Esther Serrano-Pertierra

Extracellular vesicles (EVs) have emerged as promising biomarkers for the diagnosis and monitoring of various diseases; however, their clinical application is limited by the need for sensitive and accessible detection platforms. In this study, a highly sensitive electrochemical lateral flow immunoassay (eLFIA) for the detection and accurate quantification of EVs in human plasma is presented. Using a polymer precipitation-based kit, EVs were isolated and determined by using iodine-doped starch-functionalized gold nanoparticle based immunochromatographic test. This strategy has never been used before for electrochemical coupling and it has been protected by an international patent. With this electrochemical approach, the biosensor achieved a detection limit of 9 EVs/µL, demonstrating significantly higher sensitivity than conventional optical methods and even exceeding the limits reported by more complex bulk widely-used techniques, such as Nanoparticle Tracking Analysis. In addition, validation on real biological samples confirmed the accuracy of the device with recoveries above 98%, underlining its robustness and reproducibility. This work establishes a versatile and efficient platform for EV detection that addresses key limitations of current immunoassays, positioning this eLFIA as a promising tool for clinical diagnostics. Furthermore, the proposed strategy has the potential to be adapted to other biomarkers, broadening its applicability to a variety of pathologies. AbstractSection Graphical Abstract The alternative text for this image may have been generated using AI.

In the past decade, extracellular vesicles (EVs) have gained increasing attention in biomedical research. These membrane-bound particles are naturally secreted by cells under both physiological and pathological conditions, and they exhibit a wide range of sizes and molecular compositions. EVs transport bioactive molecules - such as proteins, nucleic acids, and lipids - making them ideal candidates for biomarker discovery. Consequently, their accurate characterization and quantification are critical for understanding their roles in intercellular communication and evaluating their potential in diagnostics, prognostics, disease monitoring, and therapeutic applications. Microfluidic technologies offer promising solutions for EV analysis, addressing key limitations of conventional methods by enabling precise and sensitive measurements with small sample volumes. While microfluidic devices have been predominantly used for EV separation and isolation, their application in EV quantification remains underexplored. Compared to traditional techniques like nanoparticle tracking analysis or flow cytometry, microfluidic systems can provide faster, more accessible alternatives for EV quantification. This review summarizes recent advances in microfluidic technologies for EV quantification, discussing their advantages, current limitations, and future prospects.

Lateral flow immunoassays (LFIAs) are widely used for point-of-care diagnostic devices due to their simplicity, low cost, and rapid results. In this work, we demonstrate that a heterosandwich design LFIA can be an effective tool for verifying the presence of different proteins on the same particles. As a case study, we address a recent controversy regarding the presence of the protein L1CAM on the extracellular vesicles (EVs). EVs are crucial for cell communication and may serve as valuable disease biomarkers, including for neurodegenerative disorders. EVs from neuronal cells can cross the blood-brain barrier and be selectively isolated from plasma. Although L1CAM has been suggested as a marker for neuron-derived EVs, recent studies report that L1CAM exists as a cleaved soluble protein in plasma, not associated with EVs. We propose a heterosandwich LFIA to detect and quantify L1CAM and a confirmed EV marker, tetraspanin CD63 or CD9, on the same EV. This assay, together with several control experiments on EVs isolated from plasma by size exclusion chromatography (SEC), demonstrates that although most L1CAM in plasma is present as soluble cleaved proteins, 13% of the EVs are strongly associated with this protein. This evidence is confirmed by dynamic light scattering measurements, showing a significant size increase of gold nanoparticles conjugated with L1CAM antibodies when exposed to EVs but not to cleaved soluble L1CAM. Our results validate the selective immune-isolation of L1CAM-EVs, resolving the controversy by confirming that L1CAM is indeed associated with a significant fraction of EVs despite the presence of its soluble form in plasma.

The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditional methods, often relying on fluorescence or colorimetric staining and manual segmentation, are time consuming, labor intensive, and prone to error, raising the need for the development of automated and reliable methods. This paper delves into the evaluation of three pivotal deep learning techniques: semantic segmentation, which allows for pixel-level classification and is solely suited for characterization; object detection, which focuses on counting and locating neurons; and instance segmentation, which amalgamates the features of the other two but employing more intricate structures. The goal of this research is to discern what technique or combination of those techniques yields the optimal results for automatic counting and characterization of neurons in images of neuronal cultures. Following rigorous experimentation, we conclude that instance segmentation stands out, providing superior outcomes for both challenges. Graphical Identifying the optimal pathway for characterizing neurons in complex cultures through structured experimentation

Foodborne allergies and illnesses represent a major global health concern. In particular, fish can trigger life-threatening food allergic reactions and poisoning effects, mainly caused by the ingestion of parvalbumin toxin. Additionally, preformed histamine in less-than-fresh fish serves as a toxicological alert. Consequently, the analytical assessment of parvalbumin and histamine levels in fish becomes a critical public health safety measure. The multiplex detection of both analytes has emerged as an important issue. The analytical detection of parvalbumin and histamine requires different assays; while the determination of parvalbumin is commonly carried out by enzyme-linked immunosorbent assay, histamine is analyzed by high-performance liquid chromatography. In this study, we present an approach for multiplexing detection and quantification of trace amounts of parvalbumin and histamine in canned fish. This is achieved through a colorimetric and surface-enhanced Raman-scattering-based competitive lateral flow assay (SERS-LFIA) employing plasmonic nanoparticles. Two distinct SERS nanotags tailored for histamine or β-parvalbumin detection were synthesized. Initially, spherical 50 nm Au@Ag core-shell nanoparticles (Au@Ag NPs) were encoded with either rhodamine B isothiocyanate (RBITC) or malachite green isothiocyanate (MGITC). Subsequently, these nanoparticles were bioconjugated with anti-β-parvalbumin and antihistamine, forming the basis for our detection and quantification methodology. Additionally, our approach demonstrates the use of SERS-LFIA for the sensitive and multiplexed detection of parvalbumin and histamine on a single test line, paving the way for on-site detection employing portable Raman instruments.

Escherichia coli (E. coli) O157:H7 is a pathogenic bacterium that causes serious toxic effects in the human gastrointestinal tract. In this paper, a method for its effective analytical control in a milk sample was developed. To perform rapid (1 h) and accurate analysis, monodisperse Fe₃O₄@Au magnetic nanoparticles were synthesized and used in an electrochemical sandwich-type magnetic immunoassay. Screen-printed carbon electrodes (SPCE) were used as transducers, and electrochemical detection was performed by chronoamperometry using a secondary horseradish peroxidase-labeled antibody and 3,3′,5,5′-tetramethylbenzidine. This magnetic assay was used to determine the E. coli O157:H7 strain in the linear range from 20 to 2 × 10⁶ CFU/mL, with a limit of detection of 20 CFU/mL. The selectivity of the assay was tested using Listeria monocytogenes p60 protein, and the applicability of the assay was assessed by analyzing a commercial milk sample, demonstrating the usefulness of the synthesized nanoparticles in the developed magnetic immunoassay.