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Engineering » Biomedical Engineering » "Biosensors", book edited by Pier Andrea Serra, ISBN 978-953-7619-99-2, Published: February 1, 2010 under CC BY-NC-SA 3.0 license. © The Author(s).

Chapter 14

Design and Construction of a Distributed Sensor NET for Biotelemetric Monitoring of Brain Energetic Metabolism Using Microsensors and Biosensors

By Pier Andrea Serra, Giulia Puggioni, Gianfranco Bazzu, Giammario Calia, Rossana Migheli and Gaia Rocchitta
DOI: 10.5772/7213

Article top

Overview

Pictures showing the biotelemetry system developed during this study (see text).
Figure 1. Pictures showing the biotelemetry system developed during this study (see text).
Schematic of the amperometric module of the biotelemetric device working in “reduction mode”.
Figure 2. Schematic of the amperometric module of the biotelemetric device working in “reduction mode”.
Schematic of the amperometric module of the biotelemetric device working in “oxidation mode”.
Figure 3. Schematic of the amperometric module of the biotelemetric device working in “oxidation mode”.
Schematic of the biotelemetric system developed and used in this study.
Figure 4. Schematic of the biotelemetric system developed and used in this study.
Firmware running on the implantable biotelemetric device.
Figure 5. Firmware running on the implantable biotelemetric device.
Program process of MSP430F2274 consisting on edit-compile-download cycle.
Figure 6. Program process of MSP430F2274 consisting on edit-compile-download cycle.
Simple graphic user interface of the data-acquisition software running on the PC.
Figure 7. Simple graphic user interface of the data-acquisition software running on the PC.
Schematic representation of the oxygen microsensor used in this study.
Figure 8. Schematic representation of the oxygen microsensor used in this study.
Oxygen microsensor in-vitro calibration.
Figure 9. Oxygen microsensor in-vitro calibration.
Schematic representation of the ascorbic acid microsensor used in this study.
Figure 10. Schematic representation of the ascorbic acid microsensor used in this study.
Ascorbic acid microsensor in-vitro calibration.
Figure 11. Ascorbic acid microsensor in-vitro calibration.
Schematic representation of the glucose biosensor used in this study.
Figure 12. Schematic representation of the glucose biosensor used in this study.

						In-vitro calibration of glucose biosensor (see text).
Figure 13. In-vitro calibration of glucose biosensor (see text).
Schematic representation of the lactate biosensor used in this study.
Figure 14. Schematic representation of the lactate biosensor used in this study.

						In-vitro calibration of lactate biosensor (see text).
Figure 15. In-vitro calibration of lactate biosensor (see text).
Amperometric sensor inserted in the right striatum by means of sterotaxic surgery. The sensor was wired to the biotelemetric device fixed to the skull (inset).
Figure 16. Amperometric sensor inserted in the right striatum by means of sterotaxic surgery. The sensor was wired to the biotelemetric device fixed to the skull (inset).
Biotelemetric device implanted in a freely-moving rat.
Figure 17. Biotelemetric device implanted in a freely-moving rat.
Effect of physiological stimulation (5 min. tail pinch, vertical band) on striatal dissolved oxygen.
Figure 18. Effect of physiological stimulation (5 min. tail pinch, vertical band) on striatal dissolved oxygen.
Effect of a 5 min. tail pinch (vertical band) on striatal AA.
Figure 19. Effect of a 5 min. tail pinch (vertical band) on striatal AA.
Effect of a 5 min. tail pinch (vertical band) on striatal glucose.
Figure 20. Effect of a 5 min. tail pinch (vertical band) on striatal glucose.
Effect of a 5 min. tail pinch (vertical band) on striatal lactate.
Figure 21. Effect of a 5 min. tail pinch (vertical band) on striatal lactate.
Pearson's correlation analysis of striatal neurochemical changes during neural activation.
Figure 22. Pearson's correlation analysis of striatal neurochemical changes during neural activation.
Neurochemical pathway involved in brain energetic metabolism during neural activation. Glut: glutamate; Gluc: glucose; Lac: lactate; Pyr: pyruvate; GLUT: glucose transporter; SVCT: AA transporter; MCT: lactate transporter.
Figure 23. Neurochemical pathway involved in brain energetic metabolism during neural activation. Glut: glutamate; Gluc: glucose; Lac: lactate; Pyr: pyruvate; GLUT: glucose transporter; SVCT: AA transporter; MCT: lactate transporter.

Design and Construction of a Distributed Sensor NET for Biotelemetric Monitoring of Brain Energetic Metabolism using Microsensors and Biosensors

Pier Andrea Serra1, Giulia Puggioni1, Gianfranco Bazzu1, Giammario Calia1, Rossana Migheli1 and Gaia Rocchitta1

1. Introduction

Neurochemical pathways involved in brain physiology or disease pathogenesis are mostly unknown either in physiological conditions or in neurodegenerative diseases. Nowadays the most frequent usage for biotelemetry is in medicine, in cardiac care units or step-down units in hospitals, even if virtually any physiological signal could be transmitted (FCC, 2000; Leuher, 1983; Zhou et al., 2002). In this chapter we present a wireless device connected with microsensors and biosensors capable to detect real-time variations in concentrations of important compounds present in central nervous system (CNS) and implicated in brain energetic metabolism (Bazzu et al., 2009; Calia et al., 2009).

Molecular Oxygen (O2), an essential molecule for life, is utilized not only for cellular respiration but also for biosynthesis and metabolism of various important biomolecules such steroids, eicosanoids and neuroactive substances (Watanabe et al., 1997). It is also implicated in several biochemical reactions involving, for instance, ATP in the brain (Bazzu et al., 2009). To monitor istantaneous oxygen concentrations and relative variations could give important information about brain energetic metabolism related to glucose (Fillenz, 2005) or lactate consumption (Aubert et al., 2005). Eletrochemical reduction of oxygen is amperometrically obtained by means of complex transformations occurring at a carbon-epoxy sensor surface by applying a potential of -400 mV vs Ag/AgCl reference electrode (RE) (Bazzu et al., 2009; Calia et al., 2009) as follows:

O2+ 2H++ 2e H2O2
(1)
H2O2+ 2H++ 2e 2H2O
(2)

Because of its anatomical characteristics and physiology, the central nervous system (CNS) is assumed to be particularly sensitive to oxidative stress (Emerit et al., 2005). Oxidative stress in CNS is now known as responsible for involvement of reactive oxygen species (ROS) and reactive nitrogen species (RNS). This particular kind of stress is crucial for the modulation of fundamental cellular functions as apoptosis, calcium mobilization, ion transport and all phenomena involved in excitotoxicity (Emerit & Edeas, 2005). Oxidative stress takes place from a disparity, involving the physiological antioxidant capability and free radicals synthesis (Andersen, 2004), so the dynamics concerning antioxidants in CNS are extremely important.

Ascorbic acid (AA) is the most important component of antioxidant pool in the brain (Serra et al., 2002). Low molecular weight antioxidant AA is a water soluble vitamin that owns radical scavenger properties against ROS and RNS (Serra et al., 2002). As it is not produced in human organism but introduced with diet (Hediger, 2002), the presence of a specific transporter (SVCT2) allows its cytosolic internalization in neurons. Ascorbic acid is readily oxidized to dehydroascorbate (DHAA) which can undergo irreversible hydrolysis to 2,3-diketo-L-gulonic acid, but because of its crucial role in CNS, DHAA is easily converted to the reduced form for preventing vitamin C depletion. AA is as well implicated in the protection against excitotoxicity due to high glutamate concentration through ascorbate-glutamate hetero-exchange (Rice, 2000). Ascorbic acid is readily oxidized on epoxy-carbon electrode surface at + 120 mV against Ag/AgCl as follows:

LAscorbic acid (+120 mV)  DHAA + 2e+ 2H+
(3)

Several biologically interesting compounds are not directely oxidizable at a sensor surface, in this case it is needed a biosensor (Pantano et al., 1995). Biosensor combines a trasducer with an enzyme that is capable to give sensitivity and selectivity to the device (Scheller et al., 1997).

Glucose (GLU) is actively involved in ATP synthesis and its concentration in extracellular spaces is the most important factor for energetic metabolism (Lowry et al., 1998; Magistretti et al., 1999, Fillenz et al., 2005). GLU detection is possible by means of loading on a Pt surface transducer glucose oxidase (GOx) enzyme. GOx is covalently linked with flavin adenine dinucleotide (FAD) (Serra et al., 2007) and is extremely reliable because of its good sensitivity to the enzyme substrate and high stability when immobilized on Pt electrodes by means of poly-orthophenylenediamine (OPD) (Wilson et al., 1992; Lowry et al., 1998). The biosensor-based detection of GLU occurs as follows:

βDGlucose + FAD+GOx  DGluconoδLactone + FADH2GOx
(4)
FADH2 GOx + O2FAD+GOx + H2O2
(5)

As glucose, lactate (LAC) is an important molecule involved in brain energetic metabolism as product of glicolysys (Fillenz, 2005) or energetic substrate for neurons (Magistretti et al., 1999). LAC is oxidized by lactate oxidase (LOx), an enzyme with a covalently-linked FAD cofactor (Serra et al., 2007). After the oxidation of the substrate, the further oxidation of FADH2 to FAD+ produces H2O2 in the presence of O2.

LLactate + FAD+LOx  Pyruvate + FADH2LOx
(6)
FADH2 LOx + O2FAD+LOx + H2O2
(7)

The application of a positive potential (+700 mV vs Ag/AgCl) to a Pt working electrode generates a current proportional to the concentration of the H2O2. Reactions occur as follows:

H2O2 O2+ 2e+ 2H+
(8)

where the amount of current produced by the previous oxidation (8) is proportional to the quantity of substrate (GLU or LAC) transformed by the enzyme. The intrinsic chemical characteristics of each indicated molecule allow using specific telemetric devices able to work in oxidation mode (Calia et al., 2009) or in reduction mode (Bazzu et al., 2009; Calia et al., 2009). In this chapter we describe the implementation of a distributed biosensor NET, composed by implantable biotelemetry devices and derived from previously published systems (Bazzu et al., 2009; Calia et al., 2009), successfully used in conjunction with micro-biosensors for the in-vivo measurement of brain O2, AA, glucose and lactate. The analysis of signals derived from the above sensors allowed the real-time study of biochemical pathways involved in brain energetic metabolism. The design, construction and operation of the hardware, firmware and software are described. The proposed system, based on simple and inexpensive components, could be used as a rapid and reliable model for the real-time study of the effects of different drugs on brain neurochemicals and offers the possibility of expanding the biotelemetric NET simply and quickly.

2. Biotelemetric system hardware

The electronic circuit of the implantable biotelemetric device (Figure 1) was built using surface mount components and comprised two different parts: the amperometric module and the microcontroller/transceiver module.

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Figure 1.

Pictures showing the biotelemetry system developed during this study (see text).

As schematized in Figure 2 and 3, the amperometric module was made soldering a quad operational amplifier (MCP6042), a Zener diode (ZXRE4001), two resistors, a potentiomenter and one capacitor on a 28 mm x 17 mm PCB. The MCP6042 can operate from a single-supply voltage with “rail-to-rail” inputs and outputs; it has been designed for micropower applications consuming only 600 nA per OPA with low input bias current (1 pA) and high input resistance (>1013Ω).

The zener diode plays a pivotal role in the amperometric circuitry; as bandgap voltage reference, it generates a fixed voltage (Vz = 1.22 V) consuming around 10 µA in virtue of a limiting resistance (150K).

The not inverting input of the potentiostat was connected to Vz (Bazzu et al., 2009; Calia et al., 2009) for working with O2 microsensors (reduction mode, Figure 2).

In oxidation mode (Figure 3), the above mentioned input of the potentiostat was grounded resulting in a positive potential applied to working electrode (WE) ranging from ground

media/image10.png

Figure 2.

Schematic of the amperometric module of the biotelemetric device working in “reduction mode”.

(GND) to Vz. The inverting input of the potenziostat was directly connected to RE while its output wired to the auxiliary (AE) electrode. This allows the implementation of a feedback circuit in which RE and AE potentials are maintained at the same potential of the not-inverting input (potentiostatic circuit).

The buffered voltage divider, composed by the potentiomenter and the voltage follower, generated the potential necessary to polarize the WE in a range comprised between 0 and 1.22 V (Vz, controlled by ZXRE4001).

media/image11.png

Figure 3.

Schematic of the amperometric module of the biotelemetric device working in “oxidation mode”.

The current-to-voltage (I/V) converter is a single-supply adaptation of a classic transimpedance amplifier and derived form a previously-published design (Serra et al., 2007; Rocchitta et al., 2007). The transfer function of the I/V converter is:

VOut= (Iredox· R) + VApp
(9)

in which Iredox is the current flowing through the WE, R is the feedback resistor and VApp is the potential applied to the WE (versus ground). For example, working in reduction mode, a current of 0 nA corresponds to VApp (0.8 V, -0.4 V vs Vz) while with a cathodic current of -30 nA VOut is equal to 0.5 V (R=10MΩ). The maximum allowable current is ≈ -80 nA (VOut = 0.0 V). Instead, when the system is configured in oxidation mode, a maximum anodic current of 18 nA can be read without saturation (VApp = 0.7 V; VOut = 2.5 V; R =100MΩ). The feedback resistor (R) has a capacitor in parallel (C) to complete a low-pass filter with a cut-off frequency (Fcut-off) of 5 Hz. The value of C was calculated in Farads according to the equation:

= 1 / (Fcutoff 2π  R)
(10)

A dummy cell was made based on a previously published design (Serra et al., 2007) for testing the amperometric module of the biotelemetric device before sensors calibration. The aim of the design was to devise a Thevenin current source that would reproduce the constant amperometric response of a true electrochemical cell. The voltage applied to the dummy cell was generated between the WE and RE/AE and is equal to the voltage difference between the above electrodes. The resulting currents were converted in an output voltage (VOut) as illustrated above. Data obtained from electronics calibration are similar to those obtained in previous studies (Bazzu et al., 2009; Calia et al., 2009).

The MSP430F2274 (Texas Instruments, TI) is the heart of the digital module. This is a 16-bit CMOS IC with ultra-low power features equipped with internal 10 bit ADCs. The microcontroller unit (MCU) performed the A/D conversion of VOut, VBatt, VApp and in-chip temperature using a 2.5 V internal reference. After the digital signal processing (DSP) of acquired raw data, a serial data packet was generated and sent to the transceiver: the TI CC2500 multi-channel RF transceiver, designed for low-power wireless applications (2.4 GHz).

Several pins of the microcontroller were connected to the CC2500 providing the data lines of the transceiver. In conjunction with the MCU, this component allows the realization of a serial data transmitter working at the speed of 9600 baud. A miniaturized chip antenna was integrated in the PCB board. The in-circuit-serial-programming (ICSP) bus provides the possibility of programming the MCU “on-board” in a few seconds. The digital module (Fig 1B; Fig. 1C) is commercially available pre-assembled by TI (eZ430-RF2500). A 210 mAh, 3 V lithium coin battery (Maxell CR2032) provided the power to the biotelemetric device for up to one week in continuous transmission (1 Hz). A small plastic enclosure (3.2 x 2.1 x 1.4 mm; Fig. 1A) completed the implantable device (Fig. 1B, inset). The weight of the implantable device (Figure 1, inset) is 12.4 grams including battery. A second digital module (wired unit, Fig. 1C), connected to a personal computer (PC) by means of an USB-programmer, coupled the implanted biotelemetric device with the software running on the PC side (Figure 4).

3. Firmware and software

The firmware to drive the MSP430F2274 MCUs was realized in C language using IAR Embedded Workbench (version 5.20, KickStart) freely available from www.ti.com. The program, which runs on the biotelemetry device, consists of two routines: the main procedure and a timer-interrupt routine called every second as illustrated in Figure 5.

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Figure 4.

Schematic of the biotelemetric system developed and used in this study.

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Figure 5.

Firmware running on the implantable biotelemetric device.

When the analogue signal VOut was digitized, the hardware ADC resolution (10 bit) was improved following the oversampling and averaging method (Pagnacco et al., 1997):

where w is the number of additional bits of resolution (2), Fs is the sampling frequency (1 Hz) and Fos is the oversampling frequency. In accordance with the Nyquist’s theorem Fs was calculated as follows:

Fs= 2 · Fmax
(12)

in which Fmax has been fixed to 2 Hz. To do that, the MCU acquired and accumulated 64 consecutive samples then divided the result by 26 (Figure 5). This technique increased the ADC resolution from 10 to 12 bits. The RF data packets (1 per second), sent by the MCU, were encapsulated in an Open Source Stack (SimpliciTI™), implemented by Texas Instruments for the development of low power wireless NETs. Up to 100 implantable units, with an 8 bit ID memorized in their E2PROM, can share the same transmission channel. The interrupt-driven routine on the receiver unit, mounting a second MSP430F2274, communicates to the PC using USB and filters the IN-OUT data. The firmware was downloaded to the MCUs using the TI USB programmer connected to the ICSP bus (Figure 6).

media/image18.png

Figure 6.

Program process of MSP430F2274 consisting on edit-compile-download cycle.

The software, running on the PC under Windows XP Professional or Vista, communicates thought the USB by using the low-level driver freely available from www.ti.com. The graphic user interface (Figure 7) was developed using Profilab Expert® (version 4.0 from Abacom) while the dynamic link library (DLL) serial-data-parser was programmed in C (Dev-C++ ver. 4.9.9.2).

The application (capable of plotting, storing and retrieving data) interfaces the system with a printer via USB and a Local Area Network (LAN) and Internet via TCP/IP (Figure 4). A software alarm was generated if VBatt< 2.7 V, signal strength (RSSI) too low or a data reception time-out occurred.

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Figure 7.

Simple graphic user interface of the data-acquisition software running on the PC.

4. Statistical analysis

Concentrations of O2 were expressed in µM while AA, glucose and lactate in mM. Oxygen, ascorbate and H2O2 signals were expressed in nA and given as baseline-subtracted data (ΔnA). The sensors in-vitro response was characterized immediately before implantation and the electrochemical parameters evaluated before in-vivo experiments. The changes of brain tissue neurochemicals were calculated as absolute variations versus the corresponding baselines and their striatal concentrations were estimated using pre-implantation in-vitro calibrations.

Statistical significance of changes was evaluated using paired t-tests between the means of 300 consecutive recordings before (baseline) and during the maximum magnitude of neurochemical changes as a result of physiological stimulation (tail pinch).

Pearson's correlation coefficient was used to show correlation significance of current variations induced by tail pinches among O2, AA, glucose and lactate. Immediately after the stimulus application, the first five minutes of raw data (n=300) were compared point-by-point. The null hypothesis was rejected when p < 0.05.

5. Design, construction and calibration of oxygen and ascorbic acid microsensors

Oxygen microsensor construction and calibration were performed as previously described (Bazzu et al., 2009; Calia et al., 2009). Briefly, 1 mm silver wire (25 mm in length; Ø=125 µm, Advent Research Materials, Suffolk, UK) was introduced in a silica capillary tube (10 mm in length; I.D. Ø=180 µm, Polymicro Technologies, Phoenix, USA), partially filled with epoxy-carbon (EC) obtained mixing 450 mg of graphite with 350 mg epoxy resin (Araldite-M®, Sigma-Aldrich, Milan, Italy). A 180 µm diameter carbon composite disc electrode was made up and left 12 hours at 60 C. A conical shape was provided to the microsensor by drilling the tip.

The final oxygen microsensors (Figure 8) had a length about 250 µm, a surface of 0.00145 cm2 and a tip < 25 µm. Nitrocellulose (NC, collodion) treatment was carried out dipping the microsensor in the collodion solution (4% nitrocellulose in ethanol/diethyl ether) 3 times and drying it for 1 hour at 40 C after each coating. Cellulose nitrate membrane, because of its hydrophobic properties, sets a barrier for large organic molecules, as proteins, avoiding poisoning of microsensor surface while can be crossed by small charged ions and gases (Bazzu et al., 2009).

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Figure 8.

Schematic representation of the oxygen microsensor used in this study.

O2 reduction potential was experimentally determined in a previous study (Bazzu et al., 2009) using cyclic voltammetry and fixed at at -400 mV vs Ag/AgCl reference electrode. Constant potential amperometry (CPA) was used for in-vitro and in-vivo calibrations and experiments. No significant interferences were remarked on exposing sensors to other electroactive molecules (AA, UA, DA, DOPAC and HVA) present in the striatal extracellular fluid at pharmacologically significant concentrations (Calia et al., 2009). O2 microsensor was calibrated by adding known volumes of a standard O2 solution (100%) to nitrogenated PBS and only sensors with sensitivity lower than 10 µM were chosen. All in-vitro calibrations of oxygen microsensors were carried out 24 h after manufacture, immediately before implantation, using a previously-described electrochemical cell (Serra et al., 2007; Rocchitta et al., 2007) appropriately set for oxygen (Bazzu et al., 2009). The calibration (Figure 9) exhibited good linearity showing a slope of -254 ± 32 pA µM-1 of O2 (R2=0.999; n=6), in line with previous observations (Calia et al., 2009).

AA microsensors (Figure 10) were manufactured in the same way as oxygen microsensors without the collodion layer or any further surface modification.

In-vitro calibrations of ascorbic acid microsensors were carried out in fresh PBS at room temperature (25 C) before implantation. A constant potential of +120 mV was applied and after a stabile baseline was reached, known amount of ascorbic acid stock solution were added to PBS in order to obtain concentrations ranging from 0 to 5 mM.

AA microsensors, calibrated before implantation (Figure 11), showed good sensitivity and good linearity (2.96 nA ± 0.1 mM-1, R2= 0.998, n=6).

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Figure 9.

Oxygen microsensor in-vitro calibration.

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Figure 10.

Schematic representation of the ascorbic acid microsensor used in this study.

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Figure 11.

Ascorbic acid microsensor in-vitro calibration.

6. Design, construction and calibration of glucose and lactate biosensors

The design of the glucose biosensors (Figure 12) has been previously described in detail (Serra et al., 2007). Briefly, 1 mm platinum (Pt) cylinder, obtained cutting Teflon-insulated Pt wire (Ø=125 µm, Advent Research Materials, Suffolk, UK), was immersed for 5 min into a solution of glucose oxidase (GOx) to allow adsorption. After 10 min drying at room temperature, the biosensor was placed in the cell filled with 5 ml of nitrogenated PBS containing the o-phenylenediamine monomer (OPD, 250 mM). The electrosynthesis of poly-o-phenylenediamine (p-OPD) was carried out at +700 mV vs Ag/AgCl reference electrode for 15 min. After electropolymerization, the biosensor was stored in fridge (4 C).

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Figure 12.

Schematic representation of the glucose biosensor used in this study.

The in-vitro calibration of glucose biosensor was made in air-bubbled PBS performing ten successive injections of glucose (0.2, 0.4, 0.6, 1, 2, 10, 20, 60, 100, 140 mM). The calibration data (Figure 13) well fitted with the Michaelis-Menten equation (R2=0.984, n=6) with a VMAX of 59.6±0.8 nA and a KM of 5.6±0.4 mM. The response to low concentration of glucose (0-2 mM; Figure 13, inset), revealed excellent linearity (R2=0.999, n=6) and a slope of 8.9±0.08 nA mM-1.

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Figure 13.

In-vitro calibration of glucose biosensor (see text).

The fabrication of the lactate biosensors (Figure 14) has been derived from a previously described procedure used to make glutamate biosensors (McMahon et al., 2006). A Pt cylinder (1 mm) was placed in the electrochemical cell containing nitrogenated PBS and OPD monomer and p-OPD was electrosynthesized as previously described for glucose biosensor. The Pt/p-OPD cylinder was immersed (quick dip) into a solution of polyethylenimmine (PEI, 0.5%in water)-LOx (25 U/50 µl of PBS) to allow oxydase adsorption. After 5 min drying at room temperature, the dipping procedure was repeated four times.

With the purpose of increasing the KM of the biosensor, a diffusion-reducing membrane (Schuvailo et al., 2006) was applied on top of the PEI-LOx layers by dipping the biosensor in a polyurethane (PU) solution (2.5% in tetrahydrofurane). Finally, the biosensor was stored in fridge.

Constant potential amperometry (CPA) was used for in-vitro and in-vivo experiments fixing the H2O2 oxidation potential at +700 mV (Serra et al., 2007) vs Ag/AgCl.. All in-vitro calibrations were performed in fresh PBS 24 h from sensors’ manufacture as previously described in detail (Rocchitta et al., 2007).

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Figure 14.

Schematic representation of the lactate biosensor used in this study.

The in-vitro response of lactate biosensor was determined only before implantation adding known amounts of lactate in the electrochemical cell giving concentrations ranging between 0 and 150 mM.

Calibrations showed a classical Michaelis-Menten kinetic (R2=0.944, n=6) with VMAX and KM equal respectively to 92.8±2 nA and 8.7±0.9 mM (Figure 15). Linear region was evaluated at low concentration (0-5 mM) and it showed good linearity (R2=0.997, n=6) with a slope of 6.7±0.2 nA mM-1 (Figure 15, inset).

No significant interference signals were observed on exposing biosensors to AA or other electroactive molecules present in the striatal extracellular fluid, even at pharmacologically relevant concentrations (Calia et al., 2009).

7. Animals and neurosurgery

Male Wistar rats (Morini R. Emilia, Italy), weighing 250-350 g were used in all experiments. Rats were kept under standard animal care conditions with 12 h light/dark cycle, and room temperature 21 C, food and water ad libitum.

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Figure 15.

In-vitro calibration of lactate biosensor (see text).

All procedures were licensed under the European Community directive 86/609 included in Decreto No. 116/1992 of the Italian Ministry of Public Health.

Stereotaxic surgery was performed under chloral hydrate (400 mg Kg-1 i.p.) anesthesia and body temperature during anaesthesia was maintained at 37 C by means of an isothermal heating pad.

Before each experiment, the health of the animals was assessed according to published guidelines (Wolfensohn and Lloyd, 2003).

O2 and AA microsensors, glucose and lactate biosensors were implanted in the right striatum (Figure 16) using the following coordinates from the atlas of Paxinos & Watson (Paxinos & Watson, 2007): A/P +0.5 from bregma, +2.5 M/L, and -4.0 D/V from dura. Reference and Auxiliary electrodes were implanted in the parietal cortex.

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Figure 16.

Amperometric sensor inserted in the right striatum by means of sterotaxic surgery. The sensor was wired to the biotelemetric device fixed to the skull (inset).

The biotelemetric device was fixed by mean of two screws inserted in the skull (Figure 16, inset), as previously described (Bazzu et al., 2009; Calia et al., 2009).

Following surgery, the animals were housed in large Plexiglas bowls (45 cm diameter), and maintained in a temperature- and light- controlled environment, with free access to food and water. The sensors were connected 24 h after surgery. The monitoring of neurochemicals started with the animal in its home bowl allowing the rat free movement (Figure 17).

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Figure 17.

Biotelemetric device implanted in a freely-moving rat.

8. In-vivo experiments and results

Baseline recordings and physiological stimulation were carried out within the first day after stereotaxic surgery, starting 24 h after implantation. The Oxygen microsensor reached a stable baseline (-24.3 ± 2.7 nA) after a period of about 50 minutes from the application of the working potential. Considering that the background current of the microsensor in nitrogen-saturated PBS was -14.2 ± 0.9 nA, it is possible to estimate the concentration of O2 using in-vitro pre-calibration; this was found to correspond to 39.8 ± 7.1 μM, a value in agreement with previous results (Bazzu et al., 2009; Calia et al., 2009). Physiological stimulation as five-minute tail pinch (Figure 18), administered in order to increase neural activity and to promote regional cerebral blood flow (rCBF), led to an increased motor activity yielding to a striatal O2 current of -2.97 nA, corresponding to +11.69 µM, statistically different from baseline (p<0.001; Figure 18, inset).

Striatal oxygen dynamics, following physiological stimulation, resulted in a rise in the local O2 signal, mainly related to an increase of rCBF during neural activation in agreement with previous findings (Bazzu et al., 2009; Calia et al., 2009).

A stable AA baseline was reached after a period of about 25 minutes. The calculated AA baseline corresponded to a concentration of 566 µM, in line with previous findings (Calia et al., 2009). A physiological increase (p<0.001; Figure 19, inset) of striatal AA current (+0.447 nA corresponding to +228 µM) was observed immediately after the administration of a 5 min tail pinch (Figure 19).

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Figure 18.

Effect of physiological stimulation (5 min. tail pinch, vertical band) on striatal dissolved oxygen.

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Figure 19.

Effect of a 5 min. tail pinch (vertical band) on striatal AA.

This result is consistent with the activation of the AA/glutamate heteroexchange system (Rice, 2000) in which AA release is related to glutamate release and uptake subsequent to neural activation (Miele et al., 2000).

Glucose biosensor in-vivo experiments were carried out using the same procedures as oxygen and AA ones. Thirty minutes after biosensor activation a stable anodic current corresponding to 4.6±0.24 nA (517±26 µM from in-vitro calibration) was reached. Physiological stimulation (Figure 20) produced a significant decrease of glucose signal (-167 µM; Figure 20, inset) followed by its increase after stimulus suspension.

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Figure 20.

Effect of a 5 min. tail pinch (vertical band) on striatal glucose.

A stable baseline was observed 40 min after lactate biosensor polarization and corresponded to 5.09±0.43 nA (759±62 µM from the above in-vitro calibration). A five-minute tail pinch (Figure 21) resulted in a significant increase of lactate signal during stimulus administration and after stimulus suspension (+544 µM, p<0.001; Figure 21, inset).

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Figure 21.

Effect of a 5 min. tail pinch (vertical band) on striatal lactate.

These results are suggestive of glucose consumption and lactate production during neural activation followed by an increase of extracellular levels of glucose subsequent to astroglial glycogenolysis (Fillenz et al., 1999). Figure 22 shows Pearson's correlation analysis of current variations induced by tail pinches among O2, AA, glucose and lactate.

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Figure 22.

Pearson's correlation analysis of striatal neurochemical changes during neural activation.

It is important to remember that an increase in anodic currents (AA, glucose and lactate) reflects a striatal increase of the above neurochemicals while an increase in extracellular dissolved O2 levels, corresponds to an increase in cathodic oxygen current, resulting in a more negative signal. Results of correlation analysis are summarized in Table 1.

Ascorbic AcidGlucoseLactate
Oxygenr = - 0. 819 P<0.00 0 1r = 0. 401 P<0.00 0 1r = - 0. 457 P<0.00 0 1
Ascorbic Acidr = -0.266 P<0.00 0 1r = 0.5 18 P<0.00 0 1
Glucoser = -0.388 P<0.00 0 1

Table 1.

Results of correlation analysys of striatal neurochemical changes during neural activation.

After each experiment, rats were sacrificed with an injection of chloral hydrate (800 mg Kg-1 i.p.). The location of each sensor was established by post-mortem histology. Brains were fixed in formal saline and 50 µm coronal sections were made with a cryostat. The slices were stained with cresyl violet and examined under a microscope.

9. Discussion and conclusion

In this chapter we have described the development of a distributed sensor NET for biotelemetric monitoring of brain neurochemistry using amperometric microsensors and biosensors. The implantable biotelemetric units, based on simple and inexpensive components, have been successfully used for accurate transduction of the electrochemical signals generated on the surface of the sensors implanted in the striatum of freely-moving rats. At this stage of development the system exhibits high stability and excellent linear response in the nanoampere current range. The choice of low-power CMOS technology makes the project suitable of further improvements such as the reduction of the power consumption. The use of surface mount components and careful PCB design will make it possible to further miniaturize the circuitry. As result of in-vivo experiments, we have shown some preliminary data obtained monitoring important brain molecules during short-time neural activation (5’ tail pinch). The study was carried out using new microsensors, biosensors and the newly-developed biotelemetric device in order to record real-time changes of key neurochemical markers of brain bioenergetic metabolism. Indeed, all studied species (oxygen, AA, glucose and lactate) are implicated in brain energetics.

Recently, Castro and coworkers (Castro et al., 2009) proposed a new pivotal role for AA as modulator in the metabolic switch between glucose and lactate as energetic substrate of neurons. This review paper, mainly based on in-vitro results, summarizes the evidences of the relationships among glutamate, AA, glucose and lactate during neural activation. In Figure 23, we repropose the model suggested by Castro, updated on the basis of our in-vivo results. In brief, in normal conditions both neurons and astrocytes use glucose as energetic substrate. After neural activation, cortico-striatal endings release glutamate (1) in the striatal extracellular compartment (Miele et al., 2000). Immediately after, GLU is taken by astrocytes from the synaptic cleft and exchanged (Rice, 2000) with AA (2). The in-vivo increase of extracellular AA during tail pinch is in accord with this mechanism. The glutamate entry into astrocyte stimulates glucose internalization (3), glycolysis (4) and lactate efflux (5). We observed a reduction of extracellular glucose and an increase of lactate during tail pinch in agreement with this model. Finally, ascorbic acid enters neurons (6) through a specific transporter (SVCT2) and activates the metabolic switch (Castro et al., 2009), stimulating lactate uptake in neurons (7) and inhibiting glucose consumption (8). The rise in the local O2 signal,

media/image35.png

Figure 23.

Neurochemical pathway involved in brain energetic metabolism during neural activation. Glut: glutamate; Gluc: glucose; Lac: lactate; Pyr: pyruvate; GLUT: glucose transporter; SVCT: AA transporter; MCT: lactate transporter.

during neural activation, might reflect an increase of rCBF (Bazzu et al., 2009; Calia et al., 2009). Immediately after the end of the physiological stimulation, AA current inverted its trend and slowly (10 min) returned to baseline levels. Oxygen and lactate currents continued to rise for 5 min after the end of the tail pinch then slowly decreased to baseline levels. The drop of glucose during neural activation was followed by its increase as result of astroglial glycogenolysis (Fillenz et al., 1999). Post-activation data are complex to interpret and suggest the return to normal (inactive) condition with glucose as energetic substrate for neurons. It is important to remember that AA is oxidized to DHAA by ROS and RNS in neurons (Serra et al., 2002). DHAA, secreted in the extracellular space, enters astrocyte through GLUT1 transporter and is back-reduced to AA (Hediger, 2002). A sustained increase of extracellular glucose could interfere with AA/DHAA cycle resulting in oxidative stress, AA loss and neuron damage.

These in-vivo results indicate that, during neural activation in physiological conditions, extracellular striatal changes of the above neurochemicals mainly reflect astrocyte response to glutamatergic stimulation. Further in-vivo pharmacological studies are necessary to confirm in-vitro evidences on bioenergetic substrates used by neurons in different experimental conditions.

10. Acknowledgements

The authors acknowledge the Italian distributors of Texas Instruments for free samples of integrated circuits. The research was supported by the Ministero dell’Istruzione, dell’Universita` e della Ricerca (MIUR, PRIN 2007 fund), University of Sassari (ex 60% fund) and Fondazione Banco di Sardegna.This work is dedicated to the beloved memory of Giovanna Pisanu who left us too early.

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