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date: 19 February 2019

The Proteomics and Metabolomics of Pain—Opportunities for Systems Medicine

Abstract and Keywords

This article critically discusses the opportunities and challenges of proteomics and metabolomics in the context of pain research and clinical practice. Painful pathologies are extremely complex and often inadequately managed. In order to significantly improve therapeutic options of pain syndromes, a system-wide analysis of the underlying mechanisms is fundamental. To this end, the article highlights the potential of firmly integrating proteomics and metabolomics into the pain researcher’s toolbox. The article introduces technological aspects of mass spectrometry and also associated stumbling blocks for its standardization in clinical research. Lastly, it outlines the path towards personalized systems pain medicine via the definition of pain phenomes—from multilayered molecular data to phenotypical profiles and integrated analysis of disease networks.

Keywords: Proteomics, metabolomics, pain phenomes, mass spectrometry, standardization, personalized medicine, systems medicine, disease networks


Pain, particularly when chronic, is a complex maladaptive disease with high incidence and is a major cause of long-term disability (Breivik, Collett, Ventafridda, Cohen, & Gallacher, 2006; Price & Gold, 2017). Despite immense research efforts, our knowledge of the mechanistic underpinnings of chronic pain syndromes remains unsatisfactory (Grosser, Woolf, & FitzGerald, 2017; Price & Gold, 2017; Sommer, 2016; Vardeh, Mannion, & Woolf, 2016). Consequently, today’s pain medications are best described as symptom-based, exhibit only limited efficacy, and are often accompanied by strong side effects. Given these limitations, the American Pain Society identified the following goals in their “Pain Research Agenda” (Gereau et al., 2014): the development of novel pain therapies with acceptable side effects; progress in prevention, diagnosis, and management of chronic pain; as well as optimization of the use of available treatments. The success in reaching these goals will crucially depend on our understanding of the underlying mechanisms governing distinct pain syndromes. To this end, a reductionist approach has been largely followed so far; that is, the individual assessment of genes or molecules in the context of pain. Ideally, however, one would like to identify the system-wide compendium of molecules and pathways that are specifically modulated during distinct chronic pain syndromes. Among those, so-called disease signatures of chronic pain (Borsook, Becerra, & Hargreaves, 2011; Costigan, 2012; Price & Gold, 2017) could be defined and ultimately serve as biomarkers. However, disease signatures of chronic pain remain largely elusive to date. New avenues could be opened by the firm integration of proteomics (i.e., the compendium of proteins in a cell, tissue, or organism) and metabolomics (i.e., the collection of small molecules produced by a cell, tissue, or organism) into pain research and management. Recent technical advances towards a more reproducible and comprehensive proteome and metabolome profiling by mass spectrometry have made this idea feasible.

The Quest for Disease Signatures of Pain—From Genomes to Multilayered Phenomes

Large public initiatives have searched for specific genetic signatures underlying painful pathologies by characterizing whole genomes of thousands of individuals. Examples include the Orofacial Pain Prospective Evaluation and Risk Assessment (OPPERA) study on temporomandibular disorders (Slade et al., 2016) and the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network on chronic pelvic pain (Clemens et al., 2014). These genome-wide studies have identified several candidate genes involved in chronic pain disorders (Diatchenko, Fillingim, Smith, & Maixner, 2013; Meloto et al., 2017; Zorina-Lichtenwalter, Meloto, Khoury, & Diatchenko, 2016). However, genome-wide studies are restricted in their power for diagnosis and causal predictions of environmental influences (changing lifestyle, nutritional status, comorbidities, and medical treatments) on phenotypes (Mogil, 2012; Oren & Ablin, 2013). A growing number of studies has also explored epigenetic ((Denk & McMahon, 2012; Doehring, Geisslinger, & Lotsch, 2011; Niederberger, 2014), gene regulatory (Meloto et al., 2017; Parisien et al., 2017; Peng et al., 2017), and transcriptomic changes (Grace et al., 2012; Jeong et al., 2016; Simonetti et al., 2013) associated with painful pathologies in rodents and human patients. Even so, the notion is evolving that only a limited percentage of detectable transcriptomic alterations are translated at the proteome or metabolome level to ultimately affect disease phenotypes (Liu, Beyer, & Aebersold, 2016; Schwanhausser et al., 2011; Sharma et al., 2015). This is especially true for dynamic conditions, in which protein turnover and half-lives, post-transcriptional and post-translational cellular buffering mechanisms may make significant contributions to phenotypes (Liu et al., 2016; Williams et al., 2016).

In this context, system-wide and diverse layers of data such as the genome, epigenome, transcriptome, proteome, and the metabolome should ideally be combined. Given the complexity of painful diseases and the subjective nature of pain, these multilayered measurements need to be synergistically integrated with a comprehensive characterization of phenotypes of the same individual to assemble the individual’s phenome (Bush, Oetjens, & Crawford, 2016; Houle, Govindaraju, & Omholt, 2010; Oti, Huynen, & Brunner, 2008). The phenome is defined as the full set of interactions between genes, transcripts, proteins, and metabolites shaping the phenotype of an organism. As such, it serves as a dynamic fingerprint, changing in response to aforementioned environmental influences. Thus, it is extremely valuable for assessing the ever-changing state of an organism with ample opportunities to monitor disease status, disease progression, as well as treatment response (Bush et al., 2016; Houle et al., 2010; Oti et al., 2008; Williams et al., 2016; Zierer, Menni, Kastenmuller, & Spector, 2015). Several recent studies have demonstrated the superior ability of multilayered phenomics to unravel mechanisms associated with complex cellular processes. For example, traits of mitochondrial liver metabolism were studied across a mouse genetic reference population of recombinant inbred strains under two dietary conditions on the genome, transcriptome, proteome, and metabolome levels (Williams et al., 2016; Wu et al., 2014). Also, a phenome-wide association study in mice and humans revealed novel molecular associations with disease risks such as bone mineral density (Wang et al., 2016). Altogether, these findings indicated that the integrated analysis of multiple layers provided more mechanistic insights than could be derived from any layer by itself. This nourishes the hope that implementation of proteomics and metabolomics approaches can significantly expand our knowledge of mechanisms underlying painful pathologies in animal models and humans.

Assembling Multilayered Phenomes of Chronic Pain: Integration of Proteomics and Metabolomics

Whereas the genotypical basis for phenotypical variation in painful experiences has been explored (Diatchenko et al., 2013; Meloto et al., 2017; Zorina-Lichtenwalter et al., 2016), there has still been relatively little work done on the integration of proteomics and metabolomics to mechanistically explain phenotypes related to pain.

Technical Considerations in Reproducible Mass Spectrometry

Mass spectrometry (MS) is the method of choice for proteomics and metabolomics experiments. In a classic bottom-up proteomics experiment (Aebersold & Mann, 2003, 2016), protein extracts obtained from tissues or liquids of interest are cleaved into short peptide sequences using enzymatic digestions (e.g., trypsin). These are separated by chromatography and analyzed in a mass spectrometer. Similarly, in the case of metabolomics, metabolites are extracted from complex samples and prepared for analysis by mass spectrometry (Patti, Yanes, & Siuzdak, 2012). Depending on the intended goals, distinct types of mass spectrometry technologies can be used (please see Figure 1 for an overview) (Aebersold & Mann, 2016; Sajic, Liu, & Aebersold, 2015). To date, most of the proteomics/metabolomics experiments in the context of pain (please see sections “Proteomic Studies in Pain Research” and “Metabolomics Studies in Pain Research”) have been performed using data-dependent acquisition (DDA) mass spectrometry. As shown in Figure 1, in a typical DDA experiment, the mass spectrometer selects a subset of peptides/metabolites (in general referred to as precursors) for fragmentation and identification. Due to its high sampling breadth, DDA enables the large-scale discovery of changes in the relative abundance of peptides/metabolites across conditions (e.g., health versus disease) (Aebersold & Mann, 2016; Geyer, Kulak, et al., 2016; Zamboni, Saghatelian, & Patti, 2015). Despite its common use, DDA is variable in selecting peptides/metabolites for measurements, leading to imprecise quantification and limited reproducibility across replicates (40–70%) (Michalski, Cox, & Mann, 2011; Sajic et al., 2015). With the emergence of novel MS approaches and optimizations in bioinformatics data analysis, these limitations can be overcome to achieve extensive proteome coverage and reliable quantitation. Among these techniques, targeted MS (Picotti & Aebersold, 2012; Sajic et al., 2015) and data-independent acquisition (DIA)-MS (Panchaud et al., 2009; Sajic et al., 2015; Venable, Dong, Wohlschlegel, Dillin, & Yates, 2004) hold great potential for hypothesis-driven experiments and biomarker discovery/validation (Cerciello et al., 2013; Huttenhain et al., 2012; Takadate et al., 2013). Targeted MS modes like selected reaction monitoring (SRM; Figure 1) feature the a priori (i.e., “targeted”) selection and subsequent fragmentation of multiple (on average 50–100 per run) peptides/metabolites of choice. When combined with reference peptides/metabolites (of known concentrations and spiked in as internal standards), highly accurate and absolute quantification of chosen peptides/metabolites across many samples is achieved. Yet this comes at the expense of preceding labor-intensive and time-consuming experiments required to establish the analyte-specific targeted selection. On the other hand, in DIA strategies, no precursor/metabolite selection takes place, and most of the peptides present in a complex sample are fragmented (in a given mass/charge [m/z] range and above the detection limit of the MS instrument; Figure 1). This permits the continuous and unbiased acquisition of peptides/metabolites, thereby increasing the data dimensionality. However, the resulting DIA datasets (stored in digital maps) are too complex to be analyzed by a classical database search (Figure 1). For this reason, diverse analytical methods have been developed for DIA digital maps: computational workflows independent from or dependent on reference spectral libraries (Figure 1) (Bruderer, Bernhardt, et al., 2017; Bruderer, Sondermann, et al., 2017; Chen et al., 2017; Li et al., 2015; Schubert et al., 2015; Tsou et al., 2015; Wang et al., 2015). Along the same lines, metabolite reference spectral libraries have been introduced for DIA metabolomics (Bruderer et al., 2018). Reference spectral libraries represent a compendium of peptides/metabolites with the necessary physicochemical information to uniquely identify single peptides/metabolites in the aforementioned complex spectra of DIA digital maps.

Figure 1 Overview of mass spectrometry in proteomics/metabolomics.

The Proteomics and Metabolomics of Pain—Opportunities for Systems MedicineClick to view larger

Prototypical examples of three major types of mass spectrometry approaches. Alongside, data analysis pipelines (P denotes “used for proteomics,” M denotes “used for metabolomics”) and an overview of major properties are listed.

The data-dependent acquisition (DDA) method is shown using a quadrupole-orbitrap instrument. The first quadrupole (Q1) selects precursor ions dependent on their abundance. These are further fragmented in the collision cell (Q2), and the fragments’ mass/charge (m/z) is analyzed by the orbitrap mass analyzer, yielding MS/MS spectra. The peptide identity of these spectra is then determined by searching protein databases such as Uniprot ( Similarly, the identity of features represented by MS/MS spectra in a metabolomics experiment is determined by searching metabolite databases such as the Human Metabolome Database ( (Wishart et al., 2013). Examples of data analysis pipelines: MaxQuant (Cox & Mann, 2008) and Perseus (, (meta)XCMS (Tautenhahn et al., 2011; Tautenhahn et al., 2012), and Mzmine (Katajamaa, Miettinen, & Oresic, 2006).

In selected-reaction-monitoring (SRM), a distinct precursor is selected in Q1 based on predetermined m/z values. This precursor is fragmented in Q2, and specific fragments (transitions) are further selected in Q3 for quantification. The intensity of each transition is then recorded over the whole chromatographic time; i.e., the instrument (shown here is a triple quadrupole mass spectrometer) continuously scans the sample for the predetermined peptide. Examples of SRM data analysis pipelines: Skyline (MacLean et al., 2010), mProphet (Reiter et al., 2011), and (meta)XCMS (Tautenhahn et al., 2011; Tautenhahn et al., 2012).

Several variations of DIA approaches have been implemented. As an example, sequential window acquisition of all theoretical fragment-ion spectra (SWATH)–DIA-MS is depicted. The mass spectrometer (shown here is a quadrupole-orbitrap instrument) sequentially cycles through ranges (windows) of m/z values (typical window width is 25m/z units). All peptides eluting in these windows from the chromatography are fragmented, and MS/MS spectra of all fragments are acquired in the mass analyzer. The instrument rapidly and continuously scans these windows over the whole chromatographic time and m/z precursor range. In this way, all detectable precursors entering the mass spectrometer can be measured generating multiplexed MS/MS spectra. Examples of data analysis pipelines: OpenSWATH (Rost et al., 2014), Spectronaut™ (Biognosys, Switzerland), DIA-Umpire (Tsou et al., 2015), (meta)XCMS (Tautenhahn et al., 2011; Tautenhahn et al., 2012), and MetaboDia (Chen et al., 2017).

Based on their distinct properties regarding analytical breadth, DDA and DIA approaches are especially suitable for discovery proteomics/metabolomics aiming at identifying large sets of proteins. Conversely, SRM approaches represent the gold standard for highly accurate quantification and are therefore used for validation of distinct candidate molecules in biomarker research and in clinical samples. Irrespective of the MS workflow, statistical data analysis includes the determination of false-discovery rates and diverse clustering algorithms for intra- and inter-group comparisons.

A valuable feature of DIA digital maps is that they can be re-currently queried in silico, reducing the need for new biological material—an immense advantage when biological material is limited, as is the case with some patient samples (e.g., invasive biopsies; Ebhardt, Root, Sander, & Aebersold, 2015). Combined with storage of reference spectral libraries and MS datasets in growing public repositories (e.g., PeptideAtlas,; PRIDE archive of the ProteomeXChange consortium,; the Human Protein Reference Database [HPRD] of Humanproteinpedia,; iProX of the Human Protein Project [HUPO],, the integration of MS technologies into clinical pain research and management would open immense opportunities: longitudinal monitoring, hypothesis generation, and patient stratification. Importantly, standardization and quality control of MS results for routine clinical studies are currently discussed (Collins et al., 2017; Kolker et al., 2014; Rocca-Serra et al., 2016; Rosenberger et al., 2017). Overall, technical advances in MS have already excelled in proof-of-concept studies interrogating pathology-related proteomes. For example, in patients, robust tumor protein signatures could be identified (Addona et al., 2011; Cerciello et al., 2013; Huttenhain et al., 2012; Takadate et al., 2013). Hopefully, similar success stories will emerge in relation to painful pathologies in the upcoming years.

Methodological Challenges—Standardized Sampling, Analyte Abundance, and Complexity of Samples

The variable quality of biological material used in research and clinical routine represents a major bottleneck for reproducibility. For example, venipuncture used to obtain human blood samples is highly variable in regard to the use of sampling vials, temperature, and time of storage. These variables have a huge impact on the stability and abundance of quantifiable proteins (Chambers, Percy, Hardie, & Borchers, 2013; Percy, Parker, & Borchers, 2013; Rosskopf et al., 2015) and metabolites (Breier et al., 2014). Ultimately, this influences the statistical power and clinical utility of biomarker discovery projects. The lack of sampling standardization contributes to the well-known gap between the amount of preclinically identified proteomic/metabolomic biomarkers and the actually validated ones available for clinical practice (Drucker & Krapfenbauer, 2013). In light of these difficulties, minimally invasive and reproducible procedures that enable preservation of easy-to-degrade molecules (e.g., proteins and metabolites) are mandatory. Only in this way can reliable monitoring of analytes useful for preventive, diagnostic, and prognostic management of chronic pain be achieved. The development of devices for reproducible capillary blood collection points in this direction (e.g., Tap® by SeventhSense Biosystems, and HemoLinkTM by Tasso INC). Standardized sampling procedures are also key requirements for the utility of samples stored in biobanks and for longitudinal studies. Longitudinal studies are designed to measure alterations of particular analytes from individual baselines, rather than using one-time measurements and population-based cutoff values. In this line, it is noteworthy that the proteome of blood plasma has been shown to be more constant within an individual over time than between individuals (Geyer, Holdt, Teupser, & Mann, 2017; Geyer, Wewer Albrechtsen, et al., 2016; Liu et al., 2015). Consequently, alterations from individuals’ baselines have been successfully exploited to reveal personalized disease- or lifestyle-associated changes in the plasma proteome (Geyer et al., 2017; Geyer, Wewer Albrechtsen, et al., 2016; Liu et al., 2015). Certainly, manifold pain-related aspects could be examined by longitudinal and “before–after” studies, such as treatment response and probability for pain chronification after surgery (Backryd, 2015; Sommer, 2016).

In addition to biosampling standardization, several biological issues must be considered when analyzing proteomes and metabolomes. Protein and metabolite abundance varies with cell type, developmental stage, and disease stage (Geyer et al., 2017). Moreover, likely disease-associated proteins are only found at minute concentrations in blood, composing the so-called “hidden blood proteome.” In contrast, serum proteins are very abundant. This very wide dynamic range of ten to eleven orders of magnitude (from <pg to mg/µl concentrations) poses substantial practical challenges to the comprehensive analysis of potential biomarker signatures (Geyer et al., 2017; Geyer, Kulak, et al., 2016). Furthermore, a tissue/liquid biopsy is rarely composed of only one cell type. This leads to the fact that generally mixed samples are analyzed, in which protein/metabolite (and certainly also transcript-level) changes in one cell type may be masked by opposing changes in another one (Berta, Qadri, Tan, & Ji, 2017; Krames, 2014). This challenge also applies to samples harboring affected and unaffected cells side-by-side, such as preclinical mouse models of chronic pain involving only partial injury of the sciatic nerve (e.g., the chronic constriction injury [CCI]-model and the spared nerve injury [SNI]-model; Minett, Quick, & Wood, 2011), and painful conditions in patients such as peripheral neuropathies and disc herniation (Costigan, Scholz, & Woolf, 2009; Price & Gold, 2017). Interestingly, pronounced differences in the transcriptome profile of injured and uninjured sensory neurons have been found (Berta, Perrin, et al., 2017; Reinhold et al., 2015). While assays specific for cell types and subcellular compartments are being developed (Kim & Roux, 2016; Sharma et al., 2015; Usoskin et al., 2015), they should ideally be complemented by cell labeling and separation reflecting their pathophysiological state; for example, injured versus uninjured cells.

A complementary and clinically applicable solution to dealing with sample heterogeneity may be offered by MS-based imaging of formalin-fixed paraffin embedded (FFPE) specimens. Sample preservation through paraffin embedding is commonplace in clinical routine, such as minimally invasive skin biopsies to diagnose patients with small fiber neuropathy, fibromyalgia, and painful diabetic neuropathies (Levine & Saperstein, 2015; Themistocleous et al., 2016). When coupled with laser microdissection and MS high resolution, spatial localization of proteins/metabolites can be achieved and correlated with tissue pathology, as demonstrated by several oncological studies (Ebhardt et al., 2015; Steiner et al., 2014).

Proteomic Studies in Pain Research

While a full list of proteomic studies in pain would be beyond the scope of this chapter, representative studies focused on different aspects will be highlighted. On one hand, pathology-related changes of proteomes were interrogated in diverse tissues and preclinical rodent models of pain (Huang et al., 2008; Komori et al., 2007; Lee et al., 2003; Rouwette, Sondermann, Avenali, Gomez-Varela, & Schmidt, 2016; Sui et al., 2014; Vacca et al., 2014; Zhang et al., 2008). Despite many discrepancies across these studies (probably due to aforementioned technical and biological factors), regulations in distinct cellular pathways have meanwhile reproducibly emerged. For example, proteins implicated in oxidative stress or its prevention were found to be regulated in hyper-excitable neuromas (Huang et al. (2008), in a dorsal root ganglia (DRG) proteome after spinal nerve ligation (Komori et al., 2007), and also in DRG membrane proteomes after inflammatory and neuropathic pain (Rouwette et al., 2016). The latter study represents the first to apply DIA-MS in pain research and reproducibly quantified approximately 2,500 membrane-associated proteins across two pain models. Besides, several studies promote pain research by defining proteomes in regions along the pain-axis of naïve rodent models, such as the sciatic nerve (Lu, Wisniewski, & Mann, 2009), DRG (Rouwette et al., 2016), the spinal cord (Lu et al., 2009), as well as various areas of the mouse brain (Sharma et al., 2015). Ultimately, this knowledge will serve as a stepping stone to assess proteome alterations under painful conditions.

Differential proteome analysis also suggested prominent changes in synaptic transmission, as inferred by alterations in Synapsin 1 (SYN1), N-ethylmaleimide-sensitive factor attachment protein (NAPB), and synaptophysin (SYP) in the dorsal horn of spinal cords upon spinal nerve ligation in rats (Sui et al., 2014). These results are in line with the crucial role that several types of voltage-gated calcium channels play for presynaptic neurotransmitter release in the spinal cord, such as Cav2.2 (Brittain et al., 2011; Brittain et al., 2009). Using antibody-mediated protein immunoprecipitation followed by MS, several Cav2.2 binding proteins (i.e., members of the Cav 2.2 interactome) were identified. Among those, the collapsin response mediator protein 2 (CRMP-2) increases Cav2.2 surface expression and activity, contributing to nociceptor hyperexcitability and chronic pain (Brittain et al., 2011; Brittain et al., 2009). Remarkably, this finding prompted the design of analgesic CRMP-2 peptides. Their in vivo injection uncoupled the CRMP-2–Cav2.2 interaction, and ultimately relieved acute, inflammatory, and neuropathic pain in various mouse models (Brittain et al., 2011; Brittain et al., 2009). Hence, MS-based identification of protein–protein interactions can offer elegant strategies to interfere with dysregulated proteins for pain relief—at least in rodent models of pain. Moreover, antibody-mediated protein immunoprecipitation followed by MS has elucidated novel pain players:

  1. (1) Interacting proteins of transient receptor potential (TRP) ion channels; e.g., gamma-aminobutyric acid type B receptors subunit 1 (GABAB1) suppressing transient receptor potential vanilloid 1 (TRPV1) ion channels during inflammatory pain (Hanack et al., 2015), annexin A2 (ANXA2) controlling transient receptor potential ankyrin 1 (TRPA1) -dependent nociceptive signaling (Avenali et al., 2014), and transmembrane protein 100 (TMEM100) potentiating TRPA1 by weakening its association with TRPV1 (Weng et al., 2015);

  2. (2) Interactions between the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunit 2 (GLUR2) and glutamate receptor-interacting protein (GRIP), protein interacting with C-kinase (PICK1), and N-ethylmaleimide sensitive fusion protein (NSF), all of which are linked to hypersensitivity upon nerve injury (Garry et al., 2003; Katano et al., 2008; Wang et al., 2010);

  3. (3) Interaction partners of the voltage-gated sodium channel NaV1.7, such as the G protein-regulated inducer of neurite outgrowth (Gprin1) and also aforementioned CRMP-2 (Kanellopoulos et al., 2018).

Proteomics on tissue biopsies of human patients is in its infancy. Nevertheless, several studies have revealed the first mechanistic insights into disorders like chronic widespread pain (CWP) (Olausson et al., 2015; Olausson, Ghafouri, Ghafouri, & Gerdle, 2016), complex regional pain syndrome (CRPS) (Oki et al., 2012), and chronic musculoskeletal pain; i.e., myalgia (Hadrevi, Ghafouri, Larsson, Gerdle, & Hellstrom, 2013; Olausson, Gerdle, Ghafouri, Larsson, & Ghafouri, 2012). For example, Olausson and colleagues identified 17 differentially regulated proteins in the trapezius muscle of female patients diagnosed with CWP compared to healthy controls (Olausson et al., 2015). Functionally, these proteins have been implicated in glycolysis and gluconeogenesis, muscle damage and recovery, stress and inflammation (Olausson et al., 2015). These results were later corroborated by examining the cerebrospinal fluid (CSF) proteome of female CWP patients (Olausson, Ghafouri, Backryd, & Gerdle, 2017), which suggested a prominent role of inflammation in CWP.

Liquid biopsies such as saliva, urine, CSF, and blood are of high clinical relevance. Especially the last holds great promise for easy implementation of longitudinal studies designed to assess diverse aspects such as surgery-induced changes, treatment success, and disease progression (Backryd, 2015). MS-based proteomics on plasma samples from farmers with work-related musculoskeletal disorders suggested alterations in several proteins such as hemopexin, apolipoprotein A1, and antithrombin, to name a few (Ghafouri, Carlsson, Holmberg, Thelin, & Tagesson, 2016). Interestingly, hemopexin correlated with pain intensity and physical disability upon low back pain (LBP) due to intervertebral disc degeneration (DD) in an unrelated MS-based examination of CSF (Lim et al., 2017). Furthermore, the analysis of CSF from patients with peripheral neuropathic pain uncovered several differentially regulated proteins compared to healthy controls (Backryd, Ghafouri, Carlsson, Olausson, & Gerdle, 2015). Among those, seven proteins (e.g., angiotensinogen isoforms, haptoglobin, and alpha-1-antitrypsin) exhibited high discriminative power between patients and healthy controls. Furthermore, numerous regulated proteins were also increased in rat serum five weeks after the induction of neuropathic pain in the CCI-model (Bellei et al., 2017); for example, prostaglandin-H2-D isomerase (PTGDS), transthyretin (TTR), apolipoprotein E (APOE), and apolipoprotein A-1 (APOA1). These similarities across species reinforce translational efforts in pain research.

Insights into proteome changes in response to diverse treatment options could also be gathered. In neuropathic pain patients, electrical neuromodulation by spinal cord stimulation was described to alter numerous CSF proteins in a before–after trial (i.e., each patient served as his/her own control) (Lind et al., 2016). Modulated canonical pathways included neuroprotection (e.g., gelsolin, angiotensinogen), synaptic plasticity (e.g., apoliprotein C-1 and APOE, similar to aforementioned across-species findings on neuropathic pain; Backryd et al., 2015; Bellei et al., 2017), and nociceptive signaling (e.g., neurosecretory protein VGF). A comparison of CSF proteomes of patients with postherpetic neuralgia (PHN) before and after treatment with intrathecal methylprednisolone and lidocaine four times every week yielded 14 prominently regulated proteins (Lu et al., 2012). As shown for peripheral neuropathic pain (Backryd et al., 2015), prostaglandin metabolism seemed to exhibit prominent regulation, as evidenced by a reduction of lipocalin-type prostaglandin D synthase (L-PGDS) upon treatment (Lu et al., 2012). Yet, this did not correlate with treatment-induced pain relief (Lu et al., 2012).

Taken together, these studies offer promising and at times intriguingly overlapping insights into chronic pain conditions. However, the validity and clinical utility of identified protein changes need to be critically assessed in large-cohort multi-center studies. This is especially necessary as these studies were performed on a small scale in few patients, using varying sample preparation methods, MS, and data processing technologies.

Metabolomics in Pain Research

The importance of metabolites as triggers for adaptive and dysregulated responses in biological systems has increasingly been revealed. Even though many small molecules are still uncharacterized (Patti, Yanes, & Siuzdak, 2012), metabolic dysregulations have been shown to be involved in many chronic diseases, including painful ones (Patti, Yanes, Shriver, et al., 2012; Patti, Yanes, & Siuzdak, 2012; Williams et al., 2016; Zierer et al., 2015; Zub et al., 2015). These studies are supported by storage of detected metabolites and their biochemical characteristics in reference databases such as the Human Metabolome Database (; Wishart et al., 2013) and METLIN (Guijas et al., 2018; Smith et al., 2005). These databases are of significant value, as MS/MS metabolomics data analysis software (Tautenhahn et al., 2011; Tautenhahn, Patti, Rinehart, & Siuzdak, 2012) only identify features, not metabolites (Figure 1). The identification of metabolites relies on accurate matching of MS/MS spectra with databases of known metabolites (Patti, Yanes, Shriver, et al., 2012; Patti, Yanes, & Siuzdak, 2012).

The use of metabolomic profiling in pain research is in its initial stages. Yet MS-independent methods have provided evidence that painful conditions correlate with changes in distinct metabolites. For example, Finco and colleagues used nuclear magnetic resonance (NMR) spectroscopy to compare the urine metabolome across healthy controls and patients suffering from pain. Distinct metabolome profiles allowed them to classify patients and discriminate between nociceptive and neuropathic pain (Finco et al., 2016). Hence, this study provided a proof-of-concept for metabolome-based patient stratification. In another study, elevation of salivary cortisol (detected by enzyme immunoassay) was found to be associated with pain catastrophizing in the context of experimental pain (Quartana et al., 2010).

Few pioneer studies have systematically analyzed the metabolome by MS to gain comprehensive mechanistic insights into chronic pain. Patti and colleagues discovered pronounced changes of metabolic features in several tissues and blood plasma of a rat model of neuropathic pain (Patti, Yanes, Shriver, et al., 2012). In particular, their data indicated that neuropathic pain alters sphingomyelin–ceramide metabolism, resulting in increased levels of distinct ceramide catabolites (N,N-dimethylsphingosines; DMS) with functional consequences for rat pain behaviors. These findings are in line with previous reports on the importance of ceramide and its metabolite sphingosine-1-phosphate for nociceptor hyperexcitability and pain (Chi & Nicol, 2010; Zhang, Vasko, & Nicol, 2002). In another study, the same work group presented an improved meta-analysis software for metabolomics experiments (Tautenhahn et al., 2011). Aided by this software, they identified several hundred dysregulated features in the plantar skin of three etiologically different mouse models of pain (compared to their respective controls): the acute pain model of noxious heat application to the hind paw, the inflammatory complete Freund´s adjuvant (CFA) model, and a spontaneous arthritis model of pain (animals intraperitoneally injected with serum from K/BxN mice representing a model for inflammatory arthritis-). In addition, histamine and two so-far-uncharacterized metabolites were found to be commonly dysregulated in all three pain models (Tautenhahn et al., 2011). It will be interesting to decipher the identity of these dysregulated features, as metabolite databases are constantly increasing their breadth.

Su and colleagues assessed the response of patients diagnosed with primary dysmenorrhea (PD)—painful menstrual cramps—to herbal treatment (specifically Shaofu Zhuyu formula concentrated-granule, SFZYFG) (Su et al., 2013). Longitudinal (before and after a three-month treatment) MS-based metabolomics identified alterations in 35 metabolites in blood plasma and urine of patients compared to healthy controls. Remarkably, the levels of several metabolites were normalized after herbal treatment, providing promising mechanistic insights into PD pathophysiology and potential treatment options (Su et al., 2013). Another study employed MS-based imaging of the spinal cord to investigate the regulation of spinal lipids upon neuropathic pain induced by peripheral nerve injury in mice (Banno et al., 2017). In this way, arachidonic acid containing phosphatidylcholine was found to be increased in the ipsilateral dorsal horn and correlated with behavioral hypersensitivity and activation of microglia.

An additional level of complexity arises from functional interactions between proteins and metabolites (PMIs). These interactions may modulate diverse enzymes, membrane proteins, and transcription factors (Changeux & Christopoulos, 2016; Gerosa & Sauer, 2011; Piazza et al., 2018), and thereby control a variety of cellular processes. Thus, PMIs can be exploited to determine the (patho)-physiological status of an organism by using large-scale MS-based approaches (Piazza et al., 2018). This knowledge may ultimately lead to the identification of novel allosteric sites (Nussinov & Tsai, 2013) with potential relevance for future drug discovery related to chronic pain.

Multilayered Phenomics in Personalized Systems Pain Medicine

Abovementioned studies highlight the immense opportunities that could be provided by a firm integration of proteomics and metabolomics into the pain researcher’s toolbox: in essence, (1) mechanistic insights into pain-associated phenome changes, (2) the identification of chronic pain signatures, and, ultimately, (3) novel therapeutic targets. Overall, the assembly of multilayered phenomes can pave the way towards systems medicine (Barabasi, Gulbahce, & Loscalzo, 2011; Menche et al., 2015; Zhou, Menche, Barabasi, & Sharma, 2014). Systems medicine aims at revealing the multilayered relationships among different disease-causing factors to obtain a comprehensive understanding of pathologies. Its application to pain would transform currently practiced empirical pain therapies into personalized, preventive, and predictive care; that is, personalized systems pain medicine (Borsook, Hargreaves, Bountra, & Porreca, 2014; Borsook & Kalso, 2013; Bruehl et al., 2013; Price & Gold, 2017; Vardeh et al., 2016).

From the operational point of view, the following four pillars are required to implement this paradigm shift:

  1. (1) Proper, statistically supported study design and objective phenotyping: Well-controlled and unbiased study design is key to deriving clinical decisions based on multilayered phenomes (Pepe, Feng, Janes, Bossuyt, & Potter, 2008; Ransohoff, 2005). Traditionally, biomarker discovery involves only few sample numbers at the initial proteome/metabolome discovery stage. During verification and validation steps the sample number is steadily increased until large clinical cohorts are analyzed by orthogonal methods, such as immunoassays. Due to the small initial sample number, these approaches lack statistical power (Diamandis, 2010; Hanash, 2011; Pepe et al., 2008; Ransohoff, 2005) and are limited by well-known technicalities of specificity and sensitivity associated with immunoassays (Geyer et al., 2017; Hanash, 2011). In contrast, in a so-called rectangular workflow, the discovery phase would already be performed on an adequately composed and large cohort (Geyer et al., 2017). In this way, statistically supported validation can be achieved in a straightforward manner and on a faster time-scale (Dunn, Wilson, Nicholls, & Broadhurst, 2012; Geyer et al., 2017). In this context, objective phenotyping tools enabling patient stratification are urgently required. Several efforts along these lines have been made for painful conditions; notably, standardized assessment of sensory characteristics by quantitative sensory testing (QST) (Arendt-Nielsen & Yarnitsky, 2009; Baron et al., 2017; Landis et al., 2014; Themistocleous et al., 2016) paired with elaborate pain questionnaires (Ruscheweyh, Marziniak, Stumpenhorst, Reinholz, & Knecht, 2009; Ruscheweyh et al., 2012), neuroimaging approaches (Landis et al., 2014; Woo & Wager, 2015), and aforementioned blood protein signatures, once identified.

  2. (2) Standardized biosampling procedures as discussed (please see “Methodological Challenges” section).

  3. (3) Reproducible and accurate analysis technology as discussed (please see “Technical Considerations” for MS-based analysis in this article).

  4. (4) Integrated data analysis pipelines: Thorough proteome/metabolome profiling of large cohorts will result in highly complex and big datasets with specific demands on MS informatics infrastructures. Issues related to data processing, reporting, sharing, and quality control need to be addressed. Along these lines, diverse databases and public repositories have been created (please see “Proteomic Studies in Pain Research” and “Metabolomics Studies in Pain Research” sections). Ideally, these databases should additionally integrate multilayered phenomes of large cohorts; that is, molecular data linked to phenotypical data and electronic medical records (observing legal and ethical precautions) (Borsook & Kalso, 2013; Bowton et al., 2015). Advanced bioinformatics tools for “big data” analysis will then be needed to aid with the interpretation of integrated data. Several laboratories have contributed to developing algorithms of network theory aimed at finding disease modules in large datasets (Barabasi et al., 2011; Kitsak et al., 2016; Shi et al., 2015; Zhang et al., 2009). In this way, disease signatures associated with distinct pathologies (cardiovascular diseases, cancer, and diabetes) could be identified (Barabasi et al., 2011; Kitsak et al., 2016; Oti, Snel, Huynen, & Brunner, 2006; Zhou et al., 2014). In addition, the correlation of two diseases can be mapped based on the similarity of symptoms and the connectivity of underlying protein interaction networks (Zhou et al., 2014). Remarkably, network-based algorithms have successfully been employed to predict human disease commonalities, even though our knowledge of the human interactome is fairly incomplete (Menche et al., 2015). A study by Guney and colleagues systematically compared the role of protein networks for the interplay of 238 drugs with 78 human diseases (Guney, Menche, Vidal, & Barabasi, 2016). It revealed that therapeutic drug effects are rather focused on a distinct subnetwork of disease modules (therefore, effective drugs are called “proximal drugs”). In contrast, ineffective drugs are more likely to target proteins “distant” from disease modules (hence they are called “distant drugs”), as is the case for ibuprofen in the context of rheumatoid arthritis (Guney et al., 2016). Moreover, machine-learning methods are rapidly evolving to assist in the search for disease-associated predictive features in multilayered datasets of large cohorts (Miotto, Li, Kidd, & Dudley, 2016; Soualmia & Lecroq, 2015; Tenenbaum et al., 2016). Ultimately, the goal is to derive personalized medical decisions by comparison of individual multilayered phenomes with existing reference profiles in these ever-growing datasets.


To date, most of the studies summarized here (please see “Proteomic Studies in Pain Research” and “Metabolomics Studies in Pain Research”) concentrated on the generation of one type of “-omics” data to compare different conditions; e.g., health versus disease. However, the strong interdependencies of different “-omics” data need to be kept in mind when aiming at a comprehensive understanding of pain syndromes. This is especially true for the study of chronic pain, due to its multifaceted nature, pronounced subjectivity, and high degree of inter-individual variability. The collection and integration of all collectable data (molecular—from genomic, epigenomic, transcriptomic, and proteomic to metabolomic—and clinical phenotypical data) into a multilayered phenome would allow the identification of biomarkers and the development of systems pain medicine. This would open novel avenues to address currently unmet needs in pain management. Of special interest will be the application to the fields of patient stratification, drug development, and treatment. Analyzing large cohorts for phenomic disease signatures underlying distinct chronic pain disorders will promote the stratification of patients by refining diagnosis, and also enable effective companion diagnostic strategies. All of this may expedite drug development.

In this new paradigm, the individual patient will be placed at the center of the stage and can play a crucial participatory role (Borsook & Kalso, 2013; Bruehl et al., 2013). Personalized care is already advocated by patient associations. This reflects the increasing determination of patients to better manage their own health by demanding more personalized and clinically meaningful information.

Future Directions

To make this vision come true, numerous hurdles in respect to sample preparation, data collection (standardization, sensitivity, throughput, and storage), and data processing still have to be overcome. In addition, legal, ethical, and political guidelines related to collection, clinical use, and storage of extensive personal data will need to be addressed. Nonetheless, if the technological advances of multilayered phenomics are embraced by the pain community (basic/clinical researchers and medical practitioners alike), it is conceivable that the next decade will bring about a significant change towards mechanism-based and personalized pain therapies.


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