Neuropeptidomics of the Mammalian Brain

  • Fang Xie
  • Krishna D. B. Anapindi
  • Elena V. Romanova
  • Jonathan V. SweedlerEmail author
Part of the Neuromethods book series (NM, volume 146)


A suite of bioactive peptides orchestrates a variety of cellular interactions in the mammalian brain. The bioanalytical strategy known as neuropeptidomics evolved from the quest to globally characterize these important cell–cell signaling peptides. The goal of a neuropeptidomics experiment is to characterize the peptides present in an intact brain, brain region, or even an individual neuron. A neuropeptidomics measurement needs to contend with the large dynamic range and low abundance of the neuropeptides that are present within a background of peptides resulting from the postmortem degradation of high-level ubiquitous proteins. The core components of a successful effort include effective tissue sampling and stabilization, sensitive and robust peptide characterization, and comprehensive data analysis and interpretation. Mass spectrometry (MS) has become the central analytical approach for high-throughput characterization of the brain peptidome because of its capability to detect, identify, and quantify known and unknown peptides with high confidence. Robust fractionation techniques, such as two-dimensional liquid chromatography (LC), are commonly used in conjunction with MS to enhance investigation of the peptidome. Identification and characterization of peptides is more complex when neuropeptide prohormone genes have not been annotated in an unbiased manner. This chapter outlines techniques and describes protocols for three different experimental designs that combine MS with LC, each aimed at high-throughput discovery of peptides in brain tissue. Further, we describe the currently available bioinformatics tools for automatic query of the experimental data against existing protein databases, manual retrieval of structural information from raw MS data, and label-free quantitation.


Neuropeptidome Hormone Neuropeptide Bioinformatics Liquid chromatography Mass spectrometry 



This work was supported by Award Number P30 DA018310 from the National Institute on Drug Abuse (NIDA), and by Award No. NS031609 from the National Institute of Neurological Disorders and Stroke (NINDS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Fang Xie
    • 1
  • Krishna D. B. Anapindi
    • 1
  • Elena V. Romanova
    • 1
  • Jonathan V. Sweedler
    • 1
    Email author
  1. 1.Department of Chemistry, Beckman InstituteUniversity of IllinoisUrbanaUSA

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