Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin
Abstract Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79271-9 |
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| author | Yuying Jin Julian Brennecke Annemarie Sodmann Robert Blum Claudia Sommer |
| author_facet | Yuying Jin Julian Brennecke Annemarie Sodmann Robert Blum Claudia Sommer |
| author_sort | Yuying Jin |
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| description | Abstract Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach. |
| format | Article |
| id | doaj-art-ad3cd3e7373a4f8f9ced564c2cb09401 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ad3cd3e7373a4f8f9ced564c2cb094012024-11-24T12:26:20ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-79271-9Antibody selection and automated quantification of TRPV1 immunofluorescence on human skinYuying Jin0Julian Brennecke1Annemarie Sodmann2Robert Blum3Claudia Sommer4Department of Neurology, University Hospital of WürzburgDepartment of Neurology, University Hospital of WürzburgDepartment of Neurology, University Hospital of WürzburgDepartment of Neurology, University Hospital of WürzburgDepartment of Neurology, University Hospital of WürzburgAbstract Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.https://doi.org/10.1038/s41598-024-79271-9Transient receptor potential vanilloid 1 (TRPV1)ImmunofluorescenceDeep learningMachine learningBioimage analysis |
| spellingShingle | Yuying Jin Julian Brennecke Annemarie Sodmann Robert Blum Claudia Sommer Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin Scientific Reports Transient receptor potential vanilloid 1 (TRPV1) Immunofluorescence Deep learning Machine learning Bioimage analysis |
| title | Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin |
| title_full | Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin |
| title_fullStr | Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin |
| title_full_unstemmed | Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin |
| title_short | Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin |
| title_sort | antibody selection and automated quantification of trpv1 immunofluorescence on human skin |
| topic | Transient receptor potential vanilloid 1 (TRPV1) Immunofluorescence Deep learning Machine learning Bioimage analysis |
| url | https://doi.org/10.1038/s41598-024-79271-9 |
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