Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry.

This perspective encompasses a focused review of the literature leading to a tipping point in electroanalytical chemistry. We tie together the threads of a "revolution" quietly in the making for years through the work of many authors. Long-held misconceptions about the use of background subtraction in fast voltammetry are addressed. We lay out future advantages that accompany background-inclusive voltammetry, particularly when paired with modern machine-learning algorithms for data analysis.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression.

Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.

Correction to: Multiple Ion Transition Summation of Isotopologues for Improved Mass Spectrometric Detection of N-Acetyl-S-(1,2-Dichlorovinyl)-L-Cysteine.

The original article has been corrected to include the missing chemical structure in Fig. 1.

Multiple Ion Transition Summation of Isotopologues for Improved Mass Spectrometric Detection of N-Acetyl-S-(1,2-dichlorovinyl)-L-cysteine.

Multiple ion transition summation of isotopologues (MITSI) is an adaptable and easy-to-implement methodology for improving analytical sensitivity, especially for halogenated compounds and otherwise abundant isotopologues. This novel application of signal summing was applied to measure and quantitate the two most abundant ion transitions of two isotopologues of N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (1DCV), a urinary metabolite of trichloroethylene (TCE). Because 1DCV is dichlorinated, only approximately half of the total potential signal is quantifiable when the monoisotopic ion transition (i.e., m/z 256 → 127 for 35Cl2) is monitored. By summing the intensity of a separate and high-abundance 1DCV isotopologue ion transition (i.e., m/z 258 → 129 to include 35Cl and 37Cl), overall signal intensity increased by over 70%. This summation technique improved the analytical sensitivity and limit of detection (LOD) by factors of 2.3 and 2.9, respectively, compared to monitoring the two transitions separately, without summation. Separation and detection were performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in negative-ion mode with scheduled selected reaction monitoring. This approach was verified for accuracy and precision using two quality control materials. In addition, we derived a modified signal summation equation to calculate predicted signal enhancements specific to the MITSI approach. Graphical Abstract .