasive alternative to electrode-based methods. AimHere, we present a DNA-based fluorescence nanoprobe capable of sensing relative concentrations of potassium ions within populations of neurons. We present its effectiveness in monitoring neuronal K+ dynamics in response to electrical stimulation ex vivo. ApproachWe used widefield fluorescence microscopy to monitor changes in fluorescence intensity in labeled brain tissue in response to electrical stimulation ex vivo. ResultsWe found that our nanoprobe could be retained within the intracellular compartment and modulate in fluorescence intensity linearly in response to induced electrical current. Our K+ Sensor showed a fractional fluorescence change of approximately 1% per 10 mA of applied stimulation current in brain tissue. Optical spectroscopy confirmed the selectivity of the nanoprobes to potassium ions over other endogenous ions. ConclusionsOur findings indicate that this nanoprobe can be used to detect more complex potassium dynamics implicated in various pathologies of the nervous system, such as migraines, seizures, and trauma.

1Three-dimensional organoid cultures have emerged as powerful models for studying human tissue biology, disease mechanisms, and drug responses. Fluorescence confocal microscopy of organoids generates complex volumetric image data that is increasingly analyzed using deep learning pipelines for cell segmentation, morphometry, and phenotyping. However, training and benchmarking such pipelines requires large annotated datasets, the manual curation of which is prohibitively expensive and time-consuming. Here we present a parametric, physics-based computational framework for generating synthetic 3D fluorescence organoid images with exact ground-truth cell body and nucleus label masks. The framework models cell placement using force-directed sphere packing with optional hollow lumen exclusion for cyst-forming organoids, cell morphology using power-diagram (Laguerre) tessellation with apical-basal elongation and surface flattening for polarized epithelial cells, membrane curvature using low-frequency coordinate displacement, nuclear shape using irregular ellipsoid deformation with smooth radial eccentricity direction blending, and optical effects using depth-dependent point-spread function broadening, a physically motivated staining diffusion gradient with residual interior plateau, z-attenuation, haze, shot noise, and channel crosstalk. The necrotic core model uses a three-phenotype nuclear population, pyknotic, ghost, and karyorrhectic, reflecting the histological diversity of real necrotic zones. Five condition-specific presets are provided, calibrated to published biological measurements and covering PDAC osmotic stress, HMECyst normal and cyst-forming organoids, and a large primary PDAC organoid with a necrotic core. Unlike generative adversarial network approaches, our method requires no training data, produces exact ground truth by construction, and allows systematic and interpretable control over every morphological and optical parameter. The framework is released as open-source Python software with a PyQt5 graphical interface and produces OME-TIFF output compatible with arivis Pro, FIJI, and napari, as well as most other microscopy image analysis software.

The translation of mRNA into protein is tightly regulated by both cellular trans-factors and cis-regulatory elements encoded within transcripts. Although transcript fate can be measured by transcript abundance or translation efficiency, separating the contribution of each individual cis-element within a single transcript is an ongoing challenge. Current massively parallel reporter assay (MPRAs) approaches enable systematic interrogation of cis-regulatory elements that control transcript stability, but translation-focused MPRAs remain technically limited and often inaccessible. Here we present Nascent Peptide Translating Ribosome Affinity Purification (NaP-TRAP), a reporter-based approach that simultaneously measures translation and mRNA abundance. Unlike previous methods, NaP-TRAP captures translation directly through the immunoprecipitation of epitope-tagged nascent peptide chains, providing instantaneous, frame-specific readouts without specialized instrumentation. The method is highly scalable from single reporters to complex libraries, and adaptable across in vivo and in vitro systems. NaP-TRAP is versatile, allowing assessment of cis-regulatory impact of elements distributed throughout the mRNA, from cap-to-tail. This protocol covers experimental design, reporter construction, sample processing, and computational analysis for both low- and high-throughput applications. Bench work can be completed in 4- 5 days, with qPCR-based readouts requiring only basic Excel skills for data processing. Sequencing-based readouts require skills in command-line tools and Python scripting and add an additional 2-3 days. NaP-TRAP thus offers an accessible, robust, and quantitative platform to decode the regulatory logic of mRNA translation and stability in diverse biological contexts. Basic Protocol 1Design, assembly, and synthesis of NaP-TRAP reporter libraries. Support Protocol 1Design, assembly, and synthesis of NaP-TRAP individual reporters and spike-ins. Basic Protocol 2NaP-TRAP delivery by micro-injection in zebrafish embryos. Alternate Protocol 1NaP-TRAP delivery by transfection in cultured mammalian cells. Basic Protocol 3NaP-TRAP pulldown and RNA extraction. Basic Protocol 4Preparation of NaP-TRAP cDNA Sequencing Libraries. Alternate Protocol 2NaP-TRAP-qPCR module for low-cost validation. Basic Protocol 5Computational analysis of NaP-TRAP MPRA data.

The auxin-inducible degron (AID) technology is a convenient and powerful tool for protein functional characterization in a broad array of eukaryotic species. We recently demonstrated that the original AID and improved AID2 systems are very effective at rapid protein depletion in Candida albicans and described a limited set of reagents for their use in certain auxotrophic lab strains. With an eye towards broader applicability with improved flexibility, we report here a new series of template vectors suitable for employing AID2 technology in prototrophic C. albicans strains, including clinical isolates. We adapted a common recyclable antibiotic marker system for the required genome editing steps and developed a strategy for simultaneous CRISPR/Cas9-mediated tagging of both target alleles. We also developed a composite all-in-one tagging cassette that combines the degron tag and the OsTIR1F74A gene for single step strain engineering. We added a fluorescent protein tag option and designed and validated an approach for N-terminal tagging that retains natural promoter control. We also compared effectiveness of the two commonly used synthetic auxins, 5-Ph-IAA and 5-Ad-IAA and the two common OsTIR1 variants, F74A and F74G, and provide guidelines for using the new AID2 system. Finally, using the novel all-in-one cassette, we demonstrate that the AID2 system also works in Candida auris. The new reagents should enhance the convenience and accessibility of the AID2 system for the Candida research community. IMPORTANCEInvasive fungal infections, including those caused by Candida species, are a persistent global health problem, and their treatment is hindered by limited antifungal options and the emergence of drug resistance. There is an urgent need for tools and methods to accelerate discovery of novel therapeutic targets. The expanded and optimized auxin-inducible degron system described herein provides a versatile platform for characterizing protein function and dissecting pathways governing important traits like virulence, stress tolerance, and antifungal resistance. The new reagents make AID technology applicable to any strain. Ultimately, this enhanced toolkit has the potential to help identify and validate new high-value drug targets and deepen our understanding of molecular mechanisms that drive pathogenicity of Candida and other fungal pathogen species.