Introduction to a special issue on big data and pain

This special issue comprised 7 articles from leaders in the field that focus on “big pain data”, the large datasets and the associated methods for data analysis that are currently emerging in pain research. This collection of articles highlights the power and potential as well as points of caution that multi-disciplinary research utilising big data and their associated methods and interpretations present for pain research

Using stratified medicine to understand, diagnose, and treat neuropathic pain

Neuropathic pain (NeuP) is defined as pain arising from a lesion or disease of the somatosensory nervous system. NeuP is common, affecting approximately 6-8% of the general population and currently treatment is inadequate due to both poor drug efficacy and tolerability. Many different types of injury can cause neuropathic pain including genetic (e.g. SCN9A gain of function variants), metabolic (e.g. diabetic polyneuropathy), infective (e.g. HIV associated neuropathy, hepatitis), traumatic and toxic (e.g. chemotherapy induced neuropathy) causes. Such injurious events can impact on anatomically distinct regions of the somatosensory nervous system ranging from the terminals of nociceptive afferents (in small fiber neuropathy) to the thalamus (in post-stroke pain). Classification of neuropathic pain using etiology and location remains an important aspect of routine clinical practice; however, pain medicine is coming to the realization that we need more precision in this classification. The hope is that improved classification will lead to better understanding of risk, prognosis and optimal treatment of NeuP

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible “pain genes”. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics, protein-protein interaction network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a “pain score” per gene. The top ranked genes were validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of “pain” genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible “pain genes”. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics (eg. transcriptomics, proteomics, etc.), protein-protein interaction (PPI) network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a “pain score” per gene. The top ranked genes were then validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of “pain” genes. As such, a PPI network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research

Deep RNA-seq of male and female murine sensory neuron subtypes after nerve injury

Dorsal root ganglia (DRG) neurons have been well described for their role in driving both acute and pain. Although nerve injury is known to cause transcriptional dysregulation, how this differs across neuronal subtypes and the impact of sex is unclear. Here, we study the deep transcriptional profiles of multiple murine DRG populations in early and late pain states while considering sex. We have exploited currently available transgenics to label numerous subpopulations for fluorescent activated cell sorting (FACS) and subsequent transcriptomic analysis. Using bulk tissue samples, we are able to circumvent the issues of low transcript coverage and drop-outs seen with single cell datasets. This increases our power to detect novel and even subtle changes in gene expression within neuronal subtypes and discuss sexual dimorphism at the neuronal subtype level. We have curated this resource into an accessible database for other researchers (https://livedataoxford.shinyapps.io/drg-directory/). We see both stereotyped and unique subtype signatures in injured states after nerve injury at both an early and late timepoint. While all populations contribute to a general injury signature, subtype enrichment changes can also be seen. Within populations, there is not a strong intersection of sex and injury, but previously unknown sex differences in naïve states-particularly in Aβ-RA + Aδ-LTMRs - still contribute to differences in injured neurons

Deep RNA-seq of male and female murine sensory neuron subtypes after nerve injury

Dorsal root ganglia (DRG) neurons have been well described for their role in driving both acute and chronic pain. Although nerve injury is known to cause transcriptional dysregulation, how this differs across neuronal subtypes and the impact of sex is unclear. Here, we study the deep transcriptional profiles of multiple murine DRG populations in early and late pain states while considering sex. We have exploited currently available transgenics to label numerous subpopulations for fluorescent-activated cell sorting and subsequent transcriptomic analysis. Using bulk tissue samples, we are able to circumvent the issues of low transcript coverage and drop-outs seen with single-cell data sets. This increases our power to detect novel and even subtle changes in gene expression within neuronal subtypes and discuss sexual dimorphism at the neuronal subtype level. We have curated this resource into an accessible database for other researchers (https://livedataoxford.shinyapps.io/drg-directory/). We see both stereotyped and unique subtype signatures in injured states after nerve injury at both an early and late timepoint. Although all populations contribute to a general injury signature, subtype enrichment changes can also be seen. Within populations, there is not a strong intersection of sex and injury, but previously unknown sex differences in naïve states—particularly in Aβ-RA + Aδ-low threshold mechanoreceptors—still contribute to differences in injured neurons

Reliability of a clinical sensory test battery in patients with spine‐related leg and arm pain

Background: The current standard to evaluate the presence of somatosensory dysfunctions is quantitative sensory testing, but its clinical utility remains limited. Low-cost and time-efficient clinical sensory testing (CST) batteries have thus been developed. Recent studies show moderate to substantial reliability in populations with neuropathic pain. This study evaluates the inter- and intra-tester reliability of people with spine-related leg and arm pain, representing mixed pain mechanisms. Methods: Fifty-three patients with spine-related leg (n = 41) and arm pain (n = 12) attended three CST sessions. The CST battery consisted of eleven tests, determining loss and gain of sensory nerve function. CST was performed by the same investigator twice and by an additional investigator to determine inter- and intra-tester reliability. Fleiss' (inter-tester) and Cohen's (intra-tester) kappa were calculated for dichotomized and intraclass correlation coefficients (ICC) for continuous outcomes. Results: Fleiss' kappa varied among modalities from fair to substantial (κ = 0.23–0.66). Cold, warm, and vibration detection thresholds and cold and pressure pain thresholds reached kappa >0.4 (moderate to substantial reliability). Cohen's kappa ranged from moderate to substantial (κ = 0.45–0.66). The reliability of the windup ratio was poor (ICC <0.18). Conclusion: CST modalities with moderate to substantial inter-tester reliability could be of benefit as a screening tool. The moderate to substantial intra-tester reliability for all sensory modalities (except windup ratio) supports their potential use in clinical practice and research to monitor somatosensory changes over time in patients with spine-related limb pain of mixed pain mechanisms. Significance: We already know that most modalities of clinical sensory test (CST) batteries achieve moderate to substantial inter- and intra-tester reliability in populations with neuropathic pain. This study evaluates the reliability of a CST battery in populations with mixed pain mechanisms. We found inter-tester reliability varied from poor to substantial for sensory modalities, questioning the value of some CST modalities. The CST battery showed moderate to substantial intra-tester reliability, suggesting its usefulness to monitor sensory changes over time in this cohort

Cutaneous expression of growth-associated protein 43 is not a compelling marker for human nerve regeneration in carpal tunnel syndrome.

Growth-associated protein 43 (GAP-43) has long been used as a marker for nerve regeneration following nerve injury, with numerous in vitro and animal studies showing its upregulation in regenerating neurons. In humans, expression of GAP-43 has predominantly been examined in skin biopsies from patients with peripheral neuropathies; with several studies showing a reduction in GAP-43 immunoreactive cutaneous nerve fibres. However, it remains elusive whether cutaneous GAP-43 is a valid marker for human nerve regeneration. Here, we present a cohort of 22 patients with electrodiagnostically confirmed carpal tunnel syndrome (CTS), used as a model system for focal nerve injury and neural regeneration after decompression surgery. We evaluate GAP-43 immunoreactivity and RNA expression levels in finger skin biopsies taken before and 6 months after surgery, relative to healthy controls. We further classify patients as 'regenerators' or 'non-regenerators' based on post-surgical epidermal re-innervation. We demonstrate that patients with CTS have lower GAP-43 positive intra-epidermal nerve fibre density (IENFD) before surgery than healthy controls. However, this difference disappears when normalising for total IENFD. Of note, we found surgery did not change GAP-43 expression in IENF, with no differences both in patients who were classified as regenerators and non-regenerators. We also did not identify pre-post surgical differences in cutaneous GAP-43 gene expression or associations with regeneration. These findings suggest cutaneous GAP-43 may not be a compelling marker for nerve regeneration in humans

Mechanisms of neurodynamic treatments (MONET): a protocol for a mechanistic, randomised, single-blind controlled trial in patients with carpal tunnel syndrome

Background: Physiotherapeutic management is the first-line intervention for patients with entrapment neuropathies such as carpal tunnel syndrome (CTS). As part of physiotherapy, neurodynamic interventions are often used to treat people with peripheral nerve involvement, but their mechanisms of action are yet to be fully understood. The MONET (mechanisms of neurodynamic treatment) study aims to investigate the mechanisms of action of neurodynamic exercise intervention on nerve structure, and function. Methods: This mechanistic, randomised, single-blind, controlled trial will include 78 people with electrodiagnostically confirmed mild or moderate CTS and 30 healthy participants (N = 108). Patients will be randomly assigned into (1) a 6-week progressive home-based neurodynamic exercise intervention (n = 26), (2) a steroid injection (= 26), or (3) advice (n = 26) group. The primary outcome measure is fractional anisotropy of the median nerve at the wrist using advanced magnetic resonance neuroimaging. Secondary outcome measures include neuroimaging markers at the wrist, quantitative sensory testing, electrodiagnostics, and patient reported outcome measures. Exploratory outcomes include neuroimaging markers at the cervical spine, inflammatory and axonal integrity markers in serial blood samples and biopsies of median nerve innervated skin. We will evaluate outcome measures at baseline and at the end of the 6-week intervention period. We will repeat questionnaires at 6-months. Two-way repeated measures ANCOVAs, followed by posthoc testing will be performed to identify differences in outcome measures among groups and over time. Discussion: This study will advance our understanding of the mechanisms of action underpinning neurodynamic exercises, which will ultimately help clinicians to better target these treatments to those patients who may benefit from them. The inclusion of a positive control group (steroid injection) and a negative control group (advice) will strengthen the interpretation of our results. Trial registration: NCT05859412, 20/4/2023

Utilising clinical parameters to improve the selection of nerve biopsy candidates

Background Peripheral nerve biopsy is a valuable final diagnostic tool; however, histopathological results can be non-diagnostic. Aims We aim to identify quality improvement measures by evaluating the pre-biopsy assessment and diagnostic yield of specific histopathological diagnosis. Methods This was a retrospective study based on 10 years of experience with peripheral nerve biopsies at a single centre. Clinical data were obtained regarding pre-biopsy history, examination, serum and cerebrospinal fluid (CSF) investigations, neurophysiology and peripheral nerve imaging. Based upon a histopathological outcome, patients were grouped into vasculitis, granulomatous and infiltrative (diagnostic) group, or a comparison group of non-specific axonal neuropathy and normal (non-specific/normal) group. Results From a cohort of 64 patients, 21 (32.8%) were included in the diagnostic group and 30 (46.9%) in the non-specific/normal group. Clinical parameters associated with the diagnostic group were shorter history (mean 10.2 months vs 38.1), stepwise progression (81% vs 20%), neuropathic pain (85.7% vs 56.7%), vasculitic rash (23.8% vs 0%), mononeuritis multiplex (57.1% vs 10%), asymmetry (90.5% vs 60%), raised white cell count (47.6% vs 16.7%), myeloperoxidase antibody (19.1% vs 0%) and abnormal peripheral nerve imaging (33.3% vs 10%). Conclusion Selection of patients undergoing nerve biopsy requires careful consideration of clinical parameters, including peripheral nerve imaging. Several quality improvement measures are proposed to improve yield of clinically actionable information from nerve biopsy