title: Exploring novel paths towards protein signatures of chronic pain.
abstract: Pain is a major symptom of many medical conditions and the worldwide number one reason for people to seek medical assistance. It affects the quality of life of patients and poses a heavy financial burden on society with high costs of treatment and lost productivity. Furthermore, the treatment of chronic pain presents a big challenge as pain therapeutics often lack efficacy and exhibit minimal safety profiles. The latter can be largely attributed to the fact that current therapies target molecules with key physiological functions throughout the body. In light of these difficulties, the identification of proteins specifically involved in chronic pain states is of paramount importance for designing selective interventions. Several profiling efforts have been employed with the aim to dissect the molecular underpinnings of chronic pain, both on the level of the transcriptome and proteome. However, generated results are often inconsistent and non-overlapping, which is largely due to inherent technical constraints. A potential solution may be offered by emerging strategies capable of performing standardized and reproducible proteome analysis, such as data-independent acquisition-mass spectrometry (DIA-MS). We have recently demonstrated the applicability of DIA-MS to interrogate chronic pain-related proteome alterations in mice. Based on our results, we aim to provide an overview on DIA-MS and its potential to contribute to the comprehensive characterization of molecular signatures underlying pain pathologies.
authors.affiliation: Max-Planck Institute of Experimental Medicine, Göttingen, Germany.
authors.forename: David
authors.initials: D
authors.lastname: Gomez-Varela
authors.affiliation: Max-Planck Institute of Experimental Medicine, Göttingen, Germany mschmidt@em.mpg.de gomezvarela@em.mpg.de.
authors.forename: Manuela
authors.initials: M
authors.lastname: Schmidt
mesh_terms: D000818:Animals; D059350:Chronic Pain / Q000378:metabolism* / Q000473:pathology; D006801:Humans; D013058:Mass Spectrometry; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D040901:Proteomics; D012984:Software
chemical_list: D020543:Proteome
keywords: Chronic pain; data-independent acquisition-mass spectrometry; mass spectrometry; mouse models of chronic pain; protein signature; proteomics