Detection and classification of neurotoxins using a novel short-term plasticity quantification method

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.

Boolean modeling of neural systems with point-process inputs and outputs. Part I: theory and simulations

This paper presents a new modeling approach for neural systems with point-process (spike) inputs and outputs that utilizes Boolean operators (i.e. modulo 2 multiplication and addition that correspond to the logical AND and OR operations respectively, as well as the AND_NOT logical operation representing inhibitory effects). The form of the employed mathematical models is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean-Volterra model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of their accurate estimation from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, with excitatory and inhibitory terms, in the presence of considerable noise (spurious spikes) in the outputs and/or the inputs in a computationally efficient manner. A pilot application of this approach to an actual neural system is presented in the companion paper (Part II).

Boolean modeling of neural systems with point-process inputs and outputs. Part II: Application to the rat hippocampus

This paper presents a pilot application of the Boolean-Volterra modeling methodology presented in the companion paper (Part I) that is suitable for the analysis of systems with point-process inputs and outputs (e.g., recordings of the activity of neuronal ensembles). This application seeks to discover the causal links between two neuronal ensembles in the hippocampus of a behaving rat. The experimental data come from multi-unit recordings in the CA3 and CA1 regions of the hippocampus in the form of sequences of action potentials-treated mathematically as point-processes and computationally as spike-trains-that are collected in vivo during two behavioral tasks. The modeling objective is to identify and quantify the causal links among the neurons generating the recorded activity, using Boolean-Volterra models estimated directly from the data according to the methodological framework presented in the companion paper. The obtained models demonstrate the feasibility of the proposed approach using short data-records and provide some insights into the functional properties of the system (e.g., regarding the presence of rhythmic characteristics in the neuronal dynamics of these ensembles), making the proposed methodology an attractive tool for the analysis and modeling of multi-unit recordings from neuronal systems in a practical context.

Easily made single-walled carbon nanotube surface microelectrodes for neuronal applications

The present work examines the feasibility of a simple method for using single-walled carbon nanotubes (SWNT) to fabricate multielectrode arrays (MEA) for electrophysiological recordings. A suspension of purified SWNTs produced by arc discharged was directly deposited onto standard platinum electrodes. The in vitro impedance and electrochemical characterizations demonstrated the enhanced electrical properties of the SWNT microelectrode array. To test its functionality we performed extracellular ganglion cell recordings in isolated superfused rabbit retinas. Our results showed that SWNT based electrode arrays have potential advantages over metal electrodes and can be successfully used to record the single and multi-unit activity of ganglion cell populations.

Cortical networks grown on microelectrode arrays as a biosensor for botulinum toxin

Botulinum toxin (BoNT) is a potent neurotoxin produced by toxigenic strains of Clostridium botulinum. Botulinum toxin poses a major threat since it could be employed in a deliberate attack on the U.S. food supply. Furthermore, BoNT may be liberated in any insufficiently processed food containing a reduced oxygen atmosphere. Hence, rapid and reliable detection of BoNT in foods is necessary to reduce risks posed through food contamination. We present a BoNT biosensor employing living neural cultures grown in vitro on microelectrode arrays (MEAs). An MEA is a culture dish with a grid of electrodes embedded in its surface, enabling extracellular recording of action potentials of neural cultures grown over the array. Pharmaceutical grade BoNT A was applied to the media bath of mature cortical networks cultured on MEAs. Both spontaneous and evoked activities were monitored over 1 wk to quantify changes in the neural population produced by BoNT A. Introduction of BoNT A resulted in an increased duration and number of spikes in spontaneous and evoked bursts relative to control cultures. Increases were significant within 48 h of BoNT A dosage (P < 0.05). Application of BoNT A also induced unique oscillatory behavior within each burst that is reminiscent of early developmental activity patterns rather than the mature cultures used here. Three or more activity peaks were observed in 50% of the BoNT dosed cultures. Control cultures exhibited only a single activity peak. Thus activity of these cortical networks measured with MEAs could provide a valuable substrate for BoNT detection.

Screening for the effects of antiepileptic drugs on short term plasticity using a time efficient bioassay

Screening for changes in the short-term plasticity (STP) characteristics induced by antiepileptic drugs (AEDs) can be accelerated using a novel in vitro bioassay. The bioassay is based on the analysis of varying population spike (PS) amplitudes recorded in the CA1 region of the hippocampal slice in response to Poisson distributed random electrical stimuli. Three antiepileptic drugs (phenytoin 100 microM, carbamazepine 100 microM, and valproate 700 microM) were tested at maximal effective therapeutic concentrations. The data were analyzed using an advanced nonlinear approach that is more specific and time-efficient than the conventional paired pulse and fixed frequency train methods. STP was quantified by the first and the second order Volterra kernels. The first order kernel (k1) represented the mean PS amplitude while the second order kernel (k2) quantified the effect on the current PS amplitude of the interaction between the current stimulus impulse and each past stimulus impulse within a time (memory) window mu. The mean PS (k1 decreased by 15%, 10%, and 7% when phenytoin, carbamazepine, and valproate were added respectively. Phenytoin caused an increase in the k2 peak facilitation in the high frequency domain. Carbamazepine impaired frequency facilitation in the theta frequency range by causing a left shift in the second order kernel.

A novel and simple cell-based detection system with a collagen-encapsulated B-lymphocyte cell line as a biosensor for rapid detection of pathogens and toxins

Cell-based biosensors (CBBs) are becoming important tools for biosecurity applications and rapid diagnostics in food microbiology for their unique capability of detecting physiologically hazardous materials. A multi-well plate-based biosensor containing B-cell hybridoma, Ped-2E9, encapsulated in type I collagen matrix, was developed for rapid detection of viable cells of pathogenic Listeria, the toxin listeriolysin O, and the enterotoxin from Bacillus species. This sensor measures the alkaline phosphatase release from infected Ped-2E9 cells colorimetrically. Pathogenic L. monocytogenes cells and toxin preparations from L. monocytogenes or B. cereus showed cytotoxicity ranging from 24 to 98% at 3-6 h postinfection. In contrast, nonpathogenic L. innocua (F4247) and B. subtilis induced minimal cytotoxicity, ranging only 0.4-7.6%. Laser scanning cytometry and cryo-nano scanning electron microscopy confirmed the live or dead status of the infected Ped-2E9 cells in gel matrix. This paper presents the first example of a cell-based sensing system using collagen-encapsulated mammalian cells for rapid detection of pathogenic bacteria or toxin, and demonstrates a potential for onsite use as a portable detection system.

Improving bioassay sensitivity for neurotoxins detection using volterra based third order nonlinear analysis

Based on a novel analytical method for analyzing short-term plasticity (STP) of the CA1 hippocampal region in vitro, a screening tool for the detection and classification of unknown chemical compounds affecting the nervous system was recently introduced [1], [2]. The recorded signal consisted of evoked population spike in response to Poisson distributed random train impulse stimuli. The developed analytical approach used the first order Volterra kernel and the Laguerre coefficients of the second order Volterra model as classification features [3]. The biosensor showed encouraging results, and was able to classify out of sample compounds correctly [2]. We have taken an exploratory step to investigate the advantage of introducing a third order model [4]. DAP5, an NMDA channel blocker, did not show major changes in the second order kernel and in its corresponding Laguerre coefficients. Data were reanalyzed using a third order model. DAP5 showed discernable changes in the third order kernel as well as in the some of the corresponding Laguerre coefficients. Hence, the third order Volterra based model has the potential to improve the sensitivity and the discriminatory power of the proposed bioassay.

Investigating neuronal activity with planar microelectrode arrays: achievements and new perspectives

Neuronal networks underlie memory storage and information processing in the human brain, and ultimately participate in what Eccles referred to as "the creation of consciousness". Moreover, as physiological dysfunctions of neurons almost always translate into serious health issues, the study of the dynamics of neuronal networks has become a major avenue of research, as well as their response to pharmacological tampering. Planar microelectrode arrays represent a unique tool to investigate such dynamics and interferences, as they allow one to observe the activity of neuronal networks spread in both space and time. We will here review the major results obtained with microelectrode arrays and give an overview of the latest technological developments in the field, including our own efforts to develop the potential of this already powerful technology.