Fully automated detection, segmentation, and analysis of in vivo RPE single cells

Objective To develop a fully automated method of retinal pigmented epithelium (RPE) cells detection, segmentation and analysis based on in vivo cellular resolution images obtained with the transscleral optical phase imaging method (TOPI). Methods Fourteen TOPI-RPE images from 11 healthy individuals were analysed. The developed image processing method encompassed image filtering and normalisation, detection and removal of blood vessels, cell detection and cell membrane segmentation. The produced measures were cellular density of RPE layer, cell area, number of neighbouring cells, eccentricity, circularity and solidity. In addition, we proposed coefficient of variation (CV) of RPE cellular membrane (CMDCV) and the solidity of the RPE cell membrane-shape as new metrics for the assessment of RPE single cells. Results The observed median cellular density of the RPE layer was 3743 cells/mu m(2)(interquartile rate (IQR) 1687), with a median observed RPE cell area of 193 mu m(2)(IQR 141). The mean number of neighbouring cells was 5.22 (standard deviation (SD) 0.05) per RPE cell. The mean RPE cell eccentricity was 0.67 (SD 0.02), median circularity 0.83 (IQR 0.01), and median solidity 0.92 (IQR 0.00). The median CMD(CV)was 0.19 (IQR 0.02). The method is characterised by a median image processing and analysis time of 48 sec (IQR 12) per image. Conclusions The present study provides the first fully automated quantitative assessment of human RPE single cells in vivo. The method provides a baseline for future research in the field of clinical ophthalmology, enabling characterisation and diagnostics of retinal diseases at the single-cell level.

Plasmonic nanohole array biosensor for label-free and real-time analysis of live cell secretion

Hatice Altug, Alexander Belushkin, Xiaokang Li, Cenk Ibrahim Özdemir, Maria Soler Aznar, Filiz Yesilköy

Cell secretion dynamics plays a central role in physiological and disease processes. Due to its various temporal profiles, it is essential to implement a precise detection scheme for continuous monitoring of secretion in real time. The current fluorescent and colorimetric approaches hinder such applications due to their multiple time-consuming steps, molecular labeling, and especially the 'snapshot' endpoint readouts. Here, we develop a nanoplasmonic biosensor for real-time monitoring of live cell cytokine secretion in a label-free configuration. Our nanoplasmonic biosensor is composed of gold nanohole arrays supporting extraordinary optical transmission (EOT), which enables sensitive and high-throughput analysis of biomolecules. The nanobiosensor is integrated with an adjustable microfluidic cell module for the analysis of live cells under well-controlled culture conditions. We achieved an outstanding sensitivity for the detection of vascular endothelial growth factor (VEGF) directly in complex cell media. Significantly, the secretion dynamics from live cancer cells were monitored and quantified for 10 hours while preserving good cell viability. This novel approach of probing cytokine secretion activity is compatible with conventional inverted microscopes found in a common biology laboratory. With its simple optical set-up and label-free detection configuration, we anticipate our nanoplasmonic biosensor to be a powerful tool as a lab-on-chip device to analyze cellular activities for fundamental cell research and biotechnologies.

Royal Society of Chemistry2017

Optofluidic nanoplasmonic biosensor for label-free live cell analysis in real time

Hatice Altug, Alexander Belushkin, Xiaokang Li, Maria Soler Aznar, Filiz Yesilköy

Cell signaling activities play a critical role in physiological and disease processes. The analysis of the tumor microenvironment or the immune system activation is nowadays providing valuable insights towards disease understanding and novel therapies development. Due to the various dynamic profiles, it is essential to implement a continuous monitoring methodology for accurate analysis. The current fluorescent and colorimetric approaches hinder such applications due to their multiple time-consuming steps, molecular labeling, and the ‘snapshot’ endpoint readouts. Photonics technology, and especially nanoplasmonic biosensors offer a unique opportunity to implement lab-on-a-chip systems that provide highly sensitive and label-free analysis of cell signaling events in real time. Here, we will present a microfluidics-integrated nanoplasmonic biosensor for long-term and real-time monitoring of cell secretion activity. The biosensor consists of a gold nanohole array supporting extraordinary optical transmission (EOT), which has been optimized to enable ultra-sensitive and high-throughput biomolecular detection. The nanobiosensor is integrated with a specifically designed microfluidic system that provides well-controlled cell culture conditions for long-term monitoring. We achieved an outstanding sensitivity for the detection of vascular endothelial growth factor (VEGF) directly secreted from microfluidic-cultured cancer cells. We demonstrated real-time monitoring for over 10 hours, preserving good cell viability. The multiplexing capability of our nanobiosensor could enable simultaneous analysis of different cell types and molecules-of-interest. Thus, our innovative approach of probing live cells can be a powerful tool to evaluate cellular activities for diagnostics and novel therapy development.

2018

The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis

Mohamed Farhat, Michelle Susanne Frei, Pascal Damian Odermatt

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and a Francis model test turbine both at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

International Association For Hydraulic Research2014