Micro-algal biosensors

Sol-gel silica platforms for microalgae-based optical biosensors

An advanced hybrid biosensing platform with improved optical quality is developed based on the acidic encapsulation of microalgi in silica matrices synthesized by TAFR (tetraethoxysilane derived alcohol free route). The three microalgi (Chlorella vulgaris, Pseudokirchneriella subcapitata and Chlamydomonas reinhardtii) were previously immobilized in alginate following the two-step procedure. Tuning the alginate protecting function with the aid of Tris-HCl buffer, the sol-gel synthesis was conducted at pH 4.0 well below the tolerance limit imposed by the encapsulated microalgae. The acidic condensation of Si(IV) generates silica matrices with outstanding optical properties that suit the requirements of biosensors based on optical detection methods.

Microalgae dual-head biosensors for selective detection of herbicides with fiber-optic luminescent O2 transduction

The microalgal species Dictyosphaerium chlorelloides (D. c.) was immobilized into porous silicone films and their photosynthetic activity was monitored with an integrated robust luminescent O2 sensor. The biosensor specificity towards a particular pesticide has been achieved by manufacturing a fiber-optic dual-head device containing both analyte-sensitive and analyte-resistant D. c. strains. The latter are not genetically modified microalgae, but a product of modified Luria-Delbrück fluctuation analysis followed by ratchet selection cycles. In this way the target herbicide decreases the O2 production of the analyte-sensitive immobilized strain without affecting the analyte-resistant population response; any other pollutant will lower the O2 production of both strains. The effect of the sample flow-rate, exposure time to the herbicide, biomass loading, biosensor film thickness, intensity of the actinic light, illumination cycle, and temperature on the biosensor response has been evaluated using waterborne simazine as test bench. The biosensing device is able to provide in situ measurements of the herbicide concentration every 180 min. The biosensor limit of detection for this herbicide was 12 μg L(-1), with a working range of 50-800 μg L(-1). The biosensor specificity to simazine has been assessed by comparing its response to that of isoproturon.

A powerful molecular engineering tool provided efficient Chlamydomonas mutants as bio-sensing elements for herbicides detection

This study was prompted by increasing concerns about ecological damage and human health threats derived by persistent contamination of water and soil with herbicides, and emerging of bio-sensing technology as powerful, fast and efficient tool for the identification of such hazards. This work is aimed at overcoming principal limitations negatively affecting the whole-cell-based biosensors performance due to inadequate stability and sensitivity of the bio-recognition element. The novel bio-sensing elements for the detection of herbicides were generated exploiting the power of molecular engineering in order to improve the performance of photosynthetic complexes. The new phenotypes were produced by an in vitro directed evolution strategy targeted at the photosystem II (PSII) D1 protein of Chlamydomonas reinhardtii, using exposures to radical-generating ionizing radiation as selection pressure. These tools proved successful to identify D1 mutations conferring enhanced stability, tolerance to free-radical-associated stress and competence for herbicide perception. Long-term stability tests of PSII performance revealed the mutants capability to deal with oxidative stress-related conditions. Furthermore, dose-response experiments indicated the strains having increased sensitivity or resistance to triazine and urea type herbicides with I(50) values ranging from 6 × 10(-8) M to 2 × 10(-6) M. Besides stressing the relevance of several amino acids for PSII photochemistry and herbicide sensing, the possibility to improve the specificity of whole-cell-based biosensors, via coupling herbicide-sensitive with herbicide-resistant strains, was verified.

Alternating current-dielectrophoresis driven on-chip collection and chaining of green microalgae in freshwaters

The capability of the AC dielectrophoresis (DEP) for on-chip capture and chaining of microalgae suspended in freshwaters was evaluated. The effects of freshwater composition as well as the electric field voltage, frequency, and duration, on the dielectrophoretic response of microalga Chlamydomonas reinhardtii were characterized systematically. Highest efficiency of cell alignment in one-dimensional arrays, determined by the percentage of cells in chain and the chain length, was obtained at AC-field of 20 V mm(-1) and 1 kHz applied for 600 s. The DEP response and cell alignment of C. reinhardtii in water sampled from lake, pond, and river, as well as model media were affected by the chemical composition of the media. In the model media, the efficiency of DEP chaining was negatively correlated to the conductivity of the cell suspensions, being higher in suspensions with low conductivity. The cells suspended in freshwaters, however, showed anomalously high chaining at long exposure times. High concentrations of nitrate and dissolved organic matter decrease cell chaining efficiency, while phosphate and citrate concentrations increase it and favor formation of longer chains. Importantly, the application of AC-field had no effect on algal autofluorescence, cell membrane damage, or oxidative stress damages in C. reinhardtii.

Lab on a chip technologies for algae detection: a review

Over the last few decades, lab on a chip technologies have emerged as powerful tools for high-accuracy diagnosis with minute quantities of liquid and as tools for exploring cell properties in general. In this paper, we present a review of the current status of this technology in the context of algae detection and monitoring. We start with an overview of the detection methods currently used for algae monitoring, followed by a review of lab on a chip devices for algae detection and classification, and then discuss a case study based on our own research activities. We conclude with a discussion on future challenges and motivations for algae-oriented lab on a chip technologies.

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.