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A high-content platform for physiological profiling and unbiased classification of individual neurons
High-throughput physiological assays lose single-cell resolution, precluding subtype-specific analyses of activation mechanism and drug effects. We demonstrate APPOINT (automated physiological phenotyping of individual neuronal types), a physiological assay platform combining calcium imaging, roboti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312640/ https://www.ncbi.nlm.nih.gov/pubmed/34318289 http://dx.doi.org/10.1016/j.crmeth.2021.100004 |
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author | DuBreuil, Daniel M. Chiang, Brenda M. Zhu, Kevin Lai, Xiaofan Flynn, Patrick Sapir, Yechiam Wainger, Brian J. |
author_facet | DuBreuil, Daniel M. Chiang, Brenda M. Zhu, Kevin Lai, Xiaofan Flynn, Patrick Sapir, Yechiam Wainger, Brian J. |
author_sort | DuBreuil, Daniel M. |
collection | PubMed |
description | High-throughput physiological assays lose single-cell resolution, precluding subtype-specific analyses of activation mechanism and drug effects. We demonstrate APPOINT (automated physiological phenotyping of individual neuronal types), a physiological assay platform combining calcium imaging, robotic liquid handling, and automated analysis to generate physiological activation profiles of single neurons at large scale. Using unbiased techniques, we quantify responses to sequential stimuli, enabling subgroup identification by physiology and probing of distinct mechanisms of neuronal activation within subgroups. Using APPOINT, we quantify primary sensory neuron activation by metabotropic receptor agonists and identify potential contributors to pain signaling. We expand the role of neuroimmune interactions by showing that human serum directly activates sensory neurons, elucidating a new potential pain mechanism. Finally, we apply APPOINT to develop a high-throughput, all-optical approach for quantification of activation threshold and pharmacologically validate contributions of ion channel families to optical activation. |
format | Online Article Text |
id | pubmed-8312640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83126402021-07-26 A high-content platform for physiological profiling and unbiased classification of individual neurons DuBreuil, Daniel M. Chiang, Brenda M. Zhu, Kevin Lai, Xiaofan Flynn, Patrick Sapir, Yechiam Wainger, Brian J. Cell Rep Methods Article High-throughput physiological assays lose single-cell resolution, precluding subtype-specific analyses of activation mechanism and drug effects. We demonstrate APPOINT (automated physiological phenotyping of individual neuronal types), a physiological assay platform combining calcium imaging, robotic liquid handling, and automated analysis to generate physiological activation profiles of single neurons at large scale. Using unbiased techniques, we quantify responses to sequential stimuli, enabling subgroup identification by physiology and probing of distinct mechanisms of neuronal activation within subgroups. Using APPOINT, we quantify primary sensory neuron activation by metabotropic receptor agonists and identify potential contributors to pain signaling. We expand the role of neuroimmune interactions by showing that human serum directly activates sensory neurons, elucidating a new potential pain mechanism. Finally, we apply APPOINT to develop a high-throughput, all-optical approach for quantification of activation threshold and pharmacologically validate contributions of ion channel families to optical activation. Elsevier 2021-04-02 /pmc/articles/PMC8312640/ /pubmed/34318289 http://dx.doi.org/10.1016/j.crmeth.2021.100004 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article DuBreuil, Daniel M. Chiang, Brenda M. Zhu, Kevin Lai, Xiaofan Flynn, Patrick Sapir, Yechiam Wainger, Brian J. A high-content platform for physiological profiling and unbiased classification of individual neurons |
title | A high-content platform for physiological profiling and unbiased classification of individual neurons |
title_full | A high-content platform for physiological profiling and unbiased classification of individual neurons |
title_fullStr | A high-content platform for physiological profiling and unbiased classification of individual neurons |
title_full_unstemmed | A high-content platform for physiological profiling and unbiased classification of individual neurons |
title_short | A high-content platform for physiological profiling and unbiased classification of individual neurons |
title_sort | high-content platform for physiological profiling and unbiased classification of individual neurons |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312640/ https://www.ncbi.nlm.nih.gov/pubmed/34318289 http://dx.doi.org/10.1016/j.crmeth.2021.100004 |
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