Association of different prediction methods for determination of the efficiency and selectivity on neuron-based sensors

Bioanalytical and chemical sensors using living taste, olfactory, and neural cells and tissues: a short review

Biosensors utilizing living tissues and cells have recently gained significant attention as functional devices for chemical sensing and biochemical analysis. These devices integrate biological components (i.e. single cells, cell networks, tissues) with micro-electro-mechanical systems (MEMS)-based sensors and transducers. Various types of cells and tissues derived from natural and bioengineered sources have been used as recognition and sensing elements, which are generally characterized by high sensitivity and specificity. This review summarizes the state of the art in tissue- and cell-based biosensing platforms with an emphasis on those using taste, olfactory, and neural cells and tissues. Many of these devices employ unique integration strategies and sensing schemes based on sensitive transducers including microelectrode arrays (MEAs), field effect transistors (FETs), and light-addressable potentiometric sensors (LAPSs). Several groups have coupled these hybrid biosensors with microfluidics which offers added benefits of small sample volumes and enhanced automation. While this technology is currently limited to lab settings due to the limited stability of living biological components, further research to enhance their robustness will enable these devices to be employed in field and clinical settings.

Genetic Programming as a tool for identification of analyte-specificity from complex response patterns using a non-specific whole-cell biosensor

Whole-cell biosensors are mostly non-specific with respect to their detection capabilities for toxicants, and therefore offering an interesting perspective in environmental monitoring. However, to fully employ this feature, a robust classification method needs to be implemented into these sensor systems to allow further identification of detected substances. Substance-specific information can be extracted from signals derived from biosensors harbouring one or multiple biological components. Here, a major task is the identification of substance-specific information among considerable amounts of biosensor data. For this purpose, several approaches make use of statistical methods or machine learning algorithms. Genetic Programming (GP), a heuristic machine learning technique offers several advantages compared to other machine learning approaches and consequently may be a promising tool for biosensor data classification. In the present study, we have evaluated the use of GP for the classification of herbicides and herbicide classes (chemical classes) by analysis of substance-specific patterns derived from a whole-cell multi-species biosensor. We re-analysed data from a previously described array-based biosensor system employing diverse microalgae (Podola and Melkonian, 2005), aiming on the identification of five individual herbicides as well as two herbicide classes. GP analyses were performed using the commercially available GP software 'Discipulus', resulting in classifiers (computer programs) for the binary classification of each individual herbicide or herbicide class. GP-generated classifiers both for individual herbicides and herbicide classes were able to perform a statistically significant identification of herbicides or herbicide classes, respectively. The majority of classifiers were able to perform correct classifications (sensitivity) of about 80-95% of test data sets, whereas the false positive rate (specificity) was lower than 20% for most classifiers. Results suggest that a higher number of data sets may lead to a better classification performance. In the present paper, GP-based classification was combined with a biosensor for the first time. Our results demonstrate GP was able to identify substance-specific information within complex biosensor response patterns and furthermore use this information for successful toxicant classification in unknown samples. This suggests further research to assess perspectives and limitations of this approach in the field of biosensors.

Optical neuronal guidance in three-dimensional matrices

We demonstrate effective guidance of neurites extending from PC12 cells in a three-dimensional collagen matrix using a focused infrared laser. Processes can be redirected in an arbitrarily chosen direction in the imaging plane in approximately 30 min with an 80% success rate. In addition, the application of the laser beam significantly increases the rate of neurite outgrowth. These results extend previous observations on 2D coated glass coverslips. We find that the morphology of growth cones is very different in 3D than in 2D, and that this difference suggests that the filopodia play a key role in optical guidance. This powerful, flexible, non-contact guidance technique has potentially broad applications in tissues and engineered environments.