Gosnell, M.E.Staikopoulos, V.Anwer, A.G.Mahbub, S.B.Hutchinson, M.R.Mustafa, S.Goldys, E.M.2021-11-082021-11-082021Neurobiology of Disease, 2021; 160:105528-1-105528-110969-99611095-953Xhttps://hdl.handle.net/2440/133010Available online 7 October 2021Our 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.en© 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/).Chronic pain; Autofluorescence imaging; Spinal cord; Allodynia; Nerve injury; Deep learning; Chronic constriction injury (CCI)Journal article10.1016/j.nbd.2021.1055282021-11-08591277Hutchinson, M.R. [0000-0003-2154-5950]Mustafa, S. [0000-0002-8677-5151]