TY - JOUR AU - Farkas, Enikő AU - Kovács, Kinga Dóra AU - Székács, Inna AU - Péter, Beatrix AU - Lagzi, István László AU - Kitahata, Hiroyuki AU - Suematsu, Nobuhiko J. AU - Horváth, Róbert TI - Kinetic monitoring of molecular interactions during surfactant-driven self-propelled droplet motion by high spatial resolution waveguide sensing JF - JOURNAL OF COLLOID AND INTERFACE SCIENCE J2 - J COLLOID INTERF SCI VL - 677 PY - 2025 SP - 352 EP - 364 PG - 13 SN - 0021-9797 DO - 10.1016/j.jcis.2024.07.236 UR - https://m2.mtmt.hu/api/publication/35173267 ID - 35173267 AB - Hypothesis: Self-driven actions, like motion, are fundamental characteristics of life. Today, intense research focuses on the kinetics of droplet motion. Quantifying macroscopic motion and exploring the underlying mechanisms are crucial in self-structuring and self-healing materials, advancements in soft robotics, innovations in self-cleaning environmental processes, and progress within the pharmaceutical industry. Usually, the driving forces inducing macroscopic motion act at the molecular scale, making their real-time and high-resolution investigation challenging. Label-free surface sensitive measurements with high lateral resolution could in situ measure both molecular-scale interactions and microscopic motion. Experiments: We employ surface-sensitive label-free sensors to investigate the kinetic changes in a self-assembled monolayer of the trimethyl(octadecyl)azanium chloride surfactant on a substrate surface during the self-propelled motion of nitrobenzene droplets. The adsorption–desorption of the surfactant at various concentrations, its removal due to the moving organic droplet, and rebuilding mechanisms at droplet-visited areas are all investigated with excellent time, spatial, and surface mass density resolution. Findings: We discovered concentration dependent velocity fluctuations, estimated the adsorbed amount of surfactant molecules, and revealed multilayer coverage at high concentrations. The desorption rate of surfactant (18.4 s−1) during the microscopic motion of oil droplets was determined by in situ differentiating between droplet visited and non-visited areas. LA - English DB - MTMT ER - TY - GEN AU - Kanyó, Nicolett AU - Kovács, Kinga Dóra AU - Péter, Beatrix AU - Székács, Inna AU - Rajmon, Imola AU - Horváth, Róbert TI - Cancer cell adhesion and glycocalyx degradation measurement via FluidFM and single-cell optical biosensor PY - 2024 UR - https://m2.mtmt.hu/api/publication/35508615 ID - 35508615 LA - English DB - MTMT ER - TY - GEN AU - Balogh, Anna AU - Kovács, Kinga Dóra AU - Kovács, Boglárka AU - Rajmon, Imola AU - Molnár, Kinga AU - Székács, Inna AU - Péter, Beatrix AU - Horváth, Róbert TI - Funkcionalizált nanorészecskék és élő sejtek kölcsönhatásainak jelölésmentes vizsgálata modern biofizikai módszerekkel CY - Poszter PY - 2024 UR - https://m2.mtmt.hu/api/publication/35257274 ID - 35257274 LA - Hungarian DB - MTMT ER - TY - GEN AU - Kovács, Kinga Dóra AU - Béres, Bálint AU - Kanyó, Nicolett AU - Szabó, Bálint AU - Péter, Beatrix AU - Bősze, Szilvia AU - Székács, Inna AU - Horváth, Róbert TI - Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics CY - Poszter PY - 2024 UR - https://m2.mtmt.hu/api/publication/35257228 ID - 35257228 LA - English DB - MTMT ER - TY - JOUR AU - Béres, Bálint AU - Kovács, Kinga Dóra AU - Kanyó, Nicolett AU - Péter, Beatrix AU - Székács, Inna AU - Horváth, Róbert TI - Label-Free Single-Cell Cancer Classification from the Spatial Distribution of Adhesion Contact Kinetics JF - ACS SENSORS J2 - ACS SENSORS VL - 9 PY - 2024 IS - 11 SP - 5815 EP - 5827 PG - 13 SN - 2379-3694 DO - 10.1021/acssensors.4c01139 UR - https://m2.mtmt.hu/api/publication/35166632 ID - 35166632 AB - There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment. Today, live cell selection procedures incorporate some kind of biomolecular labeling or other invasive measures, which may impact cellular functionality or cause damage to the cells. In this study, we first introduce a highly accurate single-cell segmentation methodology by combining the high spatial resolution of a phase-contrast microscope with the adhesion kinetic recording capability of a resonant waveguide grating (RWG) biosensor. We present a classification workflow that incorporates the semiautomatic separation and classification of single cells from the measurement data captured by an RWG-based biosensor for adhesion kinetics data and a phase-contrast microscope for highly accurate spatial resolution. The methodology was tested with one healthy and six cancer cell types recorded with two functionalized coatings. The data set contains over 5000 single-cell samples for each surface and over 12,000 samples in total. We compare and evaluate the classification using these two types of surfaces (fibronectin and noncoated) with different segmentation strategies and measurement timespans applied to our classifiers. The overall classification performance reached nearly 95% with the best models showing that our proof-of-concept methodology could be adapted for real-life automatic diagnostics use cases. The label-free measurement technique has no impact on cellular functionality, directly measures cellular activity, and can be easily tuned to a specific application by varying the sensor coating. These features make it suitable for applications requiring further processing of selected cells. © 2024 The Authors. Published by American Chemical Society. LA - English DB - MTMT ER - TY - JOUR AU - Kovács, Kinga Dóra AU - Béres, Bálint AU - Kanyó, Nicolett AU - Szabó, Bálint AU - Péter, Beatrix AU - Bősze, Szilvia AU - Székács, Inna AU - Horváth, Róbert TI - Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 PG - 13 SN - 2045-2322 DO - 10.1038/s41598-024-61257-2 UR - https://m2.mtmt.hu/api/publication/34880970 ID - 34880970 AB - Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70–80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine. LA - English DB - MTMT ER - TY - JOUR AU - Kovács, Kinga Dóra AU - Szittner, Zoltán AU - Magyaródi, Beatrix AU - Péter, Beatrix AU - Szabó, Bálint AU - Vörös, Alexa AU - Kanyó, Nicolett AU - Székács, Inna AU - Horváth, Róbert TI - Optical sensor reveals the hidden influence of cell dissociation on adhesion measurements JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 PG - 10 SN - 2045-2322 DO - 10.1038/s41598-024-61485-6 UR - https://m2.mtmt.hu/api/publication/34878499 ID - 34878499 AB - Cell adhesion experiments are important in tissue engineering and for testing new biologically active surfaces, prostheses, and medical devices. Additionally, the initial state of adhesion (referred to as nascent adhesion) plays a key role and is currently being intensively researched. A critical step in handling all adherent cell types is their dissociation from their substrates for further processing. Various cell dissociation methods and reagents are used in most tissue culture laboratories (here, cell dissociation from the culture surface, cell harvesting, and cell detachment are used interchangeably). Typically, the dissociated cells are re-adhered for specific measurements or applications. However, the impact of the choice of dissociation method on cell adhesion in subsequent measurements, especially when comparing the adhesivity of various surfaces, is not well clarified. In this study, we demonstrate that the application of a label-free optical sensor can precisely quantify the effect of cell dissociation methods on cell adhesivity, both at the single-cell and population levels. The optical measurements allow for high-resolution monitoring of cellular adhesion without interfering with the physiological state of the cells. We found that the choice of reagent significantly alters cell adhesion on various surfaces. Our results clearly demonstrate that biological conclusions about cellular adhesion when comparing various surfaces are highly dependent on the employed dissociation method. Neglecting the choice of cellular dissociation can lead to misleading conclusions when evaluating cell adhesion data from various sources and comparing the adhesivity of two different surfaces (i.e., determining which surface is more or less adhesive). LA - English DB - MTMT ER - TY - JOUR AU - Péter, Beatrix AU - Székács, Inna AU - Horváth, Róbert TI - Label-free biomolecular and cellular methods in small molecule epigallocatechin-gallate research JF - HELIYON J2 - HELIYON VL - 10 PY - 2024 IS - 3 PG - 17 SN - 2405-8440 DO - 10.1016/j.heliyon.2024.e25603 UR - https://m2.mtmt.hu/api/publication/34572948 ID - 34572948 LA - English DB - MTMT ER - TY - GEN AU - Péter, Beatrix TI - Measurements with gold nanoparticles and cells by optical biosensors PY - 2023 UR - https://m2.mtmt.hu/api/publication/34486489 ID - 34486489 LA - English DB - MTMT ER - TY - GEN AU - Péter, Beatrix AU - Kanyó, Nicolett AU - Kovács, Kinga Dóra AU - Kovács, Viktor AU - Székács, Inna AU - Pécz, Béla AU - Molnár, Kinga AU - Nakanishi, Hideyuki AU - Lagzi, Istvan AU - Horváth, Róbert TI - In-situ nanoparticle uptake measurement of live cells with tuned glycocalyx PY - 2023 UR - https://m2.mtmt.hu/api/publication/34486453 ID - 34486453 LA - English DB - MTMT ER -