Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
Our understanding of chronic pain and the underlying molecular mechanisms remains limited due to a lack of tools to identify the complex phenomena responsible for exaggerated pain behaviours. Furthermore, currently there is no objective measure of pain with current assessment relying on patient self...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Neurobiology of Disease |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0969996121002771 |
| _version_ | 1828144841463169024 |
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| author | Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys |
| author_facet | Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys |
| author_sort | Martin E. Gosnell |
| collection | DOAJ |
| description | Our understanding of chronic pain and the underlying molecular mechanisms remains limited due to a lack of tools to identify the complex phenomena responsible for exaggerated pain behaviours. Furthermore, currently there is no objective measure of pain with current assessment relying on patient self-scoring. Here, we applied a fully biologically unsupervised technique of hyperspectral autofluorescence imaging to identify a complex signature associated with chronic constriction nerve injury known to cause allodynia. The analysis was carried out using deep learning/artificial intelligence methods. The central element was a deep learning autoencoder we developed to condense the hyperspectral channel images into a four- colour image, such that spinal cord tissue based on nerve injury status could be differentiated from control tissue.This study provides the first validation of hyperspectral imaging as a tool to differentiate tissues from nerve injured vs non-injured mice. The auto-fluorescent signals associated with nerve injury were not diffuse throughout the tissue but formed specific microscopic size regions. Furthermore, we identified a unique fluorescent signal that could differentiate spinal cord tissue isolated from nerve injured male and female animals. The identification of a specific global autofluorescence fingerprint associated with nerve injury and resultant neuropathic pain opens up the exciting opportunity to develop a diagnostic tool for identifying novel contributors to pain in individuals. |
| first_indexed | 2024-04-11T20:23:11Z |
| format | Article |
| id | doaj.art-d68ce565fc8f42179d2a360e47ef3e69 |
| institution | Directory Open Access Journal |
| issn | 1095-953X |
| language | English |
| last_indexed | 2024-04-11T20:23:11Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neurobiology of Disease |
| spelling | doaj.art-d68ce565fc8f42179d2a360e47ef3e692022-12-22T04:04:46ZengElsevierNeurobiology of Disease1095-953X2021-12-01160105528Autofluorescent imprint of chronic constriction nerve injury identified by deep learningMartin E. Gosnell0Vasiliki Staikopoulos1Ayad G. Anwer2Saabah B. Mahbub3Mark R. Hutchinson4Sanam Mustafa5Ewa M. Goldys6Quantitative Pty Ltd, 118 Great Western Highway, Mount Victoria, NSW 2786, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, University of Adelaide, Adelaide 5005, Australia; Adelaide Medical School, University of Adelaide, Adelaide 5005, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, UNSW Sydney, NSW 2052, Australia; Graduate School of Biomedical Engineering, UNSW Sydney, NSW 2052, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, UNSW Sydney, NSW 2052, Australia; Graduate School of Biomedical Engineering, UNSW Sydney, NSW 2052, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, University of Adelaide, Adelaide 5005, Australia; Adelaide Medical School, University of Adelaide, Adelaide 5005, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, University of Adelaide, Adelaide 5005, Australia; Adelaide Medical School, University of Adelaide, Adelaide 5005, AustraliaARC Centre of Excellence for Nanoscale Biophotonics, UNSW Sydney, NSW 2052, Australia; Graduate School of Biomedical Engineering, UNSW Sydney, NSW 2052, Australia; Corresponding author at: ARC Centre of Excellence for Nanoscale Biophotonics, UNSW Sydney, NSW 2052, Australia.Our understanding of chronic pain and the underlying molecular mechanisms remains limited due to a lack of tools to identify the complex phenomena responsible for exaggerated pain behaviours. Furthermore, currently there is no objective measure of pain with current assessment relying on patient self-scoring. Here, we applied a fully biologically unsupervised technique of hyperspectral autofluorescence imaging to identify a complex signature associated with chronic constriction nerve injury known to cause allodynia. The analysis was carried out using deep learning/artificial intelligence methods. The central element was a deep learning autoencoder we developed to condense the hyperspectral channel images into a four- colour image, such that spinal cord tissue based on nerve injury status could be differentiated from control tissue.This study provides the first validation of hyperspectral imaging as a tool to differentiate tissues from nerve injured vs non-injured mice. The auto-fluorescent signals associated with nerve injury were not diffuse throughout the tissue but formed specific microscopic size regions. Furthermore, we identified a unique fluorescent signal that could differentiate spinal cord tissue isolated from nerve injured male and female animals. The identification of a specific global autofluorescence fingerprint associated with nerve injury and resultant neuropathic pain opens up the exciting opportunity to develop a diagnostic tool for identifying novel contributors to pain in individuals.http://www.sciencedirect.com/science/article/pii/S0969996121002771Chronic painAutofluorescence imagingSpinal cordAllodyniaNerve injuryDeep learning |
| spellingShingle | Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys Autofluorescent imprint of chronic constriction nerve injury identified by deep learning Neurobiology of Disease Chronic pain Autofluorescence imaging Spinal cord Allodynia Nerve injury Deep learning |
| title | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| title_full | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| title_fullStr | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| title_full_unstemmed | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| title_short | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| title_sort | autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
| topic | Chronic pain Autofluorescence imaging Spinal cord Allodynia Nerve injury Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S0969996121002771 |
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