Model System Research Articles

Genome Assembly of the Nematode Rhabditoides Inermis From a Complex Microbial Community

Abstract Free-living nematodes such as Caenorhabditis elegans and Pristionchus pacificus are powerful model systems for linking specific traits to their underlying genetic basis. To trace the evolutionary history of specific traits or genes, a robust phylogenomic framework is indispensable. In the context of the nematode family Diplogastridae to which P. pacificus belongs, the identity of a sister group has long been debated. In this work, we generated a pseudochromosome level genome assembly of the nematode Rhabditoides inermis, which has previously been proposed as the sister taxon. The genome was assembled from a complex microbial community that is stably associated with R. inermis isolates and that consists of multiple bacteria and a fungus, which we identified as a strain of Vanrija albida. The R. inermis genome spans 173.5Mb that are largely assembled into five pseudochromosomes. This chromosomal configuration likely arose from two recent fusions of different Nigon elements. Phylogenomic analysis did not support a sister group relationship between R. inermis and diplogastrids, but rather supports a sister group relationship between the monophyletic Diplogastridae and a group of genera of Rhabditidae including C. elegans and R. inermis. Thus, our work addresses for the first time the long lasting question about the sister group to diplogastrids at the phylogenomic level and provides with the genomes of R. inermis and the associated fungus V. albida valuable resources for future genomic comparisons.

Effects of mono- and divalent- ions and their strength on foaming properties of infant formula protein model system

Effects of mono- and divalent- ions and their strength on foaming properties of infant formula protein model system

A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020

In epidemiological research, accurate estimation of historical ground-level ozone (O3) concentrations with enhanced spatiotemporal resolution is crucial for effective exposure assessment. The current state-of-the-art for estimating air pollutant concentrations is a two-stage ensemble method that integrates outputs from multiple machine learning algorithms. Despite its effectiveness, opportunities exist to refine this approach for more precise O3 estimation. In this study, we propose an enhanced ensemble method that incorporates four key strategies. First, we employ high-resolution spatiotemporal predictors derived from prior machine learning studies for refined secondary learning. Second, we use sophisticated algorithms, including categorical gradient boosting, deep neural network, random forest, stochastic variable Gaussian process, transformer, and a combination of convolutional neural network and long short-term memory neural network, as sublearners to enhance learning capabilities. Third, we spatiotemporally split the sample set and then train submodels separately on each subset to eliminate the unobserved spatiotemporal heterogeneity. Finally, we apply a complex machine learning algorithm, rather than the generalized additive model, for integrating sublearner predictions, enabling the capture of intricate nonlinear relationships beyond basic spatiotemporal linear weights. To validate these improvements, we estimated daily maximum 8-h moving average O3 concentrations ([O3]MDA8) across Chinese mainland from 2013 to 2020 at a 1 km spatial resolution. The proposed method demonstrated notable accuracy, achieving an out-of-station determination coefficient (R2) of 0.943 and a root-mean-square error (RMSE) of 10.197 μg/m3. This performance marks a nearly 15% improvement over the best existing Chinese O3 exposure model based on a single algorithm and also surpasses previous studies utilizing traditional ensemble methods for other air pollutants. Our enhanced ensemble approach significantly bolsters the reliability and robustness of future environmental epidemiological studies by further mitigating “misclassification” errors.

Drug-Linker Constructs Bearing Unique Dual-Mechanism Tubulin Binding Payloads Tethered through Cleavable and Non-Cleavable Linkers

Antibody-drug conjugates (ADCs) have advanced as a mainstay among the most promising cancer therapeutics, offering enhanced antigen targeting and encompassing wide diversity in their linker and payload components. Small-molecule inhibitors of tubulin polymerization have found success as payloads in FDA approved ADCs and represent further promise in next-generation, pre-clinical and developmental ADCs. Unique dual-mechanism payloads (previously designed and synthesized in our laboratories) function as both potent antiproliferative agents and promising vascular disrupting agents capable of imparting selective and effective damage to tumor-associated microvessels. These payloads have been incorporated into a variety of drug-linker constructs utilizing the clinically relevant cathepsin B cleavable Val-Cit dipeptide linker, employed within several FDA approved ADCs, along with other non-cleavable constructs. Various synthetic strategies were evaluated to prepare these drug-linker constructs. Aniline-based payloads were incorporated utilizing the Val-Cit dipeptide linker similar to FDA approved ADCs such as Adcetris® (brentuximab vedotin). An additional self-immolative group, previously described in the literature for related model systems, was employed to tether the phenolic payloads. A variety of drug-linker constructs (with each bearing a unique dual mechanism payload) were synthesized and evaluated biologically for their enzyme-mediated release of payload and inhibition of tubulin polymerization. Following deactivation of the highly electrophilic maleimido terminus as its corresponding N-acetyl cysteine (NAC) derivative, the most promising construct (NAC-4) demonstrated approximately 90% release of an aniline-functionalized payload (1) upon treatment with cathepsins B or L over 90 minutes. Building on these promising results, future studies will examine the conjugation of drug-linker construct 4 to selected antibodies and engineered proteins and evaluate the biological activity of the resultant antibody-drug conjugates (ADCs).

A Real-Time and Interactive Fluid Modeling System for Mixed Reality.

Within the realm of mixed reality, the capability to dynamically render environmental effects with high realism plays a crucial role in amplifying user engagement and interaction. Fluid dynamics, in particular, stand out as essential elements for crafting immersive virtual settings. This includes the simulation of phenomena like smoke, fire, and clouds, which are instrumental in enriching the virtual experience. This work showcases a cutting-edge system developed to produce dynamic and interactive fluid effects that mirror real captured data in real-time for mixed reality applications. This innovative system seamlessly incorporates fluid reconstruction alongside velocity estimation processes within the Unity engine environment. Our approach leverages a novel physics-based differentiable rendering technique, grounded in the principles of light transport in participating media, to simulate the intricate behaviors of fluid while ensuring high fidelity in visual appearance. To further enhance realism, we have expanded our framework to include the estimation of velocity fields, addressing the critical need for fluid motion simulation. The practical application of these techniques demonstrates the system's capacity to offer a robust platform for fluid modeling in mixed reality environments. Through extensive evaluations, we illustrate the effectiveness of our approach in various scenes, underscoring its potential to transform mixed reality content creation by providing developers with the tools to incorporate highly realistic and interactive fluid seamlessly.

Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin

ABSTRACTWildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments.

Identifying in vitro toxicity testing approaches for (novel) proteins in the context of food and feed risk assessment

Abstract This report documents the outcomes of the EFSA procurement (OC/EFSA/NIF/2022/01) aimed at identifying in vitro toxicity testing approaches for (novel) proteins in the context of food and feed safety assessment. In the present report, we present an integrated testing strategy for the evaluation of toxicity of novel/toxic proteins. A text‐mining approach was used to create a literature database of toxic outcomes associated with toxic proteins retrieved from the UniProt KB database using the search term “Toxin activity”. It was shown that toxic proteins are produced by a relatively limited phylogenetic subset, including, among others, bacteria, insects, serpents, molluscs, and fungi. Toxicological effects of these proteins are generally conserved within phylogenetic groups. Analysis of toxic effects from these proteins was performed using GO term analysis as well as a text‐mining based approach. Relevant tests to address and quantify these toxicity effects were identified and evaluated for their applicability in an in vitro based toxicity testing strategy. A stepwise approach was developed. As a first step, an initial in silico prediction of toxicity is carried out (Step 1). This is followed by a battery of in vitro assays to address the primary mechanisms of toxicity associated with toxic proteins (Step 2). If concern arises in the Step 2 battery of tests, the use of relevant in vitro model systems to explore potential target organ toxicity are required (Step 3). Knowledge gaps have been identified and recommendations are provided in in vitro toxicity testing strategies, in particular for (novel) proteins. Some of these gaps involve the selection and integration of a standardized, relevant in vitro digestion step, reflective of passage through the digestive tract, within the testing strategy, as well as a thorough assessment of the suitability and applicability of in vitro tests and new approach methodologies for regulatory toxicity assessment of (novel) proteins. To accelerate the incorporation of NAMs in the assessment of protein safety, case studies and proof of concept projects are needed to demonstrate the utility and effectiveness of in vitro toxicity testing strategies in the safety assessment of (novel) proteins.

Towards climate-responsible tree positioning: detailed effects of trees on heat exposure in complex urban environments

Increasing heat in urban environments has recently become one of the most dangerous climate hazards due to its adverse impacts on urban populations. Implementing street-level trees could be an effective strategy to mitigate pedestrian heat exposure, particularly due to their ability to block incoming solar radiation. In this study, the PALM model system was applied to simulate the effects of a tree canopy and its location on heat exposure, as quantified by the Universal Thermal Climate Index (UTCI), during a heat wave, using the example of Prague-Dejvice, Czech Republic. Our results show that trees reduce the UTCI under their canopy by 3.5 °C on average, with the greatest UTCI reduction in open spaces during mornings and afternoons. High spatio-temporal variations in the reduction of UTCI by a tree canopy were observed in the study domain, especially in street canyons and courtyards. The effectiveness of trees in mitigating heat exposure was found to be closely related to their individual location with respect to surrounding buildings, specifically: (i) the distance from the nearest building, (ii) the height of the nearest building, and (iii) the azimuthal angle of the vector from the nearest building towards the tree. Model simulations indicate that a particularly small reduction in UTCI (about 2.5 °C less than the mean) can be found under trees located in the shade of taller buildings that are within a few metres and between southwest and southeast of the trees. Our findings illustrate that tree positioning in cities should be undertaken carefully and thoughtfully so that the presence of trees effectively improves thermal comfort and urban quality of life.

Relative fitness of wild-type and phage-resistant pyomelanogenic P. aeruginosa and effects of combinatorial therapy on resistant formation

Bacteriophages, the natural predators of bacteria, are incredibly potent candidates to counteract antimicrobial resistance (AMR). However, the rapid development of phage-resistant mutants challenges the potential of phage therapy. Understanding the mechanisms of bacterial adaptations to phage predation is crucial for phage-based prognostic applications. Phage cocktails and combinatorial therapy, using optimized dosage patterns of antibiotics, can negate the development of phage-resistant mutations and prolong therapeutic efficacy. In this study, we describe the characterization of a novel bacteriophage and the physiology of phage-resistant mutant developed during infection. M12PA is a P. aeruginosa-infecting bacteriophage with Myoviridae morphology. We observed that prolonged exposure of P. aeruginosa to M12PA resulted in the selection of phage-resistant mutants. Among the resistant mutants, pyomelanin-producing mutants, named PA-M, were developed at a frequency of 1 in 16. Compared to the wild-type, we show that PA-M mutant is severely defective in virulence properties, with altered motility, biofilm formation, growth rate, and antibiotic resistance profile. The PA-M mutant exhibited reduced pathogenesis in an allantoic-infected chick embryo model system compared to the wild-type. Finally, we provide evidence that combinatory therapy, combining M12PA with antibiotics or other phages, significantly delayed the emergence of resistant mutants. In conclusion, our study highlights the potential of combinatory phage therapy to delay the development of phage-resistant mutants and enhance the efficacy of phage-based treatments against P. aeruginosa.

Chlorinated paraffins as chlorine donors for the formation of 2- and 3-chloropropanediols in refined vegetable oils

The knowledge of chloropropanediols (MCPD) fatty acid esters formation pathways is an important condition for these processing contaminants mitigation. This study aimed to assess the potential of a group of lipophilic environmental contaminants, polychlorinated alkanes, commonly known as chlorinated paraffins (CPs), to contribute to the formation of MCPD esters. Laboratory-scale model systems representing vegetable oils contaminated with both a technical mixture of short-chained CPs and individual short-chained CPs were designed and subjected to heat treatment (230 °C, 2 h) to simulate the deacidification and deodorisation processes. A substantial increase in MCPD content (up to 3.4 times the control levels) was observed in systems spiked with a technical mixture. MCPD formation seems to correlate very well with the concentration of CPs in these systems. Based on the generated data, we can conclude that the processing of vegetable oils contaminated with CPs might contribute to elevated concentrations of MCPD.

Cspg4 sculpts oligodendrocyte precursor cell morphology

The extracellular matrix (ECM) provides critical biochemical and structural cues that regulate neural development. Chondroitin sulfate proteoglycans (CSPGs), a major ECM component, have been implicated in modulating oligodendrocyte precursor cell (OPC) proliferation, migration, and maturation, but their specific roles in oligodendrocyte lineage cell (OLC) development and myelination in vivo remain poorly understood. Here, we use zebrafish as a model system to investigate the spatiotemporal dynamics of ECM deposition and CSPG localization during central nervous system (CNS) development, with a focus on their relationship to OLCs. We demonstrate that ECM components, including CSPGs, are dynamically expressed in distinct spatiotemporal patterns coinciding with OLC development and myelination. We found that zebrafish lacking cspg4 function produced normal numbers of OLCs, which appeared to undergo proper differentiation. However, OPC morphology in mutant larvae was aberrant. Nevertheless, the number and length of myelin sheaths produced by mature oligodendrocytes were unaffected. These data indicate that Cspg4 regulates OPC morphogenesis in vivo, supporting the role of the ECM in neural development.

The deer mouse (Peromyscus maniculatus bairdii) as a model organism to explore the naturalistic psychobiological mechanisms contributing to compulsive-like rigidity: A narrative overview of advances and opportunities

Deer mice (Peromyscus maniculatus bairdii), a wildtype species native to North America, have been investigated for their spontaneous compulsive-like behaviour. The repetitive and persistence nature of three unique compulsive-like phenotypes in deer mice, i.e., high stereotypy (HS), large nesting behaviour (LNB) and high marble burying (HMB), are characterized by behavioural and cognitive rigidity. In this narrative review, we summarize key advances in the model's application to obsessive-compulsive disorder (OCD), emphasizing how it may be used to study neurobiological and neurocognitive aspects of rigidity. Indeed, deer mice provide the field with a unique naturalistic and spontaneous model system of behavioural and cognitive rigidity, that is useful for investigating the psychobiological mechanisms that underpin a range of compulsive-like phenotypes. Throughout the review, we highlight new opportunities for future research.

Medium Amplitude Field Susceptometry (MAFS) for magnetic nanoparticles

The linear dynamic susceptibility is arguably the most important property to specify the magnetization dynamics of a given system. Therefore, this quantity has been studied in great detail and the corresponding measurements known as susceptometry are well-established. Notwithstanding its relevance, the linear susceptibility is inherently limited to describe the magnetization response to weak external fields only. Here, we suggest the framework of Medium Amplitude Field Susceptometry (MAFS) to study the non-linear response which complements the linear susceptibility and provides additional information on the magnetic properties of the system and is applicable to magnetic fields of medium amplitudes. In particular, we introduce the general third-order nonlinear susceptibility χˆ3 as a central quantity that completely specifies the lowest-order non-linear response to arbitrary time-dependent magnetic fields. We show that response functions in medium amplitude oscillatory magnetic fields and parallel superposition susceptometry are contained in χˆ3 as special cases. Also included in χˆ3 are interesting intermodulation effects when the system is probed by a superposition of oscillating magnetic fields with different frequencies. We work out the explicit form of χˆ3 for several model systems for the dynamics of magnetic nanoparticles (MNPs). We expect this unifying framework to be not only of theoretical interest, but also useful for a deeper characterization of MNP systems, giving additional information on their suitability for various applications.

The vortex splitting process from interaction between a mesoscale vortex and two islands

Mesoscale vortices are major carriers of oceanic material and energy transfer, transporting large amounts of high-energy, temperature-anomalous water bodies during their movement. This significantly impacts both the ocean and the atmosphere. Based on the distribution of the North Brazil Curren rings and the Lesser Antilles in the eastern Caribbean Sea, we use the Regional Ocean Model System ocean circulation model to construct an idealized vortex. Simulations are conducted by varying the distances between the two islands and the scales of the islands to analyze how different parameters affect the vortex path and structural evolution. Using theoretical derivation and numerical simulation results, we construct a dimensionless parameter involving vortex diameter, island diameter, and the distance between the islands to determine the conditions under which vortex splitting occurs. The reliability of this dimensionless parameter is verified using experimental data and satellite data from St. Vincent and Barbados from April 6 to May 6, 2000.

Automated-Screening Oriented Electric Sensing of Vitamin B1 Using a Machine Learning Aided Solid-State Nanopore.

Micronutrient detection and identification at the single-molecule level are paramount for both clinical and home diagnostics. Analytical tools such as high-performance liquid chromatography and liquid chromatography-tandem mass spectrometry have been widely used but include a high instrument cost and prolonged analysis time. Here, as a model system, by merging nanopore signatures with machine learning algorithms, we propose an automated electric sensing strategy to identify vitamin B1 and its phosphorylated derivatives with good accuracy. Further, the relationship between vitamin B1 dynamics and nanopore signatures is examined. To understand the machine-decision-making process, Shapley additive explanations are made. Using a machine learning aided solid-state nanopore, we pave the way for next-generation micronutrient detection.

Sodium Benzoate Induces Fat Accumulation and Reduces Lifespan via the SKN-1/Nrf2 Signaling Pathway: Evidence from the Caenorhabditis elegans Model

Background: Sodium benzoate (SB) is widely used in food products, cosmetics, and medical solutions due to its antimicrobial properties. While it is generally considered safe and has potential neuroprotective benefits, SB has also been linked to adverse effects, including hepatic oxidative stress and inflammation. However, the potential effects of SB on obesity and lifespan remain poorly understood. Objectives: In this study, we investigated the effects of SB on fat accumulation and lifespan using the nematode Caenorhabditis elegans (C. elegans) as a model system. Methods: Wild-type worms were exposed to various SB concentrations (0%, 0.0004%, 0.0008%, 0.004%, and 0.1%) and 0.016% glucose as a positive control for 72 h in liquid or on NGM agar plates. Result: Fat accumulation was assessed through the Oil Red O staining, which revealed that SB induced more fat accumulation compared to vehicle control, even at low concentrations, including the dosage of 0.0004%. Lifespan analysis also demonstrated that SB significantly reduced lifespan in wild-type worms, even at low concentrations. Further investigations found that SKN-1 (an Nrf2 homolog) is necessary for SB-induced fat accumulation and lifespan reduction. Moreover, SB inhibited the nuclear localization of SKN-1 under oxidative stress conditions. Conclusion: These findings suggest that SB may induce fat accumulation and reduce lifespan by inhibiting the oxidative stress-mediated SKN-1 signaling pathway.

Analysis of Battery-like and Pseudocapacitive Ion Intercalation Kinetics via Distribution of Relaxation Times

Abstract Improving the kinetics of electrochemical ion intercalation processes is of interest for realizing high-power electrochemical energy storage. This includes classical battery-like intercalation and pseudocapacitive intercalation processes with a capacitor-like electrochemical signature. Electrochemical methods are needed to probe the kinetics of such complex multistep processes in detail. Here, we present the use of the distribution of relaxation times (DRT) analysis of electrochemical impedance data to identify the kinetic limits of intercalation reactions. We study the lithium intercalation reaction in TiS2 from organic and aqueous electrolytes as a model system. The material can exhibit both battery-like and pseudocapacitive intercalation regimes depending on the potential range, variable diffusion lengths by adjusting its particle size, and a tunable degree of solvent cointercalation by choosing the electrolyte solvent. Using DRT, we can distinguish between the kinetic limitations imposed by solid-state ion diffusion, interfacial ion adsorption and transport, and ion desolvation processes. Thus, DRT analysis can complement existing methods, such as voltammetry or 3D-Bode analysis, to better understand the kinetics of intercalation reactions.

Prediction of strain level phage-host interactions across the Escherichia genus using only genomic information.

Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage-host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage-bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage-host specificity and supports the use of predictive algorithms in phage therapy.

Hyers-Ulam Stability of Fifth Order Linear Differential Equations

In this paper, we study the Hyers-Ulam stability for the fifth-order linear differential equation. In particular, we treat $\varsigma$ as an arrangement of differential equation and in the form%\begin{equation*}$$\varsigma^{v}(x)+\eta_1\varsigma^{iv}(x)+\eta_2\varsigma^{'''}(x)+\eta_3\varsigma^{''}(x)+\eta_4\varsigma^{'}(x)+\eta_5\varsigma(x)=\Omega(x)$$%\end{equation*}where $\varsigma \in c^{5} [k, l]$, $\Omega \in [k, l]$. We demonstrate that$\varsigma^{v}(x)+\eta_1\varsigma^{iv}(x)+\eta_2\varsigma^{'''}(x)+\eta_3\varsigma^{''}(x)+\eta_4\varsigma^{'}(x)+\eta_5\varsigma(x)=\Omega(x)$ has the Hyers-Ulam stability. Two illustrative examples are given to represent the effectiveness of the proposed method. Fifth-order linear differential equations find applications in a wide range of fields, from engineering and control theory to physics, biology, and beyond. These equations are powerful tools for modeling systems with complex dynamics that involve multiple interacting forces or rates of change. Understanding and analyzing their stability and behavior can lead to significant advancements in the design, control, and optimization of these systems.

Tracking Small Extracellular Vesicles Using a Minimally Invasive PicoGreen Labeling Strategy.

Extracellular vesicles (EVs) are cell-secreted lipid bilayer delimited particles that mediate cellular communication. These tiny sacs of cellular information play an important role in cell communication and alter the physiological process under both normal and pathological conditions. As such, tracking EVs can provide valuable information regarding the basic understanding of cell communication, the onset of early malignancy, and biomarker discovery. Most of the current EV-tracking strategies are invasive, altering the natural characteristics of EVs by modifying the lipid bilayer with lipophilic dyes or surface proteins with fluorescent reporters. The invasive labeling strategies could alter the natural processes of EVs and thereby have major limitations for functional studies. Here, we report an alternative minimally invasive EV labeling strategy using PicoGreen (PG), a small molecule that fluoresces at 520 nm when bound to dsDNA. We show that PG binds to dsDNA associated with small EVs (50-200 nm), forming a stable and highly fluorescent PG-DNA complex in EVs (PG-EVs). In both 2D cell culture and 3D organoid models, PG-EV showed efficient tracking properties, including a high signal-to-noise ratio, time- and concentration-dependent uptake, and the ability to traverse a 3D environment. We further validated PG-EV tracking using dual-labeled EVs following two orthogonal labeling strategies: (1) Bioconjugation via surface amine labeling and (2) donor cell engineering via endogenously expressing mCherry-tetraspanin (CD9/CD63/CD81) reporter proteins. Our study has shown the feasibility of using PG-EV as an effective EV tracking strategy that can be applied for studying the functional role of EVs across multiple model systems.