Justin A. Johnson

Construction of Training Sets for Valid Calibration of in Vivo Cyclic Voltammetric Data by Principal Component Analysis

Principal component regression, a multivariate calibration technique, is an invaluable tool for the analysis of voltammetric data collected in vivo with acutely implanted microelectrodes. This method utilizes training sets to separate cyclic voltammograms into contributions from multiple electroactive species. The introduction of chronically implanted microelectrodes permits longitudinal measurements at the same electrode and brain location over multiple recordings. The reliability of these measurements depends on a consistent calibration methodology. One published approach has been the use of training sets built with data from separate electrodes and animals to evaluate neurochemical signals in multiple subjects. Alternatively, responses to unpredicted rewards have been used to generate calibration data. This study addresses these approaches using voltammetric data from three different experiments in freely moving rats obtained with acutely implanted microelectrodes. The findings demonstrate critical issues arising from the misuse of principal component regression that result in significant underestimates of concentrations and improper statistical model validation that, in turn, can lead to inaccurate data interpretation. Therefore, the calibration methodology for chronically implanted microelectrodes needs to be revisited and improved before measurements can be considered reliable.

Hitchhiker’s Guide to Voltammetry: Acute and Chronic Electrodes for in Vivo Fast-Scan Cyclic Voltammetry

Fast-scan cyclic voltammetry (FSCV) has been used for over 20 years to study rapid neurotransmission in awake and behaving animals. These experiments were first carried out with carbon-fiber microelectrodes (CFMs) encased in borosilicate glass, which can be inserted into the brain through micromanipulators and guide cannulas. More recently, chronically implantable CFMs constructed with small diameter fused-silica have been introduced. These electrodes can be affixed in the brain with minimal tissue response, which permits longitudinal measurements of neurotransmission in single recording locations during behavior. Both electrode designs have been used to make novel discoveries in the fields of neurobiology, behavioral neuroscience, and psychopharmacology. The purpose of this Review is to address important considerations for the use of FSCV to study neurotransmitters in awake and behaving animals, with a focus on measurements of striatal dopamine. Common issues concerning experimental design, data collection, and calibration are addressed. When necessary, differences between the two methodologies (acute vs chronic recordings) are discussed. The topics raised in this Review are particularly important as the field moves beyond dopamine toward new neurochemicals and brain regions.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Spectrophotometric titration of bimetallic metal cation binding in polyamido(amine) dendrimer templates.

Spectrophotometric titration and a binding isotherm were used to accurately assess the loading capacity of generation four polyamido(amine) (PAMAM) dendrimer templates with terminal alcohol groups (G4-OH). Preparation of bimetallic G4-OH dendrimer-encapsulated metal nanoclusters (DENs) necessitates knowledge of the precise metal-ion binding capacity. The binding of metal ions such as Pt(2+) and Pd(2+) has proven difficult to assess via UV-vis spectroscopy because the absorbance shifts associated with metal-ion binding within the dendrimer template are masked by the absorbance of the PAMAM dendrimer itself. In contrast, the binding of Cu(2+) to G4-OH PAMAM dendrimer results in a strong, distinct absorption band at 300 nm, making UV-vis spectrophotometric titration with copper straightforward. Here we use copper binding as a means to assess the number of binding sites remaining within the PAMAM G4-OH dendrimer after the complexation of a specified molar excess of Pd(2+) or Pt(2+). In addition, we use a binding isotherm to mathematically estimate the loading capacity of the dendrimer in each case. The loading capacities for M(2+) in the G4-OH dendrimer were found to be ∼16 for copper alone, ∼21 for copper combined with palladium, and ∼25 for copper combined with platinum.

Removal of Differential Capacitive Interferences in Fast-Scan Cyclic Voltammetry

Due to its high spatiotemporal resolution, fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes enables the localized in vivo monitoring of subsecond fluctuations in electroactive neurotransmitter concentrations. In practice, resolution of the analytical signal relies on digital background subtraction for removal of the large current due to charging of the electrical double layer as well as surface faradaic reactions. However, fluctuations in this background current often occur with changes in the electrode state or ionic environment, leading to nonspecific contributions to the FSCV data that confound data analysis. Here, we both explore the origin of such shifts seen with local changes in cations and develop a model to account for their shape. Further, we describe a convolution-based method for removal of the differential capacitive contributions to the FSCV current. The method relies on the use of a small-amplitude pulse made prior to the FSCV sweep that probes the impedance of the system. To predict the nonfaradaic current response to the voltammetric sweep, the step current response is differentiated to provide an estimate of the system’s impulse response function and is used to convolute the applied waveform. The generated prediction is then subtracted from the observed current to the voltammetric sweep, removing artifacts associated with electrode impedance changes. The technique is demonstrated to remove select contributions from capacitive characteristics changes of the electrode both in vitro (i.e., in flow-injection analysis) and in vivo (i.e., during a spreading depression event in an anesthetized rat).

Dual action antimicrobial surfaces via combined nitric oxide and silver release

Recent research has demonstrated that silver sulfadiazine and small molecule nitric oxide (NO) donors kill a number of bacterial species synergistically in solution-based assays. Herein, we report on multilayered silica-based xerogels that release both NO and silver. Release of each agent was achieved by exposing amine-modified xerogels to high pressures of NO, and doping silver nitrate (AgNO3) into an alkyl-silane xerogel. Total achievable releases were 3.5 μmol cm(-2) and 1.7 ppm for NO and Ag+, respectively, with release of each agent controlled independent of the other. The NO/Ag+-releasing coating reduced bacterial adhesion and exhibited greater-than-additive killing against both Pseudomonas aeruginosa and Staphylococcus aureus. In contrast, cytotoxicity assays against L929 fibroblasts suggest that the combination does not cause greater-than-additive killing to mammalian cells. Such materials may prove useful in the design of biomedical devices prone to infection such as bone and surgical screws.

Medullary Norepinephrine Projections Release Norepinephrine into the Contralateral Bed Nucleus of the Stria Terminalis

Central norepinephrine signaling influences a wide range of behavioral and physiological processes, and the ventral bed nucleus of the stria terminalis (vBNST) receives some of the densest norepinephrine innervation in the brain. Previous work describes norepinephrine neurons as projecting primarily unilaterally; however, recent evidence for cross-hemispheric catecholamine signaling challenges this idea. Here, we use fast-scan cyclic voltammetry and retrograde tracing to characterize cross-hemispheric norepinephrine signaling in the vBNST. We delivered stimulations to noradrenergic pathways originating in the A1/A2 and locus coeruleus and found hemispherically equivalent norepinephrine release in the vBNST regardless of stimulated hemisphere. Unilateral retrograde tracing revealed that medullary, but not locus coeruleus norepinephrine neurons send cross-hemispheric projections to the vBNST. Further characterization with pharmacological lesions revealed that stimulations of the locus coeruleus and its axon bundles likely elicit vBNST norepinephrine release through indirect activation. These experiments are the first to demonstrate contralateral norepinephrine release and establish that medullary, but not coerulean neurons are responsible for norepinephrine release in the vBNST.

Measurement of Basal Neurotransmitter Levels Using Convolution-Based Nonfaradaic Current Removal

Fast-scan cyclic voltammetry permits robust subsecond measurements of in vivo neurotransmitter dynamics, resulting in its established use in elucidating these species’ roles in the actions of behaving animals. However, the technique’s limitations, namely the need for digital background subtraction for analytical signal resolution, have restricted the information obtainable largely to that about phasic neurotransmitter release on the second-to-minute time scale. The study of basal levels of neurotransmitters and their dynamics requires a means of isolating the portion of the background current arising from neurotransmitter redox reactions. Previously, we reported on the use of a convolution-based method for prediction of the resistive-capacitive portion of the carbon-fiber microelectrode background signal, to improve the information content of background-subtracted data. Here we evaluated this approach for direct analytical signal isolation. First, protocol modifications (i.e., applied waveform and carbon-fiber type) were optimized to permit simplification of the interfering background current to components that are convolution-predictable. It was found that the use of holding potentials of at least 0.0 V, as well as the use of pitch-based carbon fibers, improved the agreement between convolution predictions and the observed background. Subsequently, it was shown that measurements of basal dopamine concentrations are possible with careful control of the electrode state. Successful use of this approach for measurement of in vivo basal dopamine levels is demonstrated, suggesting the approach may serve as a useful tool in expanding the capabilities of fast-scan cyclic voltammetry.

Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data

The use of multivariate analysis techniques, such as principal component analysis–inverse least-squares (PCA–ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA–ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution–alternating least-squares (MCR–ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA–ILS, which relies on explicit user definition of component number and profiles, MCR–ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR–ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR–ALS models are characterized. It is shown, through use of previously reported techniques, that MCR–ALS can produce similar results to PCA–ILS and may serve as a useful supplement or replacement to PCA–ILS for signal isolation from FSCV data.