PT  - JOURNAL ARTICLE
AU  - Manon Bohic
AU  - Luke A. Pattison
AU  - Z. Anissa Jhumka
AU  - Heather Rossi
AU  - Joshua K. Thackray
AU  - Matthew Ricci
AU  - William Foster
AU  - Justin Arnold
AU  - Nahom Mossazghi
AU  - Eric A. Yttri
AU  - Max A. Tischfield
AU  - Ewan St. John Smith
AU  - Ishmail Abdus-Saboor
AU  - Victoria E. Abraira
TI  - Behavioral and nociceptor states of inflammatory pain across timescales in 2D and 3D
AID  - 10.1101/2021.06.16.448689
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.16.448689
4099  - http://biorxiv.org/content/early/2021/06/17/2021.06.16.448689.short
4100  - http://biorxiv.org/content/early/2021/06/17/2021.06.16.448689.full
AB  - Inflammatory pain represents a complex state involving sensitization of peripheral and central neuronal signalling. Resolving this high-dimensional interplay at the cellular and behavioral level is key to effective therapeutic development. Here, using the carrageenan model of local inflammation of the hind paw, we determine how carrageenan alters both the physiological state of sensory neurons and behaviors at rapid and continuous timescales. We identify higher excitability of sensory neurons innervating the site of inflammation by profiling their physiological state at different time points. To identify millisecond-resolved sensory-reflexive signatures evoked by inflammatory pain, we used a combination of supervised and unsupervised algorithms, and uncovered abnormal paw placement as a defining behavioral feature. For long-term detection and characterization of spontaneous behavioral signatures representative of affective-motivational pain states, we use computer vision coupled to unsupervised machine learning in an open arena. Using the non-steroidal anti-inflammatory drug meloxicam to characterize analgesic states during rapid and ongoing timescales, we identify a return to pre-injury states of some sensory-reflexive behaviors, but by and large, many spontaneous, affective-motivational pain behaviors remain unaffected. Taken together, this comprehensive exploration across cellular and behavioral dimensions reveals peripheral versus centrally mediated pain signatures that define the inflamed state, providing a framework for scaling the pain experience at unprecedented resolution.HighlightsAutomated identification of millisecond-resolved behaviors that separate inflammation-induced mechanical allodynia versus hyperalgesia.Sensory neurons innervating the inflamed paw show heightened excitability and nociceptive channel activity.Automated identification of spontaneous inflammation-induced behaviors uncovered by computer vision/machine learning during exploratory behavior in an open arena.Reduction in evoked pain signatures by an anti-inflammatory drug does not equal return to pre-injury state.Competing Interest StatementThe authors have declared no competing interest.