Sensitive operation of enzyme-based biodevices by advanced signal processing.

Analytical devices that combine sensitive biological component with a physicochemical detector hold a great potential for various applications, e.g., environmental monitoring, food analysis or medical diagnostics. Continuous efforts to develop inexpensive sensitive biodevices for detecting target substances typically focus on the design of biorecognition elements and their physical implementation, while the methods for processing signals generated by such devices have received far less attention. Here, we present fundamental considerations related to signal processing in biosensor design and investigate how undemanding signal treatment facilitates calibration and operation of enzyme-based biodevices. Our signal treatment approach was thoroughly validated with two model systems: (i) a biodevice for detecting chemical warfare agents and environmental pollutants based on the activity of haloalkane dehalogenase, with the sensitive range for bis(2-chloroethyl) ether of 0.01-0.8 mM and (ii) a biodevice for detecting hazardous pesticides based on the activity of γ-hexachlorocyclohexane dehydrochlorinase with the sensitive range for γ-hexachlorocyclohexane of 0.01-0.3 mM. We demonstrate that the advanced signal processing based on curve fitting enables precise quantification of parameters important for sensitive operation of enzyme-based biodevices, including: (i) automated exclusion of signal regions with substantial noise, (ii) derivation of calibration curves with significantly reduced error, (iii) shortening of the detection time, and (iv) reliable extrapolation of the signal to the initial conditions. The presented simple signal curve fitting supports rational design of optimal system setup by explicit and flexible quantification of its properties and will find a broad use in the development of sensitive and robust biodevices

Sensitive operation of enzyme-based biodevices by advanced signal processing

<div><p>Analytical devices that combine sensitive biological component with a physicochemical detector hold a great potential for various applications, e.g., environmental monitoring, food analysis or medical diagnostics. Continuous efforts to develop inexpensive sensitive biodevices for detecting target substances typically focus on the design of biorecognition elements and their physical implementation, while the methods for processing signals generated by such devices have received far less attention. Here, we present fundamental considerations related to signal processing in biosensor design and investigate how undemanding signal treatment facilitates calibration and operation of enzyme-based biodevices. Our signal treatment approach was thoroughly validated with two model systems: (i) a biodevice for detecting chemical warfare agents and environmental pollutants based on the activity of haloalkane dehalogenase, with the sensitive range for bis(2-chloroethyl) ether of 0.01–0.8 mM and (ii) a biodevice for detecting hazardous pesticides based on the activity of γ-hexachlorocyclohexane dehydrochlorinase with the sensitive range for γ-hexachlorocyclohexane of 0.01–0.3 mM. We demonstrate that the advanced signal processing based on curve fitting enables precise quantification of parameters important for sensitive operation of enzyme-based biodevices, including: (i) automated exclusion of signal regions with substantial noise, (ii) derivation of calibration curves with significantly reduced error, (iii) shortening of the detection time, and (iv) reliable extrapolation of the signal to the initial conditions. The presented simple signal curve fitting supports rational design of optimal system setup by explicit and flexible quantification of its properties and will find a broad use in the development of sensitive and robust biodevices.</p></div

Poor calibration based on slope versus a better quality calibration based on amplitudes in the case of a complex reaction kinetics.

<p>(A) The response of a biodevice based on experiments using 0.8 mg of haloalkane dehalogenase LinB and different concentrations of environmental pollutant 1-chlorohexane; (B) calibration points for slopes and amplitudes.</p

Comparison of the calibration curves obtained via the two approaches: The standard analysis and nonlinear curve fitting.

<p>(A) Calibration curves for the biodevice based on experiments with 0.8 mg of haloalkane dehalogenase LinB and sulfur mustard surrogate bis(2-chloroethyl) ether; (B) The normalized standard deviations of the estimates of the corresponding values used for calibration; the average normalized standard deviations of estimates (bars) were 0.8, 2.2, 6.7, and 64.5 ·10⁻³ for amplitudes and slopes from nonlinear curve fitting, and amplitudes and slopes from the standard analysis, respectively.</p

The 13 sites selected for testing the detection of lindane in Sabinanigo.

<p>The 13 sites selected for testing the detection of lindane in Sabinanigo.</p

Comparison of the four candidate variables for calibration of a biodevice for the detection of lindane.

<p>(A) The response of a biodevice based on experiments with 0.1 mg of dehydrochlorinase LinA and different concentrations of environmental pollutant lindane and the corresponding fitted curves (black); (B) Calibration curves for the biodevice: amplitudes from the nonlinear curve fitting (green), slopes from the nonlinear curve fitting (red), amplitudes from the standard analysis (blue), and slopes from the standard analysis (yellow); the average normalized standard deviations of estimates (bars) were 2, 6 ·10⁻³ for the amplitudes and slopes from nonlinear curve fitting and 7, 126 ·10⁻³ for the amplitudes and slopes from the standard analysis, respectively.</p

Extrapolation of the curves fitted based on the different time windows to the equilibrated signal at later times in the nonlinear curve fitting.

<p>(A) A signal based on experiments with 0.8 mg of haloalkane dehalogenase LinB and sulfur mustard surrogate bis(2-chloroethyl) ether and the (extrapolated) nonlinear fit for different cut-off times (in the legend); (B) The values of the amplitude as a function of the cut-off time for fitting (left), and the proportion of the curve excluded from the fitting expressed as a percentage (right).</p

An example of raw data from measurements and the demonstration of the principle behind the automated selection of the relevant signal part for further analysis.

<p>(A) The signals with a substantial mixing noise in experiments using 2 mg of haloalkane dehalogenase LinB and different concentrations of bis(2-chloroethyl) ether; (B) The corresponding absolute values of the slope differences calculated by linear regression with a moving window; the solid lines correspond to the smooth signal region identified automatically by the software; a dashed line is the threshold for the definition of the smooth segment. A mixing period (dots) is usually the time from the beginning of an injection to a completely dissolved/mixed sample and, thus, this period varies in time among different measurements. The y-axes were cut to focus on the meaningful signal.</p

A schematic representation of the biodetection systems and data processing methods.

<p>Compounds to be detected by the developed biodevices are the surrogate bis(2-chloroethyl)ether, a common environmental pollutant 1-chlorohexane, a wide-spread toxic pesticide lindane (1,2,3,4,5,6-hexachlorocyclohexane, γ-hexachlorocyclohexane), and the chemical warfare sulfur mustard (left). The handheld fluorimeter and the model enzymatic reaction is given in the middle; in the case of the handheld fluorimeter, the reaction is initiated by adding the respective enzyme, and a fluorescent signal is recorded at ambient temperature and calculated as a ratio of the responses obtained after excitation at 485 and 410 nm, respectively. The two types of data analysis compared in this article are standard analysis mainly based on the raw data and a more sophisticated nonlinear curve fitting (right).</p

Comparison of different approaches to signal processing and calibration.

<p>Comparison of different approaches to signal processing and calibration.</p