Cargando…
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 p...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551120/ https://www.ncbi.nlm.nih.gov/pubmed/34686897 http://dx.doi.org/10.1007/s00216-021-03665-1 |
_version_ | 1784591085590282240 |
---|---|
author | Movassaghi, Cameron S. Perrotta, Katie A. Yang, Hongyan Iyer, Rahul Cheng, Xinyi Dagher, Merel Fillol, Miguel Alcañiz Andrews, Anne M. |
author_facet | Movassaghi, Cameron S. Perrotta, Katie A. Yang, Hongyan Iyer, Rahul Cheng, Xinyi Dagher, Merel Fillol, Miguel Alcañiz Andrews, Anne M. |
author_sort | Movassaghi, Cameron S. |
collection | PubMed |
description | 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. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00216-021-03665-1. |
format | Online Article Text |
id | pubmed-8551120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85511202021-10-29 Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression Movassaghi, Cameron S. Perrotta, Katie A. Yang, Hongyan Iyer, Rahul Cheng, Xinyi Dagher, Merel Fillol, Miguel Alcañiz Andrews, Anne M. Anal Bioanal Chem Research Paper 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. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00216-021-03665-1. Springer Berlin Heidelberg 2021-10-23 2021 /pmc/articles/PMC8551120/ /pubmed/34686897 http://dx.doi.org/10.1007/s00216-021-03665-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Paper Movassaghi, Cameron S. Perrotta, Katie A. Yang, Hongyan Iyer, Rahul Cheng, Xinyi Dagher, Merel Fillol, Miguel Alcañiz Andrews, Anne M. Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title | Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title_full | Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title_fullStr | Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title_full_unstemmed | Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title_short | Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
title_sort | simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551120/ https://www.ncbi.nlm.nih.gov/pubmed/34686897 http://dx.doi.org/10.1007/s00216-021-03665-1 |
work_keys_str_mv | AT movassaghicamerons simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT perrottakatiea simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT yanghongyan simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT iyerrahul simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT chengxinyi simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT daghermerel simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT fillolmiguelalcaniz simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression AT andrewsannem simultaneousserotoninanddopaminemonitoringacrosstimescalesbyrapidpulsevoltammetrywithpartialleastsquaresregression |