Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE (www.oxfordhandbooks.com). © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

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

Introduction

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 (http://www.uniprot.org/). 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 (http://www.hmdb.ca (Wishart et al., 2013). Examples of data analysis pipelines: MaxQuant (Cox & Mann, 2008) and Perseus (http://www.coxdocs.org/doku.php), (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, peptideatlas.org; PRIDE archive of the ProteomeXChange consortium, www.ebi.ac.uk/pride; the Human Protein Reference Database [HPRD] of Humanproteinpedia, www.humanproteinpedia.org; iProX of the Human Protein Project [HUPO], www.iprox.hupo.org), 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 (http://www.hmdb.ca; 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.

Conclusion

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.

References

Addona, T. A., Shi, X., Keshishian, H., Mani, D. R., Burgess, M., Gillette, M. A., … Carr, S. A. (2011). A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nature Biotechnology, 29(7), 635–643. doi:10.1038/nbt.1899Find this resource:

Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207. doi:10.1038/nature01511Find this resource:

Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620), 347–355. doi:10.1038/nature19949Find this resource:

Arendt-Nielsen, L., & Yarnitsky, D. (2009). Experimental and clinical applications of quantitative sensory testing applied to skin, muscles and viscera. Journal of Pain, 10(6), 556–572. doi:10.1016/j.jpain.2009.02.002Find this resource:

Avenali, L., Narayanan, P., Rouwette, T., Cervellini, I., Sereda, M., Gomez-Varela, D., & Schmidt, M. (2014). Annexin A2 regulates TRPA1-dependent nociception. Journal of Neuroscience, 34(44), 14506–14516. doi:10.1523/JNEUROSCI.1801-14.2014Find this resource:

Backryd, E. (2015). Pain in the blood? Envisioning mechanism-based diagnoses and biomarkers in clinical pain medicine. Diagnostics (Basel), 5(1), 84–95. doi:10.3390/diagnostics5010084Find this resource:

Backryd, E., Ghafouri, B., Carlsson, A. K., Olausson, P., & Gerdle, B. (2015). Multivariate proteomic analysis of the cerebrospinal fluid of patients with peripheral neuropathic pain and healthy controls—a hypothesis-generating pilot study. Journal of Pain Research, 8, 321–333. doi:10.2147/JPR.S82970Find this resource:

Banno, T., Omura, T., Masaki, N., Arima, H., Xu, D., Okamoto, A., … Setou, M. (2017). Arachidonic acid containing phosphatidylcholine increases due to microglial activation in ipsilateral spinal dorsal horn following spared sciatic nerve injury. PLoS One, 12(5), e0177595. doi:10.1371/journal.pone.0177595Find this resource:

Barabasi, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68. doi:10.1038/nrg2918Find this resource:

Baron, R., Maier, C., Attal, N., Binder, A., Bouhassira, D., Cruccu, G., … Treede, R. D. (2017). Peripheral neuropathic pain: A mechanism-related organizing principle based on sensory profiles. Pain, 158(2), 261–272. doi:10.1097/j.pain.0000000000000753Find this resource:

Bellei, E., Vilella, A., Monari, E., Bergamini, S., Tomasi, A., Cuoghi, A., … Pini, L. A. (2017). Serum protein changes in a rat model of chronic pain show a correlation between animal and humans. Scientific Reports, 7, 41723. doi:10.1038/srep41723Find this resource:

Berta, T., Perrin, F. E., Pertin, M., Tonello, R., Liu, Y. C., Chamessian, A., … Decosterd, I. (2017). Gene expression profiling of cutaneous injured and non-injured nociceptors in SNI animal model of neuropathic pain. Scientific Reports, 7(1), 9367. doi:10.1038/s41598-017-08865-3Find this resource:

Berta, T., Qadri, Y., Tan, P. H., & Ji, R. R. (2017). Targeting dorsal root ganglia and primary sensory neurons for the treatment of chronic pain. Expert Opinion on Therapeutic Targets, 21(7), 695–703. doi:10.1080/14728222.2017.1328057Find this resource:

Borsook, D., Becerra, L., & Hargreaves, R. (2011). Biomarkers for chronic pain and analgesia. Part 1: The need, reality, challenges, and solutions. Discovery Medicine, 11(58), 197–207.Find this resource:

Borsook, D., Hargreaves, R., Bountra, C., & Porreca, F. (2014). Lost but making progress—Where will new analgesic drugs come from? Science Translational Medicine, 6(249), 249sr243. doi:10.1126/scitranslmed.3008320Find this resource:

Borsook, D., & Kalso, E. (2013). Transforming pain medicine: Adapting to science and society. European Journal of Pain, 17(8), 1109–1125. doi:10.1002/j.1532-2149.2013.00297.xFind this resource:

Bowton, E. A., Collier, S. P., Wang, X., Sutcliffe, C. B., Van Driest, S. L., Couch, L. J., … Pulley, J. M. (2015). Phenotype-driven plasma biobanking strategies and methods. Journal of Personalized Medicine, 5(2), 140–152. doi:10.3390/jpm5020140Find this resource:

Breier, M., Wahl, S., Prehn, C., Fugmann, M., Ferrari, U., Weise, M., … Lechner, A. (2014). Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One, 9(2), e89728. doi:10.1371/journal.pone.0089728Find this resource:

Breivik, H., Collett, B., Ventafridda, V., Cohen, R., & Gallacher, D. (2006). Survey of chronic pain in Europe: Prevalence, impact on daily life, and treatment. European Journal of Pain, 10(4), 287–333. doi:10.1016/j.ejpain.2005.06.009Find this resource:

Brittain, J. M., Duarte, D. B., Wilson, S. M., Zhu, W., Ballard, C., Johnson, P. L., … Khanna, R. (2011). Suppression of inflammatory and neuropathic pain by uncoupling CRMP-2 from the presynaptic Ca(2)(+) channel complex. Nature Medicine, 17(7), 822–829. doi:10.1038/nm.2345Find this resource:

Brittain, J. M., Piekarz, A. D., Wang, Y., Kondo, T., Cummins, T. R., & Khanna, R. (2009). An atypical role for collapsin response mediator protein 2 (CRMP-2) in neurotransmitter release via interaction with presynaptic voltage-gated calcium channels. Journal of Biological Chemistry, 284(45), 31375–31390. doi:10.1074/jbc.M109.009951Find this resource:

Bruderer, R., Bernhardt, O. M., Gandhi, T., Xuan, Y., Sondermann, J., Schmidt, M., … Reiter, L. (2017). Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Molecular & Cellular Proteomics, 16(12), 2296–2309. doi:10.1074/mcp.RA117.000314Find this resource:

Bruderer, R., Sondermann, J., Tsou, C. C., Barrantes-Freer, A., Stadelmann, C., Nesvizhskii, A. I., … Gomez-Varela, D. (2017). New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics, 17(9). doi:10.1002/pmic.201700021Find this resource:

Bruderer, T., Varesio, E., Hidasi, A. O., Duchoslav, E., Burton, L., Bonner, R., & Hopfgartner, G. (2018). Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition. Analytical & Bioanalytical Chemistry, 410(7), 1873–1884. doi:10.1007/s00216-018-0860-xFind this resource:

Bruehl, S., Apkarian, A. V., Ballantyne, J. C., Berger, A., Borsook, D., Chen, W. G., … Lin, Y. (2013). Personalized medicine and opioid analgesic prescribing for chronic pain: Opportunities and challenges. Journal of Pain, 14(2), 103–113. doi:10.1016/j.jpain.2012.10.016Find this resource:

Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016). Unravelling the human genome–phenome relationship using phenome-wide association studies. Nature Reviews Genetics, 17(3), 129–145. doi:10.1038/nrg.2015.36Find this resource:

Cerciello, F., Choi, M., Nicastri, A., Bausch-Fluck, D., Ziegler, A., Vitek, O., … Wollscheid, B. (2013). Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring. Clinical Proteomics, 10(1), 16. doi:10.1186/1559-0275-10-16Find this resource:

Chambers, A. G., Percy, A. J., Hardie, D. B., & Borchers, C. H. (2013). Comparison of proteins in whole blood and dried blood spot samples by LC/MS/MS. Journal of the American Society of Mass Spectrometry, 24(9), 1338–1345. doi:10.1007/s13361-013-0678-xFind this resource:

Changeux, J. P., & Christopoulos, A. (2016). Allosteric modulation as a unifying mechanism for receptor function and regulation. Cell, 166(5), 1084–1102. doi:10.1016/j.cell.2016.08.015Find this resource:

Chen, G., Walmsley, S., Cheung, G. C. M., Chen, L., Cheng, C. Y., Beuerman, R. W., … Choi, H. (2017). Customized consensus spectral library building for untargeted quantitative metabolomics analysis with data independent acquisition mass spectrometry and metaboDIA workflow. Analytical Chemistry, 89(9), 4897–4906. doi:10.1021/acs.analchem.6b05006Find this resource:

Chi, X. X., & Nicol, G. D. (2010). The sphingosine 1-phosphate receptor, S1PR(1), plays a prominent but not exclusive role in enhancing the excitability of sensory neurons. Journal of Neurophysiology, 104(5), 2741–2748. doi:10.1152/jn.00709.2010Find this resource:

Clemens, J. Q., Mullins, C., Kusek, J. W., Kirkali, Z., Mayer, E. A., Rodriguez, L. V., … Group, M. R. N. S. (2014). The MAPP research network: A novel study of urologic chronic pelvic pain syndromes. BMC Urology, 14, 57. doi:10.1186/1471-2490-14-57Find this resource:

Collins, B. C., Hunter, C. L., Liu, Y., Schilling, B., Rosenberger, G., Bader, S. L., … Aebersold, R. (2017). Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nature Communications, 8(1), 291. doi:10.1038/s41467-017-00249-5Find this resource:

Costigan, M. (2012). Pain’s peptide signature. Pain, 153(3), 509–510. doi:10.1016/j.pain.2012.01.004Find this resource:

Costigan, M., Scholz, J., & Woolf, C. J. (2009). Neuropathic pain: A maladaptive response of the nervous system to damage. Annual Review of Neuroscience, 32, 1–32. doi:10.1146/annurev.neuro.051508.135531Find this resource:

Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367–1372. doi:10.1038/nbt.1511Find this resource:

Denk, F., & McMahon, S. B. (2012). Chronic pain: Emerging evidence for the involvement of epigenetics. Neuron, 73(3), 435–444. doi:10.1016/j.neuron.2012.01.012Find this resource:

Diamandis, E. P. (2010). Cancer biomarkers: Can we turn recent failures into success? Journal of the National Cancer Institute, 102(19), 1462–1467. doi:10.1093/jnci/djq306Find this resource:

Diatchenko, L., Fillingim, R. B., Smith, S. B., & Maixner, W. (2013). The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nature Reviews Rheumatology, 9(6), 340–350. doi:10.1038/nrrheum.2013.43Find this resource:

Doehring, A., Geisslinger, G., & Lotsch, J. (2011). Epigenetics in pain and analgesia: An imminent research field. European Journal of Pain, 15(1), 11–16. doi:10.1016/j.ejpain.2010.06.004Find this resource:

Drucker, E., & Krapfenbauer, K. (2013). Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA Journal, 4(1), 7. doi:10.1186/1878-5085-4-7Find this resource:

Dunn, W. B., Wilson, I. D., Nicholls, A. W., & Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18), 2249–2264. doi:10.4155/bio.12.204Find this resource:

Ebhardt, H. A., Root, A., Sander, C., & Aebersold, R. (2015). Applications of targeted proteomics in systems biology and translational medicine. Proteomics, 15(18), 3193–3208. doi:10.1002/pmic.201500004Find this resource:

Finco, G., Locci, E., Mura, P., Massa, R., Noto, A., Musu, M., … Evangelista, M. (2016). Can urine metabolomics be helpful in differentiating neuropathic and nociceptive pain? A proof-of-concept study. PLoS One, 11(3), e0150476. doi:10.1371/journal.pone.0150476Find this resource:

Garry, E. M., Moss, A., Rosie, R., Delaney, A., Mitchell, R., & Fleetwood-Walker, S. M. (2003). Specific involvement in neuropathic pain of AMPA receptors and adapter proteins for the GluR2 subunit. Molecular & Cellular Neuroscience, 24(1), 10–22.Find this resource:

Gereau, R. W. T., Sluka, K. A., Maixner, W., Savage, S. R., Price, T. J., Murinson, B. B., … Fillingim, R. B. (2014). A pain research agenda for the 21st century. Journal of Pain, 15(12), 1203–1214. doi:10.1016/j.jpain.2014.09.004Find this resource:

Gerosa, L., & Sauer, U. (2011). Regulation and control of metabolic fluxes in microbes. Current Opinion in Biotechnology, 22(4), 566–575. doi:10.1016/j.copbio.2011.04.016Find this resource:

Geyer, P. E., Holdt, L. M., Teupser, D., & Mann, M. (2017). Revisiting biomarker discovery by plasma proteomics. Molecular Systems Biology, 13(9), 942. doi:10.15252/msb.20156297Find this resource:

Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016). Plasma proteome profiling to assess human health and disease. Cell Systems, 2(3), 185–195. doi:10.1016/j.cels.2016.02.015Find this resource:

Geyer, P. E., Wewer Albrechtsen, N. J., Tyanova, S., Grassl, N., Iepsen, E. W., Lundgren, J., … Mann, M. (2016). Proteomics reveals the effects of sustained weight loss on the human plasma proteome. Molecular Systems Biology, 12(12), 901. doi:10.15252/msb.20167357Find this resource:

Ghafouri, B., Carlsson, A., Holmberg, S., Thelin, A., & Tagesson, C. (2016). Biomarkers of systemic inflammation in farmers with musculoskeletal disorders: A plasma proteomic study. BMC Musculoskeletal Disorders, 17, 206. doi:10.1186/s12891-016-1059-yFind this resource:

Grace, P. M., Hurley, D., Barratt, D. T., Tsykin, A., Watkins, L. R., Rolan, P. E., & Hutchinson, M. R. (2012). Harnessing pain heterogeneity and RNA transcriptome to identify blood-based pain biomarkers: A novel correlational study design and bioinformatics approach in a graded chronic constriction injury model. Journal of Neurochemistry, 122(5), 976–994. doi:10.1111/j.1471-4159.2012.07833.xFind this resource:

Grosser, T., Woolf, C. J., & FitzGerald, G. A. (2017). Time for nonaddictive relief of pain. Science, 355(6329), 1026–1027. doi:10.1126/science.aan0088Find this resource:

Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., … Siuzdak, G. (2018). METLIN: A technology platform for identifying knowns and unknowns. Analytical Chemistry, 90(5), 3156–3164. doi:10.1021/acs.analchem.7b04424Find this resource:

Guney, E., Menche, J., Vidal, M., & Barabasi, A. L. (2016). Network-based in silico drug efficacy screening. Nature Communications, 7, 10331. doi:10.1038/ncomms10331Find this resource:

Hadrevi, J., Ghafouri, B., Larsson, B., Gerdle, B., & Hellstrom, F. (2013). Multivariate modeling of proteins related to trapezius myalgia, a comparative study of female cleaners with or without pain. PLoS One, 8(9), e73285. doi:10.1371/journal.pone.0073285Find this resource:

Hanack, C., Moroni, M., Lima, W. C., Wende, H., Kirchner, M., Adelfinger, L., … Siemens, J. (2015). GABA blocks pathological but not acute TRPV1 pain signals. Cell, 160(4), 759–770. doi:10.1016/j.cell.2015.01.022Find this resource:

Hanash, S. M. (2011). Why have protein biomarkers not reached the clinic? Genome Medicine, 3(10), 66. doi:10.1186/gm282Find this resource:

Houle, D., Govindaraju, D. R., & Omholt, S. (2010). Phenomics: The next challenge. Nature Reviews Genetics, 11(12), 855–866. doi:10.1038/nrg2897Find this resource:

Huang, H. L., Cendan, C. M., Roza, C., Okuse, K., Cramer, R., Timms, J. F., & Wood, J. N. (2008). Proteomic profiling of neuromas reveals alterations in protein composition and local protein synthesis in hyper-excitable nerves. Molecular Pain, 4, 33. doi:10.1186/1744-8069-4-33Find this resource:

Huttenhain, R., Soste, M., Selevsek, N., Rost, H., Sethi, A., Carapito, C., … Aebersold, R. (2012). Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. Science Translational Medicine, 4(142), 142ra194. doi:10.1126/scitranslmed.3003989Find this resource:

Jeong, H., Na, Y. J., Lee, K., Kim, Y. H., Lee, Y., Kang, M., … Oh, S. B. (2016). High-resolution transcriptome analysis reveals neuropathic pain gene-expression signatures in spinal microglia after nerve injury. Pain, 157(4), 964–976. doi:10.1097/j.pain.0000000000000470Find this resource:

Kanellopoulos, A. H., Koenig, J., Huang, H., Pyrski, M., Millet, Q., Lolignier, S., … Zhao, J. (2018). Mapping protein interactions of sodium channel NaV1.7 using epitope-tagged gene-targeted mice. EMBO Journal, 37(3), 427–445. doi:10.15252/embj.201796692Find this resource:

Katajamaa, M., Miettinen, J., & Oresic, M. (2006). MZmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 22(5), 634–636. doi:10.1093/bioinformatics/btk039Find this resource:

Katano, T., Furue, H., Okuda-Ashitaka, E., Tagaya, M., Watanabe, M., Yoshimura, M., & Ito, S. (2008). N-ethylmaleimide-sensitive fusion protein (NSF) is involved in central sensitization in the spinal cord through GluR2 subunit composition switch after inflammation. European Journal of Neuroscience, 27(12), 3161–3170. doi:10.1111/j.1460-9568.2008.06293.xFind this resource:

Kim, D. I., & Roux, K. J. (2016). Filling the void: Proximity-based labeling of proteins in living cells. Trends in Cell Biology, 26(11), 804–817. doi:10.1016/j.tcb.2016.09.004Find this resource:

Kitsak, M., Sharma, A., Menche, J., Guney, E., Ghiassian, S. D., Loscalzo, J., & Barabasi, A. L. (2016). Tissue specificity of human disease module. Scientific Reports, 6, 35241. doi:10.1038/srep35241Find this resource:

Kolker, E., Ozdemir, V., Martens, L., Hancock, W., Anderson, G., Anderson, N., … Yandl, G. (2014). Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS, 18(1), 10–14. doi:10.1089/omi.2013.0149Find this resource:

Komori, N., Takemori, N., Kim, H. K., Singh, A., Hwang, S. H., Foreman, R. D., … Matsumoto, H. (2007). Proteomics study of neuropathic and nonneuropathic dorsal root ganglia: Altered protein regulation following segmental spinal nerve ligation injury. Physiological Genomics, 29(2), 215–230. doi:10.1152/physiolgenomics.00255.2006Find this resource:

Krames, E. S. (2014). The role of the dorsal root ganglion in the development of neuropathic pain. Pain Medicine, 15(10), 1669–1685. doi:10.1111/pme.12413Find this resource:

Landis, J. R., Williams, D. A., Lucia, M. S., Clauw, D. J., Naliboff, B. D., Robinson, N. A., … Group, M. R. N. S. (2014). The MAPP research network: Design, patient characterization and operations. BMC Urology, 14, 58. doi:10.1186/1471-2490-14-58Find this resource:

Lee, S. C., Yoon, T. G., Yoo, Y. I., Bang, Y. J., Kim, H. Y., Jeoung, D. I., & Kim, H. J. (2003). Analysis of spinal cord proteome in the rats with mechanical allodynia after the spinal nerve injury. Biotechnology Letters, 25(24), 2071–2078.Find this resource:

Levine, T. D., & Saperstein, D. S. (2015). Routine use of punch biopsy to diagnose small fiber neuropathy in fibromyalgia patients. Clinical Rheumatology, 34(3), 413–417. doi:10.1007/s10067-014-2850-5Find this resource:

Li, Y., Zhong, C. Q., Xu, X., Cai, S., Wu, X., Zhang, Y., … Han, J. (2015). Group-DIA: Analyzing multiple data-independent acquisition mass spectrometry data files. Nature Methods, 12(12), 1105–1106. doi:10.1038/nmeth.3593Find this resource:

Lim, T. K. Y., Anderson, K. M., Hari, P., Di Falco, M., Reihsen, T. E., Wilcox, G. L., … Stone, L. S. (2017). Evidence for a role of nerve injury in painful intervertebral disc degeneration: A cross-sectional proteomic analysis of human cerebrospinal fluid. Journal of Pain, 18(10), 1253–1269. doi:10.1016/j.jpain.2017.06.002Find this resource:

Lind, A. L., Emami Khoonsari, P., Sjodin, M., Katila, L., Wetterhall, M., Gordh, T., & Kultima, K. (2016). Spinal cord stimulation alters protein levels in the cerebrospinal fluid of neuropathic pain patients: A proteomic mass spectrometric analysis. Neuromodulation, 19(6), 549–562. doi:10.1111/ner.12473Find this resource:

Liu, Y., Beyer, A., & Aebersold, R. (2016). On the dependency of cellular protein levels on mRNA abundance. Cell, 165(3), 535–550. doi:10.1016/j.cell.2016.03.014Find this resource:

Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L. Y., … Aebersold, R. (2015). Quantitative variability of 342 plasma proteins in a human twin population. Molecular Systems Biology, 11(1), 786. doi:10.15252/msb.20145728Find this resource:

Lu, A., Wisniewski, J. R., & Mann, M. (2009). Comparative proteomic profiling of membrane proteins in rat cerebellum, spinal cord, and sciatic nerve. Journal of Proteome Research, 8(5), 2418–2425. doi:10.1021/pr8010364Find this resource:

Lu, J., Katano, T., Nishimura, W., Fujiwara, S., Miyazaki, S., Okasaki, I., … Ito, S. (2012). Proteomic analysis of cerebrospinal fluid before and after intrathecal injection of steroid into patients with postherpetic pain. Proteomics, 12(19–20), 3105–3112. doi:10.1002/pmic.201200125Find this resource:

MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M., Finney, G. L., Frewen, B., … MacCoss, M. J. (2010). Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics, 26(7), 966–968. doi:10.1093/bioinformatics/btq054Find this resource:

Meloto, C. B., Benavides, R., Lichtenwalter, R. N., Wen, X., Tugarinov, N., Zorina-Lichtenwalter, K., … Diatchenko, L. (2017). The Human Pain Genetics Database (HPGDB): A resource dedicated to human pain genetics research. Pain. doi:10.1097/j.pain.0000000000001135Find this resource:

Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabasi, A. L. (2015). Disease networks. Uncovering disease–disease relationships through the incomplete interactome. Science, 347(6224), 1257601. doi:10.1126/science.1257601Find this resource:

Michalski, A., Cox, J., & Mann, M. (2011). More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. Journal of Proteome Research, 10(4), 1785–1793. doi:10.1021/pr101060vFind this resource:

Minett, M. S., Quick, K., & Wood, J. N. (2011). Behavioral Measures of Pain Thresholds. Current Protocols in Mouse Biology. New York: John Wiley & Sons. 1(3), 383–412. doi: 10.1002/9780470942390.mo110116Find this resource:

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094. doi:10.1038/srep26094Find this resource:

Mogil, J. S. (2012). Pain genetics: Past, present and future. Trends in Genetics, 28(6), 258–266. doi:10.1016/j.tig.2012.02.004Find this resource:

Niederberger, E. (2014). [Epigenetics and pain]. Anaesthetist, 63(1), 63–69. doi:10.1007/s00101-013-2274-7Find this resource:

Nussinov, R., & Tsai, C. J. (2013). Allostery in disease and in drug discovery. Cell, 153(2), 293–305. doi:10.1016/j.cell.2013.03.034Find this resource:

Oki, G., Wada, T., Iba, K., Aiki, H., Sasaki, K., Imai, S., … Kokai, Y. (2012). Metallothionein deficiency in the injured peripheral nerves of complex regional pain syndrome as revealed by proteomics. Pain, 153(3), 532–539. doi:10.1016/j.pain.2011.11.008Find this resource:

Olausson, P., Gerdle, B., Ghafouri, N., Larsson, B., & Ghafouri, B. (2012). Identification of proteins from interstitium of trapezius muscle in women with chronic myalgia using microdialysis in combination with proteomics. PLoS One, 7(12), e52560. doi:10.1371/journal.pone.0052560Find this resource:

Olausson, P., Gerdle, B., Ghafouri, N., Sjostrom, D., Blixt, E., & Ghafouri, B. (2015). Protein alterations in women with chronic widespread pain—An explorative proteomic study of the trapezius muscle. Scientific Reports, 5, 11894. doi:10.1038/srep11894Find this resource:

Olausson, P., Ghafouri, B., Backryd, E., & Gerdle, B. (2017). Clear differences in cerebrospinal fluid proteome between women with chronic widespread pain and healthy women—a multivariate explorative cross-sectional study. Journal of Pain Research, 10, 575–590. doi:10.2147/JPR.S125667Find this resource:

Olausson, P., Ghafouri, B., Ghafouri, N., & Gerdle, B. (2016). Specific proteins of the trapezius muscle correlate with pain intensity and sensitivity—an explorative multivariate proteomic study of the trapezius muscle in women with chronic widespread pain. Journal of Pain Research, 9, 345–356. doi:10.2147/JPR.S102275Find this resource:

Oren, O., & Ablin, J. N. (2013). Lighting up the genetic understanding of fibromyalgia. Journal of Rheumatology, 40(3), 214–215. doi:10.3899/jrheum.121411Find this resource:

Oti, M., Huynen, M. A., & Brunner, H. G. (2008). Phenome connections. Trends in Genetics, 24(3), 103–106. doi:10.1016/j.tig.2007.12.005Find this resource:

Oti, M., Snel, B., Huynen, M. A., & Brunner, H. G. (2006). Predicting disease genes using protein–protein interactions. Journal of Medical Genetics, 43(8), 691–698. doi:10.1136/jmg.2006.041376Find this resource:

Panchaud, A., Scherl, A., Shaffer, S. A., von Haller, P. D., Kulasekara, H. D., Miller, S. I., & Goodlett, D. R. (2009). Precursor acquisition independent from ion count: How to dive deeper into the proteomics ocean. Analytical Chemistry, 81(15), 6481–6488. doi:10.1021/ac900888sFind this resource:

Parisien, M., Khoury, S., Chabot-Dore, A. J., Sotocinal, S. G., Slade, G. D., Smith, S. B., … Diatchenko, L. (2017). Effect of human genetic variability on gene expression in dorsal root ganglia and association with pain phenotypes. Cell Reports, 19(9), 1940–1952. doi:10.1016/j.celrep.2017.05.018Find this resource:

Patti, G. J., Yanes, O., Shriver, L. P., Courade, J. P., Tautenhahn, R., Manchester, M., & Siuzdak, G. (2012). Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin. Nature Chemical Biology, 8(3), 232–234. doi:10.1038/nchembio.767Find this resource:

Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13(4), 263–269. doi:10.1038/nrm3314Find this resource:

Peng, C., Li, L., Zhang, M. D., Bengtsson Gonzales, C., Parisien, M., Belfer, I., … Ernfors, P. (2017). miR-183 cluster scales mechanical pain sensitivity by regulating basal and neuropathic pain genes. Science, 356(6343), 1168–1171. doi:10.1126/science.aam7671Find this resource:

Pepe, M. S., Feng, Z., Janes, H., Bossuyt, P. M., & Potter, J. D. (2008). Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: Standards for study design. Journal of the National Cancer Institute, 100(20), 1432–1438. doi:10.1093/jnci/djn326Find this resource:

Percy, A. J., Parker, C. E., & Borchers, C. H. (2013). Pre-analytical and analytical variability in absolute quantitative MRM-based plasma proteomic studies. Bioanalysis, 5(22), 2837–2856. doi:10.4155/bio.13.245Find this resource:

Piazza, I., Kochanowski, K., Cappelletti, V., Fuhrer, T., Noor, E., Sauer, U., & Picotti, P. (2018). A map of protein–metabolite interactions reveals principles of chemical communication. Cell, 172(1–2), 358–372 e323. doi:10.1016/j.cell.2017.12.006Find this resource:

Picotti, P., & Aebersold, R. (2012). Selected reaction monitoring-based proteomics: Workflows, potential, pitfalls and future directions. Nature Methods, 9(6), 555–566. doi:10.1038/nmeth.2015Find this resource:

Price, T. J., & Gold, M. S. (2017). From mechanism to cure: Renewing the goal to eliminate the disease of pain. Pain Medicine. doi:10.1093/pm/pnx108Find this resource:

Quartana, P. J., Buenaver, L. F., Edwards, R. R., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2010). Pain catastrophizing and salivary cortisol responses to laboratory pain testing in temporomandibular disorder and healthy participants. Journal of Pain, 11(2), 186–194. doi:10.1016/j.jpain.2009.07.008Find this resource:

Ransohoff, D. F. (2005). Bias as a threat to the validity of cancer molecular-marker research. Nature Reviews Cancer, 5(2), 142–149. doi:10.1038/nrc1550Find this resource:

Reinhold, A. K., Batti, L., Bilbao, D., Buness, A., Rittner, H. L., & Heppenstall, P. A. (2015). Differential transcriptional profiling of damaged and intact adjacent dorsal root ganglia neurons in neuropathic pain. PLoS One, 10(4), e0123342. doi:10.1371/journal.pone.0123342Find this resource:

Reiter, L., Rinner, O., Picotti, P., Huttenhain, R., Beck, M., Brusniak, M. Y., … Aebersold, R. (2011). mProphet: Automated data processing and statistical validation for large-scale SRM experiments. Nature Methods, 8(5), 430–435. doi:10.1038/nmeth.1584Find this resource:

Rocca-Serra, P., Salek, R. M., Arita, M., Correa, E., Dayalan, S., Gonzalez-Beltran, A., … Neumann, S. (2016). Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics, 12, 14. doi:10.1007/s11306-015-0879-3Find this resource:

Rosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C. L., Liu, Y., … Aebersold, R. (2017). Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nature Methods, 14(9), 921–927. doi:10.1038/nmeth.4398Find this resource:

Rosskopf, S., Gyurjan, I., Soldo, R., Luna-Coronell, J. A., Vierlinger, K., Singer, C. F., … Weinhaeusel, A. (2015). The pre-analytical processing of blood samples for detecting biomarkers on protein microarrays. Journal of Immunological Methods, 418, 39–51. doi:10.1016/j.jim.2015.01.009Find this resource:

Rost, H. L., Rosenberger, G., Navarro, P., Gillet, L., Miladinovic, S. M., Schubert, O. T., … Aebersold, R. (2014). OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnology, 32(3), 219–223. doi:10.1038/nbt.2841Find this resource:

Rouwette, T., Sondermann, J., Avenali, L., Gomez-Varela, D., & Schmidt, M. (2016). Standardized profiling of the membrane-enriched proteome of mouse dorsal root ganglia (DRG) provides novel insights into chronic pain. Molecular & Cellular Proteomics, 15(6), 2152–2168. doi:10.1074/mcp.M116.058966Find this resource:

Ruscheweyh, R., Marziniak, M., Stumpenhorst, F., Reinholz, J., & Knecht, S. (2009). Pain sensitivity can be assessed by self-rating: Development and validation of the Pain Sensitivity Questionnaire. Pain, 146(1–2), 65–74. doi:10.1016/j.pain.2009.06.020Find this resource:

Ruscheweyh, R., Verneuer, B., Dany, K., Marziniak, M., Wolowski, A., Colak-Ekici, R., … Knecht, S. (2012). Validation of the pain sensitivity questionnaire in chronic pain patients. Pain, 153(6), 1210–1218. doi:10.1016/j.pain.2012.02.025Find this resource:

Sajic, T., Liu, Y., & Aebersold, R. (2015). Using data-independent, high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications. Proteomics Clinical Applications, 9(3–4), 307–321. doi:10.1002/prca.201400117Find this resource:

Schubert, O. T., Gillet, L. C., Collins, B. C., Navarro, P., Rosenberger, G., Wolski, W. E., … Aebersold, R. (2015). Building high-quality assay libraries for targeted analysis of SWATH MS data. Nature Protocols, 10(3), 426–441. doi:10.1038/nprot.2015.015Find this resource:

Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., … Selbach, M. (2011). Global quantification of mammalian gene expression control. Nature, 473(7347), 337–342. doi:10.1038/nature10098Find this resource:

Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., … Simons, M. (2015). Cell type- and brain region-resolved mouse brain proteome. Nature Neuroscience, 18(12), 1819–1831. doi:10.1038/nn.4160Find this resource:

Shi, X., Barnes, R. O., Chen, L., Shajahan-Haq, A. N., Hilakivi-Clarke, L., Clarke, R., … Xuan, J. (2015). BMRF-Net: A software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method. Bioinformatics, 31(14), 2412–2414. doi:10.1093/bioinformatics/btv137Find this resource:

Simonetti, M., Hagenston, A. M., Vardeh, D., Freitag, H. E., Mauceri, D., Lu, J., … Kuner, R. (2013). Nuclear calcium signaling in spinal neurons drives a genomic program required for persistent inflammatory pain. Neuron, 77(1), 43–57. doi:10.1016/j.neuron.2012.10.037Find this resource:

Slade, G. D., Ohrbach, R., Greenspan, J. D., Fillingim, R. B., Bair, E., Sanders, A. E., … Maixner, W. (2016). Painful temporomandibular disorder: Decade of discovery from OPPERA studies. Journal of Dental Research, 95(10), 1084–1092. doi:10.1177/0022034516653743Find this resource:

Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., … Siuzdak, G. (2005). METLIN: A metabolite mass spectral database. Therapeutic Drug Monitoring, 27(6), 747–751.Find this resource:

Sommer, C. (2016). Exploring pain pathophysiology in patients. Science, 354(6312), 588–592. doi:10.1126/science.aaf8935Find this resource:

Soualmia, L. F., & Lecroq, T. (2015). Bioinformatics methods and tools to advance clinical care. Findings from the Yearbook 2015 section on bioinformatics and translational informatics. Yearbook of Medical Informatics, 10(1), 170–173. doi:10.15265/IY-2015-026Find this resource:

Steiner, C., Ducret, A., Tille, J. C., Thomas, M., McKee, T. A., Rubbia-Brandt, L., … Cutler, P. (2014). Applications of mass spectrometry for quantitative protein analysis in formalin-fixed paraffin-embedded tissues. Proteomics, 14(4–5), 441–451. doi:10.1002/pmic.201300311Find this resource:

Su, S., Duan, J., Wang, P., Liu, P., Guo, J., Shang, E., … Tang, Z. (2013). Metabolomic study of biochemical changes in the plasma and urine of primary dysmenorrhea patients using UPLC-MS coupled with a pattern recognition approach. Journal of Proteome Research, 12(2), 852–865. doi:10.1021/pr300935xFind this resource:

Sui, P., Watanabe, H., Ossipov, M. H., Bakalkin, G., Artemenko, K., & Bergquist, J. (2014). Proteomics of neuropathic pain: Proteins and signaling pathways affected in a rat model. Journal of Proteome Research, 13(9), 3957–3965. doi:10.1021/pr500241qFind this resource:

Takadate, T., Onogawa, T., Fukuda, T., Motoi, F., Suzuki, T., Fujii, K., … Unno, M. (2013). Novel prognostic protein markers of resectable pancreatic cancer identified by coupled shotgun and targeted proteomics using formalin-fixed paraffin-embedded tissues. International Journal of Cancer, 132(6), 1368–1382. doi:10.1002/ijc.27797Find this resource:

Tautenhahn, R., Patti, G. J., Kalisiak, E., Miyamoto, T., Schmidt, M., Lo, F. Y., … Siuzdak, G. (2011). metaXCMS: Second-order analysis of untargeted metabolomics data. Analytical Chemistry, 83(3), 696–700. doi:10.1021/ac102980gFind this resource:

Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G. (2012). XCMS Online: A web-based platform to process untargeted metabolomic data. Analytical Chemistry, 84(11), 5035–5039. doi:10.1021/ac300698cFind this resource:

Tenenbaum, J. D., Avillach, P., Benham-Hutchins, M., Breitenstein, M. K., Crowgey, E. L., Hoffman, M. A., … Freimuth, R. R. (2016). An informatics research agenda to support precision medicine: Seven key areas. Journal of the American Medical Informatics Association, 23(4), 791–795. doi:10.1093/jamia/ocv213Find this resource:

Themistocleous, A. C., Ramirez, J. D., Shillo, P. R., Lees, J. G., Selvarajah, D., Orengo, C., … Bennett, D. L. (2016). The Pain in Neuropathy Study (PiNS): A cross-sectional observational study determining the somatosensory phenotype of painful and painless diabetic neuropathy. Pain, 157(5), 1132–1145. doi:10.1097/j.pain.0000000000000491Find this resource:

Tsou, C. C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A. C., & Nesvizhskii, A. I. (2015). DIA-Umpire: Comprehensive computational framework for data-independent acquisition proteomics. Nature Methods, 12(3), 258–264. doi:10.1038/nmeth.3255Find this resource:

Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lonnerberg, P., Lou, D., … Ernfors, P. (2015). Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nature Neuroscience, 18(1), 145–153. doi:10.1038/nn.3881Find this resource:

Vacca, V., Marinelli, S., Pieroni, L., Urbani, A., Luvisetto, S., & Pavone, F. (2014). Higher pain perception and lack of recovery from neuropathic pain in females: A behavioural, immunohistochemical, and proteomic investigation on sex-related differences in mice. Pain, 155(2), 388–402. doi:10.1016/j.pain.2013.10.027Find this resource:

Vardeh, D., Mannion, R. J., & Woolf, C. J. (2016). Toward a mechanism-based approach to pain diagnosis. Journal of Pain, 17(9 Suppl), T50–T69. doi:10.1016/j.jpain.2016.03.001Find this resource:

Venable, J. D., Dong, M. Q., Wohlschlegel, J., Dillin, A., & Yates, J. R. (2004). Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nature Methods, 1(1), 39–45. doi:10.1038/nmeth705Find this resource:

Wang, J., Tucholska, M., Knight, J. D., Lambert, J. P., Tate, S., Larsen, B., … Bandeira, N. (2015). MSPLIT-DIA: Sensitive peptide identification for data-independent acquisition. Nature Methods, 12(12), 1106–1108. doi:10.1038/nmeth.3655Find this resource:

Wang, X., Pandey, A. K., Mulligan, M. K., Williams, E. G., Mozhui, K., Li, Z., … Williams, R. W. (2016). Joint mouse-human phenome-wide association to test gene function and disease risk. Nature Communications, 7, 10464. doi:10.1038/ncomms10464Find this resource:

Wang, Y., Wu, J., Wu, Z., Lin, Q., Yue, Y., & Fang, L. (2010). Regulation of AMPA receptors in spinal nociception. Molecular Pain, 6, 5. doi:10.1186/1744-8069-6-5Find this resource:

Weng, H. J., Patel, K. N., Jeske, N. A., Bierbower, S. M., Zou, W., Tiwari, V., … Dong, X. (2015). Tmem100 is a regulator of TRPA1-TRPV1 complex and contributes to persistent pain. Neuron, 85(4), 833–846. doi:10.1016/j.neuron.2014.12.065Find this resource:

Williams, E. G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C. A., … Auwerx, J. (2016). Systems proteomics of liver mitochondria function. Science, 352(6291), aad0189. doi:10.1126/science.aad0189Find this resource:

Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., … Scalbert, A. (2013). HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Research, 41(Database issue), D801–D807. doi:10.1093/nar/gks1065Find this resource:

Woo, C. W., & Wager, T. D. (2015). Neuroimaging-based biomarker discovery and validation. Pain, 156(8), 1379–1381. doi:10.1097/j.pain.0000000000000223Find this resource:

Wu, Y., Williams, E. G., Dubuis, S., Mottis, A., Jovaisaite, V., Houten, S. M., … Aebersold, R. (2014). Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell, 158(6), 1415–1430. doi:10.1016/j.cell.2014.07.039Find this resource:

Zamboni, N., Saghatelian, A., & Patti, G. J. (2015). Defining the metabolome: Size, flux, and regulation. Molecular Cell, 58(4), 699–706. doi:10.1016/j.molcel.2015.04.021Find this resource:

Zhang, B., Li, H., Riggins, R. B., Zhan, M., Xuan, J., Zhang, Z., … Wang, Y. (2009). Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics, 25(4), 526–532. doi:10.1093/bioinformatics/btn660Find this resource:

Zhang, Y., Wang, Y. H., Zhang, X. H., Ge, H. Y., Arendt-Nielsen, L., Shao, J. M., & Yue, S. W. (2008). Proteomic analysis of differential proteins related to the neuropathic pain and neuroprotection in the dorsal root ganglion following its chronic compression in rats. Experimental Brain Research, 189(2), 199–209. doi:10.1007/s00221-008-1419-4Find this resource:

Zhang, Y. H., Vasko, M. R., & Nicol, G. D. (2002). Ceramide, a putative second messenger for nerve growth factor, modulates the TTX-resistant Na(+) current and delayed rectifier K(+) current in rat sensory neurons. Journal of Physiology, 544(Pt 2), 385–402.Find this resource:

Zhou, X., Menche, J., Barabasi, A. L., & Sharma, A. (2014). Human symptoms-disease network. Nature Communications, 5, 4212. doi:10.1038/ncomms5212Find this resource:

Zierer, J., Menni, C., Kastenmuller, G., & Spector, T. D. (2015). Integration of “omics” data in aging research: From biomarkers to systems biology. Aging Cell, 14(6), 933–944. doi:10.1111/acel.12386Find this resource:

Zorina-Lichtenwalter, K., Meloto, C. B., Khoury, S., & Diatchenko, L. (2016). Genetic predictors of human chronic pain conditions. Neuroscience, 338, 36–62. doi:10.1016/j.neuroscience.2016.04.041Find this resource:

Zub, K. A., Sousa, M. M., Sarno, A., Sharma, A., Demirovic, A., Rao, S., … Slupphaug, G. (2015). Modulation of cell metabolic pathways and oxidative stress signaling contribute to acquired melphalan resistance in multiple myeloma cells. PLoS One, 10(3), e0119857. doi:10.1371/journal.pone.0119857Find this resource: