Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
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(Published version)
Date
2021
Authors
Gosnell, M.E.
Staikopoulos, V.
Anwer, A.G.
Mahbub, S.B.
Hutchinson, M.R.
Mustafa, S.
Goldys, E.M.
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Journal article
Citation
Neurobiology of Disease, 2021; 160:105528-1-105528-11
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Martin E. Gosnell, Vasiliki Staikopoulos, Ayad G. Anwer, Saabah B. Mahbub, Mark R. Hutchinson, Sanam Mustafa, Ewa M. Goldys
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Abstract
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.
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Available online 7 October 2021
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© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).