Roland Hergenröder

A new two-chip concept for continuous measurements on PMMA-microchips

A new concept for continuous measurements on microchips is presented. A PMMA (polymethylmethacrylate) based capillary electrophoresis chip with integrated conductivity detection is combined with a second chip, which undertakes the task of fluid handling and electrical connections. The combination of electrokinetic and hydrodynamic flows allows long-term continuous stable analyses with good reproducibilities of migration time and peak heights of analytes. The two-chip system is characterized in terms of stability and reproducibility of separation and detection of small ions. Relative standard deviations of <1% and 3% respectively for retention times and peak heights during long-term measurements can be achieved. The new system combines simple handling and automated analysis without the need for refilling, cleaning or removal of the separation chip after one or several measurements.

Application of surface plasmon resonance imaging technique for the detection of single spherical biological submicrometer particles

Recent proof-of-principle studies demonstrated the suitability of the surface plasmon resonance imaging (SPRi) technique for the detection of individual submicrometer and nanoparticles in solutions. In the current study, we used the SPRi technique for visualization of the binding of round-shaped viruses (inactivated influenza A virus) and virus-like particles (human immunodeficiency virus (HIV)-based virus-like particles) to the functionalized sensor surface. We show the applicability of the SPRi technique for the detection of individual virus-like particles in buffers without serum as well as in buffers containing different concentrations of serum. Furthermore, we prove the specificity of visualized binding events using two different pseudotypes of HIV virus-like particles. We also demonstrate the applicability of the SPRi technique for the determination of relative particle concentrations in solutions. Moreover, we suggest a technical approach, which allows enhancing the magnitude of binding signals. Our studies indicate that the SPRi technique represents an efficient research tool for quantification and characterization of biological submicrometer objects such as viruses or virus-like particles, for example.

Application of the PAMONO-Sensor for Quantification of Microvesicles and Determination of Nano-Particle Size Distribution

The PAMONO-sensor (plasmon assisted microscopy of nano-objects) demonstrated an ability to detect and quantify individual viruses and virus-like particles. However, another group of biological vesicles—microvesicles (100–1000 nm)—also attracts growing interest as biomarkers of different pathologies and needs development of novel techniques for characterization. This work shows the applicability of a PAMONO-sensor for selective detection of microvesicles in aquatic samples. The sensor permits comparison of relative concentrations of microvesicles between samples. We also study a possibility of repeated use of a sensor chip after elution of the microvesicle capturing layer. Moreover, we improve the detection features of the PAMONO-sensor. The detection process utilizes novel machine learning techniques on the sensor image data to estimate particle size distributions of nano-particles in polydisperse samples. Altogether, our findings expand analytical features and the application field of the PAMONO-sensor. They can also serve for a maturation of diagnostic tools based on the PAMONO-sensor platform.

Optical and surface plasmonic approaches to characterize extracellular vesicles. A review

Extracellular vesicles (EVs) have been recognized as messengers delivering various active molecules between cells. This feature of EVs drew the attention of clinicians as well as researchers from different fields. However, exciting ideas to employ EVs as means of drug delivery or to test them as biomarkers of cellular status require very thoughtful and attentive approaches to the selection of analytical techniques for EV characterization. Optical and surface plasmonic analytical methods offer a researcher an invaluable opportunity to use already sized and/or quantified EVs in further functional cell-based assays and in focused biochemical tests (nucleic acid and protein arrays, etc.). Moreover, a high sensitivity and relative flexibility of surface plasmonic sensors open a possibility to develop instruments performing quantitative, metrical and EV surface/content analysis in a single device. This review aims to consider the applicability of established and modern optical techniques as well as novel surface plasmonic approaches for different aspects of EV analysis.

Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks.

In this work, we improve several steps of our PLASMON ASSISTED MICROSCOPY OF NANO-SIZED OBJECTS (PAMONO) sensor data processing pipeline through application of deep neural networks. The PAMONObiosensor is a mobile nanoparticle sensor utilizing SURFACE PLASMON RESONANCE (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low SIGNAL-TO-NOISE RATIO (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables our data processing pipeline to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop GRAPHICS PROCESSING UNITs (GPUs).

The PAMONO-Sensor Enables Quantification of Individual Microvesicles and Estimation of Nanoparticle Size Distribution

In our recent work, the plasmon assisted microscopy of nano-objects (PAMONO) was successfully employed for the detection and quantification of individual viruses and virus-like particles in aquatic samples (Shpacovitch et al., 2015). Further, we adapted the PAMONO-sensor for the specific detection of individual microvesicles (MVs), which have gained growing interest as potential biomarkers of various physiological and pathological processes. Using MVs derived from human neuroblastoma cell line cells, we demonstrated the ability of the PAMONO-sensor to specifically detect individual MVs. Moreover, we proved the trait of the PAMONO-sensor to perform a swift comparison of relative MV concentrations in two or more samples without a prior sensor calibration. The detection software developed by the authors utilizes novel machine learning techniques for the processing of the sensor image data. Using this software, we demonstrated that nanoparticle size information is evident in the sensor signals and can be extracted from them. These experiments were performed with polystyrene nanoparticles of different sizes. We also suggested a theoretical model explaining the nature of observed signals. Taken together, our findings can serve as a basis for the development of diagnostic tools built on the principles of the PAMONO-sensor.