Advertisement

Proteomics of the Synapse – A Quantitative Approach to Neuronal Plasticity*

  • Daniela C. Dieterich
    Correspondence
    To whom correspondence should be addressed: Michael R. Kreutz,Leibniz-Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany, Tel.:0049-391-626394181, Daniela C. Dieterich,Institute for Pharmacology and Toxicology, Otto-von-Guericke University Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany, Tel.:0049-391-6715875
    Affiliations
    Institute for Pharmacology and Toxicology, Otto-von-Guericke University Magdeburg, Germany; Research Group Neuralomics, Leibniz Institute for Neurobiology Magdeburg, Germany;

    Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.
    Search for articles by this author
  • Michael R. Kreutz
    Correspondence
    To whom correspondence should be addressed: Michael R. Kreutz,Leibniz-Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany, Tel.:0049-391-626394181, Daniela C. Dieterich,Institute for Pharmacology and Toxicology, Otto-von-Guericke University Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany, Tel.:0049-391-6715875
    Affiliations
    RG Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany;

    Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.
    Search for articles by this author
  • Author Footnotes
    * The work in the lab of MRK was supported in recent years by grants from the Alexander-von-Humboldt Foundation (AvH), Bundesministerium für Forschung und Technologie (BMBF), Deutscher Akademischer Austauschdienst (DAAD), Deutsche Forschungsgemeinschaft (DFG), Leibniz Foundation (Pakt für Forschung), EU-FP7 MC-ITN NPlast, CBBS and Schram Foundation. Research in the Dieterich lab has been supported by grants from the Bundesministerium für Forschung und Technologie (BMBF), Deutsche Forschungsgemeinschaft (DFG), Leibniz Foundation (Pakt für Forschung), and the CBBS.
    1 The abbreviations used are:PSDpostsynaptic density2D-PAGEtwo dimensional polyacrylamide gel electrophoresisAHAazidohomoalanineAMPAα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acidAMPARα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptorANLazidonorleucineAQUAabsolute quantification (of proteins and peptides)BONCATbioorthogonal noncanonical amino acid taggingCAZcytomatrix of the active zoneCOINCorrelated optical and isotopic nanoscopyECMextracellular matrixFASSfluorescence activated synaptosome sortingFRAPfluorescence recovery after photobleachingFUNCATfluorescent noncanonical amino acid taggingGABAgamma aminobutyric acidGlyRglycine receptorHPGhomopropargylglycineICATisotope-coded affinity tagsiTRAQisobaric tags for relative and absolute quantificationLTPlong-term potentiationmGluRmetabotropic glutamate receptormRNAmessenger ribonucleic acidMSmass spectrometryNCAMneural cell adhesion moleculeN-GlcNAcN-linked-AcetylglucosamineNMDAN-Methyl-D-aspartic acidNMDARN-Methyl-D-aspartic acid receptorO-GlcNAcO-linked-AcetylglucosaminepolySiapolysialylatedPTMpost-translational modificationSILACstable isotope labeling by amino acids in cell cultureSILAMstable isotope labeling by amino acids in mammalsSIMSSecondary ion mass spectrometrySPILLspecific protein isotopic and fluorescence labelingtRNAtransfer ribonucleic acid.
Open AccessPublished:August 25, 2015DOI:https://doi.org/10.1074/mcp.R115.051482
      The advances in mass spectrometry based proteomics in the past 15 years have contributed to a deeper appreciation of protein networks and the composition of functional synaptic protein complexes. However, research on protein dynamics underlying core mechanisms of synaptic plasticity in brain lag far behind. In this review, we provide a synopsis on proteomic research addressing various aspects of synaptic function. We discuss the major topics in the study of protein dynamics of the chemical synapse and the limitations of current methodology. We highlight recent developments and the future importance of multidimensional proteomics and metabolic labeling. Finally, emphasis is given on the conceptual framework of modern proteomics and its current shortcomings in the quest to gain a deeper understanding of synaptic plasticity.
      Chemical synapses of the brain configure the most complex cell–cell junction of the body (Fig. 1). Proteomic studies have revealed that more than 2000 different proteins are found in preparations of forebrain synapses following biochemical purification (
      • Walikonis R.S.
      • Jensen O.N.
      • Mann M.
      • Provance Jr., D.W.
      • Mercer J.A.
      • Kennedy M.B.
      Identification of proteins in the postsynaptic density fraction by mass spectrometry.
      ,
      • Satoh K.
      • Takeuchi M.
      • Oda Y.
      • Deguchi-Tawarada M.
      • Sakamoto Y.
      • Matsubara K.
      • Nagasu T.
      • Takai Y.
      Identification of activity-regulated proteins in the postsynaptic density fraction.
      ,
      • Jordan B.A.
      • Fernholz B.D.
      • Boussac M.
      • Xu C.
      • Grigorean G.
      • Ziff E.B.
      • Neubert T.A.
      Identification and verification of novel rodent postsynaptic density proteins.
      ,
      • Li K.W.
      • Hornshaw M.P.
      • Van Der Schors R.C.
      • Watson R.
      • Tate S.
      • Casetta B.
      • Jimenez C.R.
      • Gouwenberg Y.
      • Gundelfinger E.D.
      • Smalla K.H.
      • Smit A.B.
      Proteomics analysis of rat brain postsynaptic density. Implications of the diverse protein functional groups for the integration of synaptic physiology.
      ,
      • Li K.
      • Hornshaw M.P.
      • Van Minnen J.
      • Smalla K.H.
      • Gundelfinger E.D.
      • Smit A.B.
      Organelle proteomics of rat synaptic proteins: correlation-profiling by isotope-coded affinity tagging in conjunction with liquid chromatography-tandem mass spectrometry to reveal postsynaptic density specific proteins.
      ,
      • Peng J.
      • Kim M.J.
      • Cheng D.
      • Duong D.M.
      • Gygi S.P.
      • Sheng M.
      Semiquantitative proteomic analysis of rat forebrain postsynaptic density fractions by mass spectrometry.
      ,
      • Yoshimura Y.
      • Yamauchi Y.
      • Shinkawa T.
      • Taoka M.
      • Donai H.
      • Takahashi N.
      • Isobe T.
      • Yamauchi T.
      Molecular constituents of the postsynaptic density fraction revealed by proteomic analysis using multidimensional liquid chromatography-tandem mass spectrometry.
      ,
      • Liu S.H.
      • Cheng H.H.
      • Huang S.Y.
      • Yiu P.C.
      • Chang Y.C.
      Studying the protein organization of the postsynaptic density by a novel solid phase- and chemical cross-linking-based technology.
      ,
      • Collins M.O.
      • Husi H.
      • Yu L.
      • Brandon J.M.
      • Anderson C.N.
      • Blackstock W.P.
      • Choudhary J.S.
      • Grant S.G.
      Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome.
      ,
      • Cheng D.
      • Hoogenraad C.C.
      • Rush J.
      • Ramm E.
      • Schlager M.A.
      • Duong D.M.
      • Xu P.
      • Wijayawardana S.R.
      • Hanfelt J.
      • Nakagawa T.
      • Sheng M.
      • Peng J.
      Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum.
      ,
      • Distler U.
      • Schmeisser M.J.
      • Pelosi A.
      • Reim D.
      • Kuharev J.
      • Weiczner R.
      • Baumgart J.
      • Boeckers T.M.
      • Nitsch R.
      • Vogt J.
      • Tenzer S.
      In-depth protein profiling of the postsynaptic density from mouse hippocampus using data-independent acquisition proteomics.
      ,
      • Bayés A.
      • van de Lagemaat L.N.
      • Collins M.O.
      • Croning M.D.
      • Whittle I.R.
      • Choudhary J.S.
      • Grant S.G.
      Characterization of the proteome, diseases and evolution of the human postsynaptic density.
      ). This complex proteome creates a challenge for future research not only from a methodological point of view but also with respect to the molecular dynamics of protein exchange at this cell–cell junction. The activity-dependent association of proteins with synaptic junctions imposes fundamental questions about synaptic function that are the driving force for most of the proteomic research done so far. It is widely believed that synapses in the forebrain undergo structural and functional changes, a phenomenon called synaptic plasticity, that underlies learning and memory processes. The synaptic basis of memory formation is still far from being understood but compelling evidence suggests that activity-dependent alterations in the molecular composition of the synapse are a key mechanism for synaptic plasticity (
      • Caroni P.
      • Donato F.
      • Muller D.
      Structural plasticity upon learning: regulation and functions.
      ,
      • Hanus C.
      • Schuman E.M.
      Proteostasis in complex dendrites.
      ,
      • Ebrahimi S.
      • Okabe S.
      Structural dynamics of dendritic spines: molecular composition, geometry, and functional regulation.
      ,
      • Rosenberg T.
      • Gal-Ben-Ari S.
      • Dieterich D.C.
      • Kreutz M.R.
      • Ziv N.E.
      • Gundelfinger E.D.
      • Rosenblum K.
      The roles of protein expression in synaptic plasticity and memory consolidation.
      ,
      • Sala C.
      • Segal M.
      Dendritic spines: the locus of structural and functional plasticity.
      ). However, to date the evidence for this notion is still largely based on the study of individual proteins and efforts to overcome these limitations with an unbiased large-scale proteomic approach have been facing several constraints that we will discuss in this review.
      Figure thumbnail gr1
      Fig. 1.Electron micrograph of rat cortex showing multiple pre- and postsynaptic structures, as well as astrocytic endfeet (*) in close contact with synapses. Note the presence of numerous synaptic vesicles in the presynaptic boutons. CAZ, cytomatrix at the active zone; PSD, postsynaptic density; SV, synaptic vesicles. Scalebar: 100 nm.
      Neurons are highly polarized cells and the number of synapses is typically huge, their molecular makeup extraordinarily complex, and their distance from the cell body, where most protein synthesis occurs, can be enormous for both their dendritic and axonal processes. Neurons have therefore adopted a number of strategies to enable local and rapid changes in proteostasis and very recent research suggests that satellite synapto-dendritic organelles, that allow for protein synthesis, modification, degradation, and that give rise to highly specialized vesicles, are important for synaptic function (Fig. 2) (
      • Hanus C.
      • Schuman E.M.
      Proteostasis in complex dendrites.
      ,
      • Hanus C.
      • Ehlers M.D.
      Secretory outposts for the local processing of membrane cargo in neuronal dendrites.
      ,
      • Ehlers M.D.
      Dendritic trafficking for neuronal growth and plasticity.
      ,
      • Maeder C.I.
      • Shen K.
      • Hoogenraad C.C.
      Axon and dendritic trafficking.
      ). The bewildering complexity in the interplay between local and somatic protein synthesis, mRNA and protein transport, protein modifications including, phosphorylation, acetylation, methylation, tyrosination, glutamylation, lipidation, and glycosylation as well as local protein degradation allows for a tightly controlled supply and removal of synaptic proteins. In principal, these interconnected machineries can give rise to a high molecular diversity in the synaptic protein make-up but the study of this question is still in its infancy (
      • O'Rourke N.A.
      • Weiler N.C.
      • Micheva K.D.
      • Smith S.J.
      Deep molecular diversity of mammalian synapses: why it matters and how to measure it.
      ,
      • Busse B.
      • Smith S.
      Automated analysis of a diverse synapse population.
      ; see also below).
      Figure thumbnail gr2
      Fig. 2.The tetrapartite synapse of principal neurons in the forebrain, consisting of the pre- and postsynaptic compartment, astrocytic endfeet, and the extracellular matrix has a tightly regulated protein composition. A microsceretory system is present in synapses and dendrites that allows for translation of mRNA, local synthesis of, processing and insertion of transmembrane proteins. Hence the turnover of the synaptic protein machinery is controlled by local and somatic de novo protein synthesis, protein degradation by the ubiquitin proteasome system, lysosomes and autophagosomes. In addition, the association of proteins with pre- and postsynaptic compartments is highly dynamic. Molecular machineries and organelles for proteostasis are shared between synapses in dendritic segments. Proteins are transported in and out of the synapse as well as by diffusion of transmembrane proteins. These processes govern the activity-dependent assembly of the pre- and postsynaptic scaffold and the synaptic surface expression of receptors, calcium channels and cell adhesion molecules. Abbreviations: CAM, cell adhesion molecules; CAZ, cytomatrix at the active zone; ECM, extracellular matrix; ER, endoplasmatic reticulum; ERGIC, endoplasmatic reticulum Golgi intermediate compartment; MT, microtubules; PSD, postsynaptic density; RE, recycling endosomes; Lys, lysomes; SV, synaptic vesicle.

      The Molecular Dynamics of Dendritic Spines

      In the human forebrain dendritic spines harbor the most abundant synapse type (Fig. 1., Fig. 2.). They are found on excitatory principal neurons of the cortex and hippocampus as well as on GABAergic medium spiny neurons of the striatum, and a single pyramidal neuron can be equipped with up to 10,000 of these synaptic contacts (
      • Sorra K.E.
      • Harris K.M.
      Overview on the structure, composition, function, development, and plasticity of hippocampal dendritic spines.
      ,
      • Carlisle H.J.
      • Kennedy M.B.
      Spine architecture and synaptic plasticity.
      ). Interestingly, up to 60% of hippocampal spine synapses are in tight contact with astrocytic endfeet (
      • Ventura R.
      • Harris K.M.
      Three-dimensional relationships between hippocampal synapses and astrocytes.
      ) and engulfed with a complex extracellular matrix, which has led to the concept of the tri- and tetrapartite synapse (
      • Rosenberg T.
      • Gal-Ben-Ari S.
      • Dieterich D.C.
      • Kreutz M.R.
      • Ziv N.E.
      • Gundelfinger E.D.
      • Rosenblum K.
      The roles of protein expression in synaptic plasticity and memory consolidation.
      ,
      • Sala C.
      • Segal M.
      Dendritic spines: the locus of structural and functional plasticity.
      ). Spines are membranous protrusions from neuronal dendrites and it is widely believed that spines are semi-autonomous biochemical microcompartments separated from the dendritic shaft by the spine neck (Fig. 1., Fig. 2.). Spine synapses allow for compartmentalization of postsynaptic Ca2+ responses (
      • Higley M.J.
      • Sabatini B.L.
      Calcium signaling in dendritic spines.
      ,
      • Raghuram V.
      • Sharma Y.
      • Kreutz M.R.
      Ca(2+) sensor proteins in dendritic spines: a race for Ca(2+).
      ) and they can continuously change their shape and volume and thus adapt to requirements of the synaptic contact (
      • Ebrahimi S.
      • Okabe S.
      Structural dynamics of dendritic spines: molecular composition, geometry, and functional regulation.
      ,
      • Sala C.
      • Segal M.
      Dendritic spines: the locus of structural and functional plasticity.
      ,
      • Noguchi J.
      • Matsuzaki M.
      • Ellis-Davies G.C.
      • Kasai H.
      Spine-neck geometry determines NMDA receptor-dependent Ca2+ signaling in dendrites.
      ,
      • Grunditz A.
      • Holbro N.
      • Tian L.
      • Zuo Y.
      • Oertner T.G.
      Spine neck plasticity controls postsynaptic calcium signals through electrical compartmentalization.
      ,
      • Colgan L.A.
      • Yasuda R.
      Plasticity of dendritic spines: subcompartmentalization of signaling.
      ). An important role for the topology of the spine synapse is played by an electron-dense structure beneath the postsynaptic membrane termed postsynaptic density (PSD)
      The abbreviations used are:
      PSD
      postsynaptic density
      2D-PAGE
      two dimensional polyacrylamide gel electrophoresis
      AHA
      azidohomoalanine
      AMPA
      α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
      AMPAR
      α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor
      ANL
      azidonorleucine
      AQUA
      absolute quantification (of proteins and peptides)
      BONCAT
      bioorthogonal noncanonical amino acid tagging
      CAZ
      cytomatrix of the active zone
      COIN
      Correlated optical and isotopic nanoscopy
      ECM
      extracellular matrix
      FASS
      fluorescence activated synaptosome sorting
      FRAP
      fluorescence recovery after photobleaching
      FUNCAT
      fluorescent noncanonical amino acid tagging
      GABA
      gamma aminobutyric acid
      GlyR
      glycine receptor
      HPG
      homopropargylglycine
      ICAT
      isotope-coded affinity tags
      iTRAQ
      isobaric tags for relative and absolute quantification
      LTP
      long-term potentiation
      mGluR
      metabotropic glutamate receptor
      mRNA
      messenger ribonucleic acid
      MS
      mass spectrometry
      NCAM
      neural cell adhesion molecule
      N-GlcNAc
      N-linked-Acetylglucosamine
      NMDA
      N-Methyl-D-aspartic acid
      NMDAR
      N-Methyl-D-aspartic acid receptor
      O-GlcNAc
      O-linked-Acetylglucosamine
      polySia
      polysialylated
      PTM
      post-translational modification
      SILAC
      stable isotope labeling by amino acids in cell culture
      SILAM
      stable isotope labeling by amino acids in mammals
      SIMS
      Secondary ion mass spectrometry
      SPILL
      specific protein isotopic and fluorescence labeling
      tRNA
      transfer ribonucleic acid.
      (Fig. 1., Fig. 2.). The PSD consists of a specialized and elaborate molecular scaffold, which organizes the neurotransmitter receptive apparatus, the adhesion of the postsynapse to presynaptic terminals (Fig. 1., Fig. 2.) and that links synaptic neurotransmission to various signaling cascades and the actin cytoskeleton (
      • Caroni P.
      • Donato F.
      • Muller D.
      Structural plasticity upon learning: regulation and functions.
      ,
      • Ebrahimi S.
      • Okabe S.
      Structural dynamics of dendritic spines: molecular composition, geometry, and functional regulation.
      ,
      • Sala C.
      • Segal M.
      Dendritic spines: the locus of structural and functional plasticity.
      ,
      • Colgan L.A.
      • Yasuda R.
      Plasticity of dendritic spines: subcompartmentalization of signaling.
      ,
      • Sheng M.
      • Hoogenraad C.C.
      The postsynaptic architecture of excitatory synapses: a more quantitative view.
      ,
      • Sheng M.
      • Kim E.
      The postsynaptic organization of synapses.
      ). The spine head is highly enriched in filamentous actin (F-actin), which mediates rapid motility and morphological alterations of spines (
      • Fischer M.
      • Kaech S.
      • Knutti D.
      • Matus A.
      Rapid actin-based plasticity in dendritic spines.
      ,
      • Hotulainen P.
      • Hoogenraad C.C.
      Actin in dendritic spines: connecting dynamics to function.
      ,
      • Dent E.W.
      • Merriam E.B.
      • Hu X
      The dynamic cytoskeleton: backbone of dendritic spine plasticity.
      ). F-actin itself is linked to the PSD via a number of protein interactions of actin-binding proteins including cortactin, ABP1, or α-Fodrin (
      • Hotulainen P.
      • Hoogenraad C.C.
      Actin in dendritic spines: connecting dynamics to function.
      ). Early FRAP-experiments indicate that 85% of filamentous actin in dendritic spines is dynamic, with an average cycle time of 44 s (
      • Star E.N.
      • Kwiatkowski D.J.
      • Murthy V.N.
      Rapid turnover of actin in dendritic spines and its regulation by activity.
      ). A plethora of studies has shown that polymerization of actin is controlled by synaptic activity and it is thought that the interface between F-actin and the PSD is crucial for the molecular dynamics of the spine (
      • Carlisle H.J.
      • Kennedy M.B.
      Spine architecture and synaptic plasticity.
      ,
      • Hotulainen P.
      • Hoogenraad C.C.
      Actin in dendritic spines: connecting dynamics to function.
      ,
      • Oertner T.G.
      • Matus A.
      Calcium regulation of actin dynamics in dendritic spines.
      ). At present it is not completely understood which regulatory events control these dynamics and it will be rather difficult to pinpoint the molecular interface of this key plasticity event with current proteomic technology (see also below).
      The presynaptic counterpart of the PSD is the cytomatrix of the active zone (CAZ) of neurotransmitter release (Fig. 1., Fig. 2.). The CAZ is like the PSD an electron dense area close to the membrane (Fig. 1., Fig. 2.). Protein components of the CAZ tether synaptic vesicles to the presynaptic membrane and mediate synaptic vesicle fusion, thereby allowing neurotransmitter to be released reliably and rapidly when an action potential arrives (
      • Gundelfinger E.D.
      • Fejtova A.
      Molecular organization and plasticity of the cytomatrix at the active zone.
      ,
      • Sigrist S.J.
      • Schmitz D.
      Structural and functional plasticity of the cytoplasmic active zone.
      ). The protein composition of the CAZ has been elucidated in much detail and the topology of this structure is arguably better understood than those of the PSD. Protein components of the CAZ and the PSD cannot be separated by conventional proteomic approaches, which is because of the purification schemes employed in these studies (see below/Fig. 3) (Table I).
      Figure thumbnail gr3
      Fig. 3.Workflow for common brain and synapse proteomics approaches tackling proteins, glycans, lipids, and phosphorylation sites of synaptic proteins, as well as receptor complexes. Synaptic fractions such as synaptosomes, CAZ, and the detergent extracted PSD are prepared using sucrose or Percoll density centrifugations followed by subsequent specific MS analyses. Dashed lining indicates further necessary processing of fractions to analyze specific subproteomes such as the lipidome, the phosphoproteome, or the different glycoproteomes. In case of global brain lipidome analysis or interactome analyses of receptor complexes organic solvents or mild detergents, respectively, are used for extraction from homogenates prior to further processing and MS analysis. IP, immunoprecipitation; AC, affinity chromatography; CAZ, cytomatrix of the active zone; PSD, postsynaptic density.
      Table I
      Synaptic subproteomes and their preparationDescriptionApprox. number of unique proteinsSelected Literature
      Postsynaptic density (PSD) proteomeThe PSD is defined at the ultrastructural level as electron-dense material associated with the postsynaptic membrane. Core PSD proteins and their associated partners have been identified in synaptic junctional protein preparations (“PSD preparations” using detergents including Triton X-100, DOC and SDS. In addition to postsynaptic proteins, components of the CAZ, glial endfeet and ECM proteins are found in “PSD preparations” due to either similar biochemical features or general stickiness to membranous preparations.Ca. 1,500(
      • Gray E.G.
      Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscope study.
      ,
      • Peters A.
      • Palay S.L.
      The morphology of synapses.
      • Ziff E.B.
      Enlightening the postsynaptic density.
      )
      Presynaptic cytomatrix at the active zone (CAZ) proteomeElectron-dense presynaptic counterpart of the PSD containing scaffolding molecules and the vesicle release machinery. Scaffolding components co-purify in PSD preparations.Ca. 500(
      • Peters A.
      • Palay S.L.
      The morphology of synapses.
      ,
      • Garner C.C.
      • Kindler S.
      • Gundelfinger E.D.
      Molecular determinants of presynaptic active zones.
      ,
      • Phillips G.R.
      • Huang J.K.
      • Wang Y.
      • Tanaka H.
      • Shapiro L.
      • Zhang W.
      • Shan W.S.
      • Arndt K.
      • Frank M.
      • Gordon R.E.
      • Gawinowicz M.A.
      • Zhao Y.
      • Colman D.R.
      The presynaptic particle web: ultrastructure, composition, dissolution, and reconstitution.
      ,
      • Weingarten J.
      • Lassek M.
      • Mueller B.F.
      • Rohmer M.
      • Lunger I.
      • Baeumlisberger D.
      • Dudek S.
      • Gogesch P.
      • Karas M.
      • Volknandt W.
      The proteome of the presynaptic active zone from mouse brain.
      )
      SynaptosomesAlso called synaptic membranes, collected after sucrose density centrifugation at the 1,0/1,2 m interface. Contain presynaptic, postsynaptic as well as astroglial and ECM components. Recent technical advancement: transmitter-specific synaptosomes can be isolated from knock-in mice carrying fluorescently labeled core components such as VGLUT1 for glutamatergic synapses (FASS methodology).Ca. 6,620 in total; 163 enriched proteins via VGLUT1-FASS analysis(
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ,
      • Biesemann C.
      • Grønborg M.
      • Luquet E.
      • Wichert S.P.
      • Bernard V.
      • Bungers S.R.
      • Cooper B.
      • Varoqueaux F.
      • Li L.
      • Byrne J.A.
      • Urlaub H.
      • Jahn O.
      • Brose N.
      • Herzog E.
      Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting.
      )
      SynaptoneurosomesCrude synaptosomes including also dendritic components are prepared using either mesh filters right after homogenization of brain material to separate nuclei and large cellular debris, or brief sucrose density centrifugation.uncertain(e.g.
      • Corena-McLeod M.
      • Walss-Bass C.
      • Oliveros A.
      • Gordillo Villegas A.
      • Ceballos C.
      • Charlesworth C.M.
      • Madden B.
      • Linser P.J.
      • Van Ekeris L.
      • Smith K.
      • Richelson E.
      New model of action for mood stabilizers: phosphoproteome from rat prefrontal cortex synaptoneurosomal preparations.
      ,
      • Liao L.
      • Park S.K.
      • Xu T.
      • Vanderklish P.
      • Yates 3rd., J.R.
      Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice.
      • Liao L.
      • Pilotte J.
      • Xu T.
      • Wong C.C.
      • Edelman G.M.
      • Vanderklish P.
      • Yates 3rd., J.R.
      BDNF induces widespread changes in synaptic protein content and up-regulates components of the translation machinery: an analysis using high-throughput proteomics.
      )
      Glial endfeet proteomeAstroglial endfeet are in close contact with spine synapses and components, therefore, copurify in PSD, and synaptosome as well as synaptoneurosome preparations.unknown(
      • Araque A.
      • Parpura V.
      • Sanzgiri R.P.
      • Haydon P.G.
      Tripartite synapses: glia, the unacknowledged partner.
      ,
      • Faissner A.
      • Pyka M.
      • Geissler M.
      • Sobik T.
      • Frischknecht R.
      • Gundelfinger E.D.
      • Seidenbecher C.
      Contributions of astrocytes to synapse formation and maturation – potential functions of the perisynaptic extracellular matrix.
      )
      Extracellular matrix (ECM) proteomeECM components located perisynaptically and within synaptic clefts are released by astrocytes and neurons and are tightly associated with the synapse. ECM components are found as co-purified proteins in “PSD preparations”.unknown(
      • Faissner A.
      • Pyka M.
      • Geissler M.
      • Sobik T.
      • Frischknecht R.
      • Gundelfinger E.D.
      • Seidenbecher C.
      Contributions of astrocytes to synapse formation and maturation – potential functions of the perisynaptic extracellular matrix.
      ,
      • Zuber B.
      • Nikonenko I.
      • Klauser P.
      • Muller D.
      • Dubochet J.
      The mammalian central nervous synaptic cleft contains a high density of periodically organized complexes.
      • Dityatev A.
      • Seidenbecher C.I.
      • Schachner M.
      Compartmentalization from the outside: the extracellular matrix and functional microdomains in the brain.
      )
      Synaptic phosphoproteomePhosphoproteins or -peptides are enriched from PSD, synaptosome or synaptoneurosome preparations using affinity resins such as TiO2 beads.Ca. 16,500 phospho-sites(
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      )
      brain lipidomeIsolated from synaptic membrane preparations followed by high Triton X-100 extraction.Ca. 2,850 in human brain(
      • Bozek K.
      • Wei Y.
      • Yan Z.
      • Liu X.
      • Xiong J.
      • Sugimoto M.
      • Tomita M.
      • Paabo S.
      • Sherwood C.C.
      • Hof P.R.
      • Ely J.J.
      • Li Y.
      • Steinhauser D.
      • Willmitzer L.
      • Giavalisco P.
      • Khaitovich P.
      Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues.
      )
      Synaptic glycoproteomesO- and N-glycoproteomes are enriched from detergent extracted synaptosomes using lectin affinity purifications.Ca. 1,260 O-glycoproteins, ca., 1,300 N-glycoproteins(
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ,
      • Zielinska D.F.
      • Gnad F.
      • Wisniewski J.R.
      • Mann M.
      Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints.
      )
      Receptor complexes / interactomesReceptor or scaffolding molecule specific antibodies are used to isolate respective complexes from brain extracts or synaptosome extracts.NMDAR complex: 77 proteins; AMPAR complex: 34 proteins; PSD95 complex: 118 proteins(
      • Husi H.
      • Ward M.A.
      • Choudhary J.S.
      • Blackstock W.P.
      • Grant S.G.
      Proteomic analysis of NMDA receptor-adhesion protein signaling complexes.
      ,
      • Schwenk J.
      • Harmel N.
      • Brechet A.
      • Zolles G.
      • Berkefeld H.
      • Müller C.S.
      • Bildl W.
      • Baehrens D.
      • Hüber B.
      • Kulik A.
      • Klöcker N.
      • Schulte U.
      • Fakler B.
      High-resolution proteomics unravel architecture and molecular diversity of native AMPA receptor complexes.
      )
      Experimental studies from the past 15 years have identified more than 2000 components of the synapse and this work has set ground to put the building blocks of synaptic neurotransmission together (
      • Ebrahimi S.
      • Okabe S.
      Structural dynamics of dendritic spines: molecular composition, geometry, and functional regulation.
      ,
      • Sala C.
      • Segal M.
      Dendritic spines: the locus of structural and functional plasticity.
      ,
      • Sheng M.
      • Kim E.
      The postsynaptic organization of synapses.
      ,
      • Bayés A.
      • Grant S.G.
      Neuroproteomics: understanding the molecular organization and complexity of the brain.
      ,
      • Emes R.D.
      • Pocklington A.J.
      • Anderson C.N.
      • Bayés A.
      • Collins M.O.
      • Vickers C.A.
      • Croning M.D.
      • Malik B.R.
      • Choudhary J.S.
      • Armstrong J.D.
      • Grant S.G.
      Evolutionary expansion and anatomical specialization of synapse proteome complexity.
      ,
      • Laβek M.
      • Weingarten J.
      • Volknandt W.
      The synaptic proteome.
      ,
      • Weingarten J.
      • Lassek M.
      • Mueller B.F.
      • Rohmer M.
      • Lunger I.
      • Baeumlisberger D.
      • Dudek S.
      • Gogesch P.
      • Karas M.
      • Volknandt W.
      The proteome of the presynaptic active zone from mouse brain.
      ). Our knowledge about the protein composition of the synapse is based to a large extent on this pioneering work. Converging targeted and shotgun proteomic approaches have shed light on the protein composition of synaptic vesicles (
      • Takamori S.
      • Holt M.
      • Stenius K.
      • Lemke E.A.
      • Grønborg M.
      • Riedel D.
      • Urlaub H.
      • Schenck S.
      • Brügger B.
      • Ringler P.
      • Müller S.A.
      • Rammner B.
      • Gräter F.
      • Hub J.S.
      • De Groot B.L.
      • Mieskes G.
      • Moriyama Y.
      • Klingauf J.
      • Grubmüller H.
      • Heuser J.
      • Wieland F.
      • Jahn R.
      Molecular anatomy of a trafficking organelle.
      ,
      • Grønborg M.
      • Pavlos N.J.
      • Brunk I.
      • Chua J.J.
      • Münster-Wandowski A.
      • Riedel D.
      • Ahnert-Hilger G.
      • Urlaub H.
      • Jahn R.
      Quantitative comparison of glutamatergic and GABAergic synaptic vesicles unveils selectivity for few proteins including MAL2, a novel synaptic vesicle protein.
      ), biochemically isolated fractions highly enriched in docked synaptic vesicles (
      • Boyken J.
      • Grønborg M.
      • Riedel D.
      • Urlaub H.
      • Jahn R.
      • Chua J.J.
      Molecular profiling of synaptic vesicle docking sites reveals novel proteins but few differences between glutamatergic and GABAergic synapses.
      ), single CNS synapse types (
      • Selimi F.
      • Cristea I.M.
      • Heller E.
      • Chait B.T.
      • Heintz N.
      Proteomic studies of a single CNS synapse type: the parallel fiber/purkinje cell synapse.
      ), excitatory receptor complexes like the N-Methyl-d-Aspartate-Receptor (NMDAR)- (
      • Husi H.
      • Ward M.A.
      • Choudhary J.S.
      • Blackstock W.P.
      • Grant S.G.
      Proteomic analysis of NMDA receptor-adhesion protein signaling complexes.
      ), metabotropic glutamate receptor (mGluR5) (
      • Farr C.D.
      • Gafken P.R.
      • Norbeck A.D.
      • Doneanu C.E.
      • Stapels M.D.
      • Barofsky D.F.
      • Minami M.
      • Saugstad J.A.
      Proteomic analysis of native metabotropic glutamate receptor 5 protein complexes reveals novel molecular constituents.
      ), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) (
      • Fukata Y.
      • Tzingounis A.V.
      • Trinidad J.C.
      • Fukata M.
      • Burlingame A.L.
      • Nicoll R.A.
      • Bredt D.S.
      Molecular constituents of neuronal AMPA receptors.
      ,
      • von Engelhardt J.
      • Mack V.
      • Sprengel R.
      • Kavenstock N.
      • Li K.W.
      • Stern-Bach Y.
      • Smit A.B.
      • Seeburg P.H.
      • Monyer H.
      CKAMP44: a brain-specific protein attenuating short-term synaptic plasticity in the dentate gyrus.
      ,
      • Schwenk J.
      • Harmel N.
      • Brechet A.
      • Zolles G.
      • Berkefeld H.
      • Müller C.S.
      • Bildl W.
      • Baehrens D.
      • Hüber B.
      • Kulik A.
      • Klöcker N.
      • Schulte U.
      • Fakler B.
      High-resolution proteomics unravel architecture and molecular diversity of native AMPA receptor complexes.
      ,
      • Schwenk J.
      • Baehrens D.
      • Haupt A.
      • Bildl W.
      • Boudkkazi S.
      • Roeper J.
      • Fakler B.
      • Schulte U.
      Regional diversity and developmental dynamics of the AMPA-receptor proteome in the mammalian brain.
      ), glycine receptor β subunit (GlyRβ)-interacting proteins (
      • Del Pino I.
      • Koch D.
      • Schemm R.
      • Qualmann B.
      • Betz H.
      • Paarmann I.
      Proteomic analysis of glycine receptor β subunit (GlyRβ)-interacting proteins: evidence for syndapin I regulating synaptic glycine receptors.
      ), synaptic cell adhesion molecules (
      • Tanaka H.
      • Takafuji K.
      • Taguchi A.
      • Wiriyasermkul P.
      • Ohgaki R.
      • Nagamori S.
      • Suh P.G.
      • Kanai Y.
      Linkage of N-cadherin to multiple cytoskeletal elements revealed by a proteomic approach in hippocampal neurons.
      ,
      • Kang Y.
      • Ge Y.
      • Cassidy R.M.
      • Lam V.
      • Luo L.
      • Moon K.M.
      • Lewis R.
      • Molday R.S.
      • Wong R.O.
      • Foster L.J.
      • Craig A.M.
      A combined transgenic proteomic analysis and regulated trafficking of neuroligin-2.
      ), and the major constituents of the pre- and postsynaptic scaffold (
      • Dosemeci A.
      • Makusky A.J.
      • Jankowska-Stephens E.
      • Yang X.
      • Slotta D.J.
      • Markey S.P.
      Composition of the synaptic PSD-95 complex.
      ,
      • Fernández E.
      • Collins M.O.
      • Uren R.T.
      • Kopanitsa M.V.
      • Komiyama N.H.
      • Croning M.D.
      • Zografos L.
      • Armstrong J.D.
      • Choudhary J.S.
      • Grant S.G.
      Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins.
      ,
      • Klemmer P.
      • Smit A.B.
      • Li K.W.
      Proteomics analysis of immuno-precipitated synaptic protein complexes.
      ,
      • Reissner K.J.
      • Uys J.D.
      • Schwacke J.H.
      • Comte-Walters S.
      • Rutherford-Bethard J.L.
      • Dunn T.E.
      • Blumer J.B.
      • Schey K.L.
      • Kalivas P.W.
      AKAP signaling in reinstated cocaine seeking revealed by iTRAQ proteomic analysis.
      ). In case of the NMDAR initially a multiprotein complex with 77 proteins was identified that contained several kinases and phosphates and other signaling components (
      • Husi H.
      • Ward M.A.
      • Choudhary J.S.
      • Blackstock W.P.
      • Grant S.G.
      Proteomic analysis of NMDA receptor-adhesion protein signaling complexes.
      ). This seminal study was followed by other approaches to identify AMPAR subcomplexes with interaction proteomics (
      • Fukata Y.
      • Tzingounis A.V.
      • Trinidad J.C.
      • Fukata M.
      • Burlingame A.L.
      • Nicoll R.A.
      • Bredt D.S.
      Molecular constituents of neuronal AMPA receptors.
      ,
      • von Engelhardt J.
      • Mack V.
      • Sprengel R.
      • Kavenstock N.
      • Li K.W.
      • Stern-Bach Y.
      • Smit A.B.
      • Seeburg P.H.
      • Monyer H.
      CKAMP44: a brain-specific protein attenuating short-term synaptic plasticity in the dentate gyrus.
      ,
      • Schwenk J.
      • Harmel N.
      • Brechet A.
      • Zolles G.
      • Berkefeld H.
      • Müller C.S.
      • Bildl W.
      • Baehrens D.
      • Hüber B.
      • Kulik A.
      • Klöcker N.
      • Schulte U.
      • Fakler B.
      High-resolution proteomics unravel architecture and molecular diversity of native AMPA receptor complexes.
      ,
      • Schwenk J.
      • Baehrens D.
      • Haupt A.
      • Bildl W.
      • Boudkkazi S.
      • Roeper J.
      • Fakler B.
      • Schulte U.
      Regional diversity and developmental dynamics of the AMPA-receptor proteome in the mammalian brain.
      ,
      • Chen N.
      • Pandya N.J.
      • Koopmans F.
      • Castelo-Székelv V.
      • van der Schors R.C.
      • Smit A.B.
      • Li K.W.
      Interaction proteomics reveals brain region-specific AMPA receptor complexes.
      ). Important auxiliary subunits that regulate AMPAR function were identified by mass spectrometry (MS) analysis of purified complexes. In recent work, using an elegant combination of immunoprecipitation with a series of antibodies directed against AMPAR subunits, extensive validation and isolation of a AMPAR complex in native blue gel electrophoresis of roughly between 0.6 to 1 MDa, Schwenk et al. (
      • Schwenk J.
      • Harmel N.
      • Brechet A.
      • Zolles G.
      • Berkefeld H.
      • Müller C.S.
      • Bildl W.
      • Baehrens D.
      • Hüber B.
      • Kulik A.
      • Klöcker N.
      • Schulte U.
      • Fakler B.
      High-resolution proteomics unravel architecture and molecular diversity of native AMPA receptor complexes.
      ) could verify 34 proteins as part of distinct AMPAR complexes with different stability. This approach was then extended to different brain regions and developmental stages, where it was found that AMPAR complexes significantly differ in their protein constituents across postnatal development and between brain regions (
      • Schwenk J.
      • Baehrens D.
      • Haupt A.
      • Bildl W.
      • Boudkkazi S.
      • Roeper J.
      • Fakler B.
      • Schulte U.
      Regional diversity and developmental dynamics of the AMPA-receptor proteome in the mammalian brain.
      ,
      • Chen N.
      • Pandya N.J.
      • Koopmans F.
      • Castelo-Székelv V.
      • van der Schors R.C.
      • Smit A.B.
      • Li K.W.
      Interaction proteomics reveals brain region-specific AMPA receptor complexes.
      ). An ultimate question that arises is how a functional complex is defined and how large such a complex might be. Interestingly enough, using transgenic mice expressing TAP-tagged PSD-95, the probably most abundant postsynaptic scaffolding molecule (
      • Sheng M.
      • Hoogenraad C.C.
      The postsynaptic architecture of excitatory synapses: a more quantitative view.
      ,
      • Sheng M.
      • Kim E.
      The postsynaptic organization of synapses.
      ,
      • Okabe S.
      Molecular anatomy of the postsynaptic density.
      ), a complex of 118 proteins was identified (
      • Dosemeci A.
      • Makusky A.J.
      • Jankowska-Stephens E.
      • Yang X.
      • Slotta D.J.
      • Markey S.P.
      Composition of the synaptic PSD-95 complex.
      ). In an earlier study employing immunopurification of PSD-95 with a specific antibody 26 major components of the PSD-95 complex were identified with relatively little overlap apart from NMDAR and AMPAR subunits to the study of Fernandez et al. (
      • Fernández E.
      • Collins M.O.
      • Uren R.T.
      • Kopanitsa M.V.
      • Komiyama N.H.
      • Croning M.D.
      • Zografos L.
      • Armstrong J.D.
      • Choudhary J.S.
      • Grant S.G.
      Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins.
      ). This variability probably reflects the general problem of interaction proteomics from a complex matrix whose outcome depends on several variables and it is difficult to isolate one functional complex of the multidomain protein PSD-95 over a large number of synapses and with a different history of synaptic activation.

      The Proteomic Toolbox to Analyze Neuronal Proteostasis in Plasticity

      The early studies were based on biochemical purification schemes that impose limitations with regard to quantitative proteomics (i.e. shot-gun proteomics versus 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) (
      • Freeman W.M.
      • Hemby S.E.
      Proteomics for protein expression profiling in neuroscience.
      ,
      • Lilley K.S.
      • Friedman D.B.
      All about DIGE: quantification technology for differential-display 2D-gel proteomics.
      ), variability in the isolation of the structures of interest because of different density gradient centrifugation protocols, immunoprecipitation of protein complexes, Fig. 3). With the advent of quantitative proteomics using ICAT (
      • Gygi S.P.
      • Rist B.
      • Gerber S.A.
      • Turecek F.
      • Gelb M.H.
      • Aebersold R.
      Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.
      ), iTRAQ (
      • Ross P.L.
      • Huang Y.N.
      • Marchese J.N.
      • Williamson B.
      • Parker K.
      • Hattan S.
      • Khainovski N.
      • Pillai S.
      • Dey S.
      • Daniels S.
      • Purkayastha S.
      • Juhasz P.
      • Martin S.
      • Bartlet-Jones M.
      • He F.
      • Jacobson A.
      • Pappin D.J.
      Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.
      ), AQUA (
      • Gerber S.A.
      • Rush J.
      • Stemman O.
      • Kirschner M.W.
      • Gygi S.P.
      Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS.
      ), SILAC (
      • Ong S.E.
      • Blagoev B.
      • Kratchmarova I.
      • Kristensen D.B.
      • Steen H.
      • Pandey A.
      • Mann M.
      Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.
      ), SILAM (
      • Rauniyar N.
      • McClatchy D.B.
      • Yates 3rd., J.R.
      Stable isotope labeling of mammals (SILAM) for in vivo quantitative proteomic analysis.
      ), and label-free quantification approaches (
      • Wiener M.C.
      • Sachs J.R.
      • Deyanova E.G.
      • Yates N.A.
      Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures.
      ) some of these issues have been resolved and differential proteomic profiling in combination with absolute and relative quantifications can address changes of the neuronal proteome associated with diseases (e.g.
      • Liao L.
      • Park S.K.
      • Xu T.
      • Vanderklish P.
      • Yates 3rd., J.R.
      Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice.
      ,
      • Pennington K.
      • Dicker P.
      • Dunn M.J.
      • Cotter D.R.
      Proteomic analysis reveals protein changes within layer 2 of the insular cortex in schizophrenia.
      ,
      • Gong Y.
      • Lippa C.F.
      • Zhu J.
      • Lin Q.
      • Rosso A.L.
      Disruption of glutamate receptors at Shank-postsynaptic platform in Alzheimer's disease.
      ,
      • Klemmer P.
      • Meredith R.M.
      • Holmgren C.D.
      • Klychnikov O.I.
      • Stahl-Zeng J.
      • Loos M.
      • van der Schors R.C.
      • Wortel J.
      • de Wit H.
      • Spijker S.
      • Rotaru D.C.
      • Mansvelder H.D.
      • Smit A.B.
      • Li K.W.
      Proteomics, ultrastructure, and physiology of hippocampal synapses in a fragile X syndrome mouse model reveal presynaptic phenotype.
      ,
      • Sun Y.
      • Dierssen M.
      • Toran N.
      • Pollak D.D.
      • Chen W.Q.
      • Lubec G.
      A gel-based proteomic method reveals several protein pathway abnormalities in fetal Down syndrome brain.
      ,
      • Martins-de-Souza D.
      • Guest P.C.
      • Rahmoune H.
      • Bahn S.
      Proteomic approaches to unravel the complexity of schizophrenia.
      ,
      • Manavalan A.
      • Mishra M.
      • Feng L.
      • Sze S.K.
      • Akatsu H.
      • Heese K.
      Brain site-specific proteome changes in aging-related dementia.
      ,
      • Zhou J.
      • Jones D.R.
      • Duong D.M.
      • Levey A.I.
      • Lah J.J.
      • Peng J.
      Proteomic analysis of postsynaptic density in Alzheimer's disease.
      ,
      • Wesseling H.
      • Guest P.C.
      • Lee C.M.
      • Wong E.H.
      • Rahmoune H.
      • Bahn S.
      Integrative proteomic analysis of the NMDA NR1 knockdown mouse model reveals effects on central and peripheral pathways associated with schizophrenia and autism spectrum disorders.
      ) or is able to tackle protein half-lives (
      • Cohen L.D.
      • Zuchman R.
      • Sorokina O.
      • Müller A.
      • Dieterich D.C.
      • Armstrong J.D.
      • Ziv T.
      • Ziv N.E.
      Metabolic turnover of synaptic proteins: kinetics, interdependencies, and implications for synaptic maintenance.
      ,
      • Toyama B.H.
      • Savas J.N.
      • Park S.K.
      • Harris M.S.
      • Ingolia N.T.
      • Yates 3rd, J.R.
      • Hetzer M.W.
      Identification of long-lived proteins reveals exceptional stability of essential cellular structures.
      ). Crucial to all of these approaches are absolute high fidelity in the technical prerequisites of the analyses including accuracy of workflows, validity of data, dynamic range of protein assessment, or PTM levels. These important aspects have been recently covered in excellent technical reviews (
      • Trinidad J.C.
      • Schoepfer R.
      • Burlingame A.L.
      ,
      • Lesur A.
      • Domon B.
      Advances in high-resolution accurate mass spectrometry application to targeted proteomics.
      ).
      However, the current proteomic toolbox still lacks the ability to grasp the full range of determinants of synapse proteostasis simultaneously because of special purification and separation requirements of the various protein modifications in combination with detection limitations of the mass spectrometers, i.e. we cannot go beyond the “mere” identification and quantification of specific proteomes such as the synaptic phosphoproteome (
      • Collins M.O.
      • Yu L.
      • Coba M.P.
      • Husi H.
      • Campuzano I.
      • Blackstock W.P.
      • Choudhary J.S.
      • Grant S.G.
      Proteomic analysis of in vivo phosphorylated synaptic proteins.
      ,
      • Trinidad J.C.
      • Thalhammer A.
      • Specht C.G.
      • Schoepfer R.
      • Burlingame A.L.
      Phosphorylation state of postsynaptic density proteins.
      ,
      • DeGiorgis J.A.
      • Jaffe H.
      • Moreira J.E.
      • Carlotti Jr., C.G.
      • Leite J.P.
      • Pant H.C.
      • Dosemeci A.
      Phosphoproteomic analysis of synaptosomes from human cerebral cortex.
      ,
      • Trinidad J.C.
      • Specht C.G.
      • Thalhammer A.
      • Schoepfer R.
      • Burlingame A.L.
      Comprehensive identification of phosphorylation sites in postsynaptic density preparations.
      ,
      • Munton R.P.
      • Tweedie-Cullen R.
      • Livingstone-Zatchej M.
      • Weinandy F.
      • Waidelich M.
      • Longo D.
      • Gehrig P.
      • Potthast F.
      • Rutishauser D.
      • Gerrits B.
      • Panse C.
      • Schlapbach R.
      • Mansuy I.M.
      Qualitative and quantitative analyses of protein phosphorylation in naive and stimulated mouse synaptosomal preparations.
      ,
      • Trinidad J.C.
      • Thalhammer A.
      • Specht C.G.
      • Lynn A.J.
      • Baker P.R.
      • Schoepfer R.
      • Burlingame A.L.
      Quantitative analysis of synaptic phosphorylation and protein expression.
      ,
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ,
      • Siddoway B.
      • Hou H.
      • Yang H.
      • Petralia R.
      • Xia H.
      Synaptic activity bidirectionally regulates a novel sequence-specific S-Q phosphoproteome in neurons.
      ) or the entity of palmitoylated proteins (
      • Roth A.F.
      • Wan J.
      • Green W.N.
      • Yates J.R.
      • Davis N.G.
      Proteomic identification of palmitoylated proteins.
      ,
      • Kang R.
      • Wan J.
      • Arstikaitis P.
      • Takahashi H.
      • Huang K.
      • Bailey A.O.
      • Thompson J.X.
      • Roth A.F.
      • Drisdel R.C.
      • Mastro R.
      • Green W.N.
      • Yates 3rd., J.R.
      • Davis N.G.
      • El-Husseini A.
      Neural palmitoyl-proteomics reveals dynamic synaptic palmitoylation.
      ) but rather have to address post-translational modifications (PTMs) one by one. In a seminal paper by the Burlingame lab, characterization of O-linked glycosylated and phosphorylated peptides from murine synaptosomes was performed in a sequential manner (
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ). A total of over 1750 O-GlcNAcylated and 16,500 phosphorylated sites were identified with heavily glycosylated proteins such as protein kinases always being extensively phosphorylated as well. This suggests toward a putative crosstalk of phosphorylation with glycosylation at catalytic grounds (
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ). However, identification of these PTM-subproteomes is not complete, particular PTM peptides originating from lower abundance proteins are missing because of the common, strong bias toward acquiring MS/MS data on higher abundance molecules. This type of issue also holds true for combining the different profiling approaches with metabolomics or neuropeptidomics. Moreover, recent evidence from several labs points to the pivotal importance of alternative splicing of mRNA for activity-dependent protein synthesis (
      • Zheng S.
      • Black D.L.
      Alternative premRNA splicing in neurons: growing up and extending its reach.
      ,
      • Stilling R.M.
      • Benito E.
      • Gertig M.
      • Barth J.
      • Capece V.
      • Burkhardt S.
      • Bonn S.
      • Fischer A.
      Deregulation of gene expression and alternative splicing affects distinct cellular pathways in the aging hippocampus.
      ,
      • de Klerk E.
      • 't Hoen P.A.
      Alternative mRNA transcription, processing, and translation: insights from RNA sequencing.
      ), the use of alternative translation initiation sites (
      • Beerman R.W.
      • Jongens T.A.
      A noncanonical start codon in the Drosophila fragile X gene yields two functional isoforms.
      ,
      • Chua J.J.
      • Schob C.
      • Rehbein M.
      • Gkogkas C.G.
      • Richter D.
      • Kindler S.
      Synthesis of two SAPAP3 isoforms from a single mRNA is mediated via alternative translational initiation.
      ,
      • Studtmann K.
      • Olschläger-Schütt J.
      • Buck F.
      • Richter D.
      • Sala C.
      • Bockmann J.
      • Kindler S.
      • Kreienkamp H.J.
      A noncanonical initiation site is required for efficient translation of the dendritically localized Shank1 mRNA.
      ) and the involvement of point mutations to the genesis of several diseases (e.g.
      • Joutel A.
      • Corpechot C.
      • Ducros A.
      • Vahedi K.
      • Chabriat H.
      • Mouton P.
      • Alamowitch S.
      • Domenga V.
      • Cecillion M.
      • Marechal E.
      • Maciazek J.
      • Vayssiere C.
      • Cruaud C.
      • Cabanis E.A.
      • Ruchoux M.M.
      • Weissenbach J.
      • Bach J.F.
      • Bousser M.G.
      • Tournier-Lasserve E.
      Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia.
      ,
      • Abidi F.
      • Miano M.
      • Murray J.
      • Schwartz C.
      A novel mutation in the PHF8 gene is associated with X-linked mental retardation with cleft lip/cleft palate.
      ,
      • Nordstrom-O'Brien M.
      • van der Luijt R.B.
      • van Rooijen E.
      • van den Ouweland A.M.
      • Majoor-Krakauer D.F.
      • Lolkema M.P.
      • van Brussel A.
      • Voest E.E.
      • Giles R.H.
      Genetic analysis of von Hippel-Lindau disease.
      ). These phenomena imply a severe problem for proteomics as full sequence coverage is a rather rare event during the identification process, and, therefore, missing domains encoded by differentially spliced internal exons or N-terminally truncated proteins in the case of alternative AUGs will be hardly identified in complex samples. Point mutations pose a significant problem especially in the case of human samples and yet unknown mutations as search engines attempt to match peptide sequences present in a given database. Here, customized databases (e.g. the SNAP database within the Global Proteome Machine (GPM), http://gpmdb.thegpm.org/snap/index.html, or PEPPI, http://bio.informatics.iupui.edu/peppi (
      • Zhou A.
      • Zhang F.
      • Chen J.Y.
      PEPPI: a peptidomic database of human protein isoforms for proteomics experiments.
      )) and the combination with transcriptomics will help to resolve this issue. Finally, although a multitude of specifically neuronal proteins has been identified, synapses share identical proteins including receptors, channels, cell adhesion molecules, and regulatory proteins and all of their putative post-translational modifications, with intimately connected cell types, such as astrocytes, oligodendrocytes and microglia. Hence, without having the possibility of neuronal cell-selectivity one cannot be entirely sure about the true identity and dynamics of the current synaptic proteomes in question. Novel approaches will circumvent this problem at least in part (see below).

      The Conceptual Framework for Synaptoneuroproteomics

      The most critical aspect in the study of synapse proteostasis is to overcome the snap shot view imposed by a steady state analysis of the highly dynamic protein equilibrium in spine synapses. The exchange rate of proteins between spine synapses is quite high (
      • Ziv N.E.
      • Fisher-Lavie A.
      Presynaptic and postsynaptic scaffolds: dynamics fast and slow.
      ) despite the fact that protein turnover of key synaptic proteins is probably in the range of days (
      • Trinidad J.C.
      • Schoepfer R.
      • Burlingame A.L.
      ,
      • Lesur A.
      • Domon B.
      Advances in high-resolution accurate mass spectrometry application to targeted proteomics.
      ,
      • Ziv N.E.
      • Fisher-Lavie A.
      Presynaptic and postsynaptic scaffolds: dynamics fast and slow.
      ) and that protein degradation and de novo synthesis alone can, therefore, probably not solely account for these high dynamics (
      • Hanus C.
      • Schuman E.M.
      Proteostasis in complex dendrites.
      ,
      • Rosenberg T.
      • Gal-Ben-Ari S.
      • Dieterich D.C.
      • Kreutz M.R.
      • Ziv N.E.
      • Gundelfinger E.D.
      • Rosenblum K.
      The roles of protein expression in synaptic plasticity and memory consolidation.
      ). In addition the molecular composition of spine synapses is different depending upon their dendritic localization, size, and age (
      • Busse B.
      • Smith S.
      Automated analysis of a diverse synapse population.
      ,
      • MacGillavry H.D.
      • Hoogenraad C.C.
      The internal architecture of dendritic spines revealed by super-resolution imaging: what did we learn so far?.
      ). Thus, although it has been proposed that the stoichiometry of the synaptic scaffold is not variable in mature spines (
      • Cheng D.
      • Hoogenraad C.C.
      • Rush J.
      • Ramm E.
      • Schlager M.A.
      • Duong D.M.
      • Xu P.
      • Wijayawardana S.R.
      • Hanfelt J.
      • Nakagawa T.
      • Sheng M.
      • Peng J.
      Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum.
      ,
      • Okabe S.
      Molecular anatomy of the postsynaptic density.
      ,
      • Sugiyama Y.
      • Kawabata I.
      • Sobue K.
      • Okabe S.
      Determination of absolute protein numbers in single synapses by a GFP-based calibration technique.
      ,
      • Lowenthal M.S.
      • Markey S.P.
      • Dosemeci A.
      Quantitative mass spectrometry measurements reveal stoichiometry of principal postsynaptic density proteins.
      ), a profound molecular diversity might exist between excitatory synapses and this heterogeneity is further increased by the existence of excitatory and inhibitory shaft synapses whose dynamics and molecular make up are not very well investigated. Thus, a number of biological constraints exist for a quantitative proteomic approach to synaptic function.
      In consequence, one might ask which questions can be addressed with the currently available technologies and how future methodological developments might overcome these limitations. A central request for a molecular underpinning of activity-dependent changes in synaptic strength is the idea that the molecular make-up of a synapse changes for time periods of days if not weeks and months. An influential hypothesis in the field called “synaptic tagging” (
      • Frey U.
      • Morris R.G.
      Synaptic tagging and long-term potentiation.
      ,
      • Redondo R.L.
      • Morris R.G.
      Making memories last: the synaptic tagging and capture hypothesis.
      ) proposes that sustained synaptic activity like during the induction of long-term potentiation (LTP) initiates the creation of a short-lasting protein-synthesis-independent “synaptic tag” at the potentiated synapse. The nature of this tag is still unknown but it is supposed that it will sequester proteins to the activated synapse, which will subsequently help to maintain LTP (
      • Redondo R.L.
      • Morris R.G.
      Making memories last: the synaptic tagging and capture hypothesis.
      ). Despite more than a decade of research the acquisition of the corresponding molecular maintenance mechanisms by a synapse is also still elusive. The list of processes and mechanisms contributing to tagging is long: Various post-translational modifications (
      • Lüscher C.
      • Malenka R.C.
      NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD).
      ), mRNA trafficking and local mRNA translation at potentiated synapses (
      • Holt C.E.
      • Schuman E.M.
      The central dogma decentralized: new perspectives on RNA function and local translation in neurons.
      ), incorporation of a pre-existing pool of plasticity-related proteins, protein degradation (
      • Karpova A.
      • Mikhaylova M.
      • Thomas U.
      • Knöpfel T.
      • Behnisch T.
      Involvement of protein synthesis and degradation in long-term potentiation of Schaffer collateral CA1 synapses.
      ,
      • Na C.H.
      • Jones D.R.
      • Yang Y.
      • Wang X.
      • Xu Y.
      • Peng J.
      Synaptic protein ubiquitination in rat brain revealed by antibody-based ubiquitome analysis.
      ), reconstruction of the postsynaptic cytoskeleton (
      • Bosch M.
      • Castro J.
      • Saneyoshi T.
      • Matsuno H.
      • Sur M.
      • Hayashi Y.
      Structural and molecular remodeling of dendritic spine substructures during long-term potentiation.
      ,
      • Meyer D.
      • Bonhoeffer T.
      • Scheuss V.
      Balance and stability of synaptic structures during synaptic plasticity.
      ), exchange of proteins between synapses (
      • Ziv N.E.
      • Fisher-Lavie A.
      Presynaptic and postsynaptic scaffolds: dynamics fast and slow.
      ), and finally weakening of neighboring inactive and nonpotentiated spine synapses (inverse tagging/
      • Okuno H.
      • Akashi K.
      • Ishii Y.
      • Yagishita-Kyo N.
      • Suzuki K.
      • Nonaka M.
      • Kawashima T.
      • Fujii H.
      • Takemoto-Kimura S.
      • Abe M.
      • Natsume R.
      • Chowdhury S.
      • Sakimura K.
      • Worley P.F.
      • Bito H.
      Inverse synaptic tagging of inactive synapses via dynamic interaction of Arc/Arg3.1 with CaMKIIβ.
      ). Looking at this conundrum of different processes that are plausibly only relevant in a defined spatio-temporal context it is obvious to ask which answers can we expect from the current “static” synapse proteomics?

      Activity-Dependent Regulation of the Synaptic Proteome

      A recent survey on quantitative and qualitative proteomic studies of the synapse indicated that more than 2700 different proteins have been identified as integral components of excitatory synapses on post- and presynaptic sites (
      • Pielot R.
      • Smalla K.H.
      • Müller A.
      • Landgraf P.
      • Lehmann A.C.
      • Eisenschmidt E.
      • Haus U.U.
      • Weismantel R.
      • Gundelfinger E.D.
      • Dieterich D.C.
      SynProt: A database for proteins of detergent-resistant synaptic protein preparations.
      ). It should be noted, however, that physical constraints (size of the PSD, copy number of the most abundant proteins etc.) make it unlikely that all of these different proteins can fit into a single spine synapse. It is instead likely that sample contaminations, synaptic heterogeneity as well as protein exchange and mobility contribute to these findings. Several manually curated databases combine these data in online repositories including G2Cdb (http://www.genes2cognition.org/
      • Croning M.D.
      • Marshall M.C.
      • McLaren P.
      • Armstrong J.D.
      • Grant S.G.
      G2Cdb: the genes to cognition database.
      ), SynaptomeDB (http://metamoodics.org/SynaptomeDB/index.php,
      • Pirooznia M.
      • Wang T.
      • Avramopoulos D.
      • Valle D.
      • Thomas G.
      • Huganir R.L.
      • Goes F.S.
      • Potash J.B.
      • Zandi P.P.
      SynaptomeDB: an ontology-based knowledgebase for synaptic genes.
      ), SynProt (www.synprot.de,
      • Bosch M.
      • Castro J.
      • Saneyoshi T.
      • Matsuno H.
      • Sur M.
      • Hayashi Y.
      Structural and molecular remodeling of dendritic spine substructures during long-term potentiation.
      ), or SynSysNet (http://bioinformatics.charite.de/synsys/
      • von Eichborn J.
      • Dunkel M.
      • Gohlke B.O.
      • Preissner S.C.
      • Hoffmann M.F.
      • Bauer J.M.
      • Armstrong J.D.
      • Schaefer M.H.
      • Andrade-Navarro M.A.
      • Le Novere N.
      • Croning M.D.
      • Grant S.G.
      • van Nierop P.
      • Smit A.B.
      • Preissner R.
      SynSysNet: integration of experimental data on synaptic protein–protein interactions with drug-target relations.
      ).
      The majority of studies that compared the proteome of synapses were performed on human brain disease states (
      • Bayés A.
      • van de Lagemaat L.N.
      • Collins M.O.
      • Croning M.D.
      • Whittle I.R.
      • Choudhary J.S.
      • Grant S.G.
      Characterization of the proteome, diseases and evolution of the human postsynaptic density.
      ,
      • Bayés A.
      • Grant S.G.
      Neuroproteomics: understanding the molecular organization and complexity of the brain.
      ,
      • Pennington K.
      • Dicker P.
      • Dunn M.J.
      • Cotter D.R.
      Proteomic analysis reveals protein changes within layer 2 of the insular cortex in schizophrenia.
      ,
      • Gong Y.
      • Lippa C.F.
      • Zhu J.
      • Lin Q.
      • Rosso A.L.
      Disruption of glutamate receptors at Shank-postsynaptic platform in Alzheimer's disease.
      ,
      • Martins-de-Souza D.
      • Guest P.C.
      • Rahmoune H.
      • Bahn S.
      Proteomic approaches to unravel the complexity of schizophrenia.
      ,
      • Zhou J.
      • Jones D.R.
      • Duong D.M.
      • Levey A.I.
      • Lah J.J.
      • Peng J.
      Proteomic analysis of postsynaptic density in Alzheimer's disease.
      ,
      • Smalla K.H.
      • Mikhaylova M.
      • Sahin J.
      • Bernstein H.G.
      • Bogerts B.
      • Schmitt A.
      • van der Schors R.
      • Smit A.B.
      • Li K.W.
      • Gundelfinger E.D.
      • Kreutz M.R.
      A comparison of the synaptic proteome in human chronic schizophrenia and rat ketamine psychosis suggest that prohibitin is involved in the synaptic pathology of schizophrenia.
      ,
      • Kirov G.
      • Pocklington A.J.
      • Holmans P.
      • Ivanov D.
      • Ikeda M.
      • Ruderfer D.
      • Moran J.
      • Chambert K.
      • Toncheva D.
      • Georgieva L.
      • Grozeva D.
      • Fjodorova M.
      • Wollerton R.
      • Rees E.
      • Nikolov I.
      • van de Lagemaat L.N.
      • Bayés A.
      • Fernandez E.
      • Olason P.I.
      • Böttcher Y.
      • Komiyama N.H.
      • Collins M.O.
      • Choudhary J.
      • Stefansson K.
      • Stefansson H.
      • Grant S.G.
      • Purcell S.
      • Sklar P.
      • O'Donovan M.C.
      • Owen M.J.
      De novo CNV analysis implicates specific abnormalities of postsynaptic signaling complexes in the pathogenesis of schizophrenia.
      ,
      • Stockton Jr., S.D.
      • Devi L.A.
      An integrated quantitative proteomics and systems biology approach to explore synaptic protein profile changes during morphine exposure.
      ,
      • Pocklington A.J.
      • O'Donovan M.
      • Owen M.J.
      The synapse in schizophrenia.
      ) and despite significant methodological progress in recent years the resulting picture is still not representative. Apart from the problem of tissue preservation and preparation the limitations of conventional proteomic analysis lead to a situation where in several studies very different proteins where shown to be up- and down-regulated in the same brain region and disease state. Although usually more than one hundred proteins are regulated in disease states only cherry-picked candidate proteins that match to the disease process have been further investigated and up-or-down-regulation of very few other candidates were confirmed by other methods. Another problem of quantitative neuroproteomics is the stoichiometry of synaptic proteins that ranges from less than ten to more than several hundred (
      • Cheng D.
      • Hoogenraad C.C.
      • Rush J.
      • Ramm E.
      • Schlager M.A.
      • Duong D.M.
      • Xu P.
      • Wijayawardana S.R.
      • Hanfelt J.
      • Nakagawa T.
      • Sheng M.
      • Peng J.
      Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum.
      ,
      • Sheng M.
      • Hoogenraad C.C.
      The postsynaptic architecture of excitatory synapses: a more quantitative view.
      ,
      • Bayés A.
      • Grant S.G.
      Neuroproteomics: understanding the molecular organization and complexity of the brain.
      ,
      • Okabe S.
      Molecular anatomy of the postsynaptic density.
      ,
      • Sugiyama Y.
      • Kawabata I.
      • Sobue K.
      • Okabe S.
      Determination of absolute protein numbers in single synapses by a GFP-based calibration technique.
      ,
      • Lowenthal M.S.
      • Markey S.P.
      • Dosemeci A.
      Quantitative mass spectrometry measurements reveal stoichiometry of principal postsynaptic density proteins.
      ). Despite the fact that synapses are relatively stable imaging studies suggest that the turnover of proteins within a spine is fast on a time scale of minutes to hours and it is likely that both a relatively immobile and a rather dynamic pool for a given synaptic protein exist (
      • Tsuriel S.
      • Geva R.
      • Zamorano P.
      • Dresbach T.
      • Boeckers T.
      • Gundelfinger E.D.
      • Garner C.C.
      • Ziv N.E.
      Local sharing as a predominant determinant of synaptic matrix molecular dynamics.
      ,
      • Tsuriel S.
      • Fisher A.
      • Wittenmayer N.
      • Dresbach T.
      • Garner C.C.
      • Ziv N.E.
      Exchange and redistribution dynamics of the cytoskeleton of the active zone molecule bassoon.
      ,
      • Choquet D.
      • Triller A.
      The dynamic synapse.
      ,
      • Lu H.E.
      • MacGillavry H.D.
      • Frost N.A.
      • Blanpied T.A.
      Multiple spatial and kinetic subpopulations of CaMKII in spines and dendrites as resolved by single-molecule tracking PALM.
      ). The metabolic half-life of most synaptic proteins is usually in the range of several days (
      • Cohen L.D.
      • Zuchman R.
      • Sorokina O.
      • Müller A.
      • Dieterich D.C.
      • Armstrong J.D.
      • Ziv T.
      • Ziv N.E.
      Metabolic turnover of synaptic proteins: kinetics, interdependencies, and implications for synaptic maintenance.
      ,
      • Toyama B.H.
      • Savas J.N.
      • Park S.K.
      • Harris M.S.
      • Ingolia N.T.
      • Yates 3rd, J.R.
      • Hetzer M.W.
      Identification of long-lived proteins reveals exceptional stability of essential cellular structures.
      ,
      • Price J.C.
      • Guan S.
      • Burlingame A.
      • Prusiner S.B.
      • Ghaemmaghami S.
      Analysis of proteome dynamics in the mouse brain.
      ) and several live-imaging studies revealed a prominent exchange of molecules between synapses (reviewed in
      • Ziv N.E.
      • Fisher-Lavie A.
      Presynaptic and postsynaptic scaffolds: dynamics fast and slow.
      ). This points to another problem of a steady-state approach such as the current proteomic ones to a highly dynamic equilibrium: The high exchange rate of synaptic proteins requires a fast supply to prevent synaptic dysfunction and this in turn points to a readily available reserve pool of proteins in dendrites that might derive from a local protein synthesis machinery (
      • Hanus C.
      • Schuman E.M.
      Proteostasis in complex dendrites.
      ,
      • Rosenberg T.
      • Gal-Ben-Ari S.
      • Dieterich D.C.
      • Kreutz M.R.
      • Ziv N.E.
      • Gundelfinger E.D.
      • Rosenblum K.
      The roles of protein expression in synaptic plasticity and memory consolidation.
      ). Knowledge about these pools is essential for the interpretation of quantitative proteomics. A central question therefore will be how important local protein translation is for synapse function and under which circumstances it is needed. However, the sensitivity of current MS-based techniques still requires sampling an entire heterogeneous brain region consisting of different subtypes of neurons and glial cells instead of a single cell or synapse and for the study of activity-dependent changes in the synaptic proteome usually larger tissue samples have to be manipulated.

      The Synaptic Proteome in Learning and Memory

      As a result of these difficulties considerably less work has been done on the dynamics of the synaptic proteome following the induction of synaptic plasticity. In consequence, very little is known to date whether patterns of protein expression are altered depending upon the synaptic input in vivo. Compelling evidence for experience-dependent changes in the synaptic proteome stem largely from studies where the sensory input was manipulated during development (
      • Butko M.T.
      • Savas J.N.
      • Friedman B.
      • Delahunty C.
      • Ebner F.
      • Yates 3rd, J.R.
      • Tsien R.Y.
      In vivo quantitative proteomics of somatosensory cortical synapses shows which protein levels are modulated by sensory deprivation.
      ,
      • Dahlhaus M.
      • Li K.W.
      • van der Schors R.C.
      • Saiepour M.H.
      • van Nierop P.
      • Heimel J.A.
      • Hermans J.M.
      • Loos M.
      • Smit A.B.
      • Levelt C.N.
      The synaptic proteome during development and plasticity of the mouse visual cortex.
      ). Pharmacological activation of synapses either in vivo (
      • Trinidad J.C.
      • Thalhammer A.
      • Burlingame A.L.
      • Schoepfer R.
      Activity-dependent protein dynamics define interconnected cores of coregulated postsynaptic proteins.
      ) or in vitro (
      • Piccoli G.
      • Verpelli C.
      • Tonna N.
      • Romorini S.
      • Alessio M.
      • Nairn A.C.
      • Bachi A.
      • Sala C.
      Proteomic analysis of activity-dependent synaptic plasticity in hippocampal neurons.
      ,
      • Matsuura K.
      • Nakamura-Hirota T.
      • Takano M.
      • Otani M.
      • Kadoyama K.
      • Matsuyama S.
      Proteomic analysis of time-dependent changes in proteins expressed in mouse hippocampus during synaptic plasticity induced by GABAA receptor blockade.
      ) with GABA-ergic antagonists to remove inhibition and thereby increase firing of excitatory synapses or after induction of LTP (
      • McNair K.
      • Davies C.H.
      • Cobb S.R.
      Plasticity-related regulation of the hippocampal proteome.
      ) results in profound changes in synaptic protein composition.
      These reports prove the feasibility of proteomics to detect activity-dependent changes in synaptic protein content. Nonetheless very few studies were undertaken to learn about learning-induced proteomic changes (
      • Henninger N.
      • Feldmann Jr., R.E.
      • Fütterer C.D.
      • Schrempp C.
      • Maurer M.H.
      • Waschke K.F.
      • Kuschinsky W.
      • Schwab S.
      Spatial learning induces predominant downregulation of cytosolic proteins in the rat hippocampus.
      ,
      • Zheng J.F.
      • Patil S.S.
      • Chen W.Q.
      • An W.
      • He J.Q.
      • Höger H.
      • Lubec G.
      Hippocampal protein levels related to spatial memory are different in the Barnes maze and in the multiple T-maze.
      ,
      • Li L.
      • Boddul S.V.
      • Patil S.S.
      • Zheng J.F.
      • An G.
      • Höger H.
      • Lubec G.
      Proteins linked to extinction in contextual fear conditioning in the C57BL/6J mouse.
      ,
      • Monopoli M.P.
      • Raghnaill M.N.
      • Loscher J.S.
      • O'Sullivan N.C.
      • Pangalos M.N.
      • Ring R.H.
      • von Schack D.
      • Dunn M.J.
      • Regan C.M.
      • Pennington S.
      • Murphy K.J.
      Temporal proteomic profile of memory consolidation in the rat hippocampal dentate gyrus.
      ,
      • Kähne T.
      • Kolodziej A.
      • Smalla K.H.
      • Eisenschmidt E.
      • Haus U.U.
      • Weismantel R.
      • Kropf S.
      • Wetzel W.
      • Ohl F.W.
      • Tischmeyer W.
      • Naumann M.
      • Gundelfinger E.D.
      Synaptic proteome changes in mouse brain regions upon auditory discrimination learning.
      ,
      • Hong I.
      • Kang T.
      • Yun K.N.
      • Yoo Y.
      • Park S.
      • Kim J.
      • An B.
      • Song S.
      • Lee S.
      • Kim J.
      • Song B.
      • Kwon K.H.
      • Kim J.Y.
      • Park Y.M.
      • Choi S.
      Quantitative proteomics of auditory fear conditioning.
      ,

      .Rao-Ruiz, P., Carney, K. E., Pandya, N., van der Loo, R. J., Verheijen, M. H., van Nierop, P., Smit, A. B., and Spijker, S., (20159 Time-dependent changes in the mouse hippocampal synaptic membrane proteome after contextual fear conditioning. Hippocampus doi: 10.1002/hipo.22432 [Epub ahead of print],

      ). Interestingly, changes in the abundance of several hundred proteins were reported even in studies that used total protein homogenates. Moreover, very little overlap even in regulated protein networks is apparent from this work, which makes it difficult to provide a synopsis of the learning-regulated proteome. It is important to note that only a small percentage of synapses and cells usually are supposed to encode the memory and it is therefore surprising that with the relatively insensitive methodology employed so many proteins were detected that are up- and down-regulated. Cell-specific labeling and purification techniques as well as visualization of newly synthesized proteins might provide a technological advance to gain deeper insights (see below). Another important aspect is here clearly the time course and the need to differentiate between memory stages like acquisition, encoding and consolidation. Likewise synaptic activation as such does not necessarily lead to an engram that can be easily detected at the protein level. Even an enriched environment or simple physical exercise can lead to the expression of several plasticity-related genes in the hippocampus and dramatic changes in the synaptic proteome that can obscure the findings (
      • Ding Q.
      • Vaynman S.
      • Souda P.
      • Whitelegge J.P.
      • Gomez-Pinilla F.
      Exercise affects energy metabolism and neural plasticity-related proteins in the hippocampus as revealed by proteomic analysis.
      ,
      • McNair K.
      • Broad J.
      • Riedel G.
      • Davies C.H.
      • Cobb S.R.
      Global changes in the hippocampal proteome following exposure to an enriched environment.
      ). In other terms the number of control experiments to elucidate a learning-induced change in a small number of synapses is high and usually outside of the scope of a single study. It is therefore unlikely that this approach as it stands today will lead to major new insights into learning-induced changes of the synaptic proteome in the next coming years.

      An Integrative View of the Molecular Dynamics of the Synapse—Perspectives for the Integration of New Technologies

      Dendritic spines can be isolated in so called synaptosomal preparations and kept for cellular in vitro assays for several hours (Fig. 3, Table I). Different purification schemes have been described to increase sample and, thus, synapse specificity. A major advance might be a recently published Fluorescence Activated Synaptosome Sorting (FASS) method that is based on VGLUT1-Venus knock-in mice and that allows an enrichment of glutamatergic synaptosomes of the forebrain to near homogeneity (
      • Biesemann C.
      • Grønborg M.
      • Luquet E.
      • Wichert S.P.
      • Bernard V.
      • Bungers S.R.
      • Cooper B.
      • Varoqueaux F.
      • Li L.
      • Byrne J.A.
      • Urlaub H.
      • Jahn O.
      • Brose N.
      • Herzog E.
      Proteomic screening of glutamatergic mouse brain synaptosomes isolated by fluorescence activated sorting.
      ). By focusing on more homogeneous samples differential profiling with high protein identification and quantification completeness among several time points in development or during plasticity events might be feasible.

      Lipidomics

      A future development that holds a lot of promise for a deeper appreciation of signaling pathways lies in the combination of proteomics, lipidomics, glycoproteomics, and metabolomics of synapses. Lipidomics of brain tissue is still in its infancy (
      • Piomelli D.
      • Astarita G.
      • Rapaka R.
      A neuroscientist's guide to lipidomics.
      ,
      • Dawson G.
      Measuring brain lipids.
      ). Several lines of evidence suggest that the lipid composition of synapses is highly dynamic (
      • Dotti C.G.
      • Esteban J.A.
      • Ledesma M.D.
      Lipid dynamics at dendritic spines.
      ). The existence of more than 40 different lipids known to modulate signaling and/or to influence membrane geometry in neurons, synapses, and synaptic vesicles demands for a systematic large scale study of lipid abundance and functional regulation in neuronal subcompartments. A very recent large-scale MS-based analysis of the lipid composition of the human brain shows a bewildering complexity of lipid composition: From 5713 lipid compounds analyzed in the study 76% were either enriched or depleted in brain (
      • Bozek K.
      • Wei Y.
      • Yan Z.
      • Liu X.
      • Xiong J.
      • Sugimoto M.
      • Tomita M.
      • Paabo S.
      • Sherwood C.C.
      • Hof P.R.
      • Ely J.J.
      • Li Y.
      • Steinhauser D.
      • Willmitzer L.
      • Giavalisco P.
      • Khaitovich P.
      Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues.
      ). Currently >600 lipids can be quantitatively accessed, whereas many lipid classes cannot be analyzed owing to impaired ionization and solubility, as well as low abundance (
      • Fhaner C.J.
      • Liu S.
      • Ji H.
      • Simpson R.J.
      • Reid G.E.
      Comprehensive lipidome profiling of isogenic primary and metastatic colon adenocarcinoma cell lines.
      ). Modern lipidomic tools can provide access to understand the complexity of lipids, their homeostatic regulation, and their role in neuronal plasticity and in synaptic diseases (for a recent review see
      • Dawson G.
      Measuring brain lipids.
      ). Studies are ultimately warranted that break ground and provide a lipid inventory of synapses and that address whether corresponding lipid alterations occur in paradigms of synaptic plasticity and efforts to develop novel lipid quantification techniques tailored for the analysis of the synaptoneurolipidome will most likely open up new avenues in synapse biology (
      • Suzuki T.
      • Zhang J.
      • Miyazawa S.
      • Liu Q.
      • Farzan M.R.
      • Yao W.D.
      Association of membrane rafts and postsynaptic density: proteomics, biochemical, and ultrastructural analyses.
      ).
      Lipids impact on cellular functions and it is therefore of fundamental importance to correlate lipid dynamics with proteins that are essential for the synthesis, modification, and turnover of lipids and vice versa. A limitation that has to be overcome is that so far different biological building blocks, i.e. proteins, lipids and metabolites have largely been investigated independently. Direct correlations between metabolic and signaling events frequently remain concealed and studies targeting different molecular classes at once are cumbersome, rare and unique. Systems scale integration of lipidomics data within data sets derived from proteomics or metabolomics experiments is conceivable by concentration change coupling analysis to investigate the interconnectivity between the different molecular layers at different condition (see for instance
      • Patel V.R.
      • Eckel-Mahan K.
      • Sassone-Corsi P.
      • Baldi P.
      CircadiOmics: integrating circadian genomics, transcriptomics, proteomics, and metabolomics.
      ). Thus, workflows to study the complexity and dynamics of lipids and their roles within synaptic membranes and synapto-dendritic organelles under physiological and pathological conditions are in principle established.

      Glycoproteomics

      Attachment of carbohydrates to proteins is as lipidation vital for a large number of cellular processes. Especially synapses are enriched in N- and O-linked glycoproteins and glycosylated proteins – among them many receptors, channels, cell adhesion molecules, extracellular matrix (ECM) proteins, and regulatory proteins—fulfill crucial functions in cell differentiation, neuronal growth, signal transduction, cell–cell recognition, LTP (
      • Matthies Jr., H.
      • Kretlow J.
      • Matthies H.
      • Smalla K.H.
      • Staak S.
      • Krug M.
      Glycosylation of proteins during a critical time window is necessary for the maintenance of long-term potentiation in the hippocampal CA1 region.
      ), and memory formation (
      • Rose S.P.
      Glycoproteins and memory formation.
      ). Complex O- and N-linked glycosylation are heterogeneous in their nature, more or less permanent PTMs, and, therefore, glycosylated peptides are rarely found in their unmodified form in contrast to phosphorylated or acetylated peptides. In contrast, simple O-linked glycosylation with β-N-acetyl-d-glucosamine (O-GlcNAc) is a very dynamic PTM resembling similar features as protein phosphorylation (
      • Hart G.W.
      • Housley M.P.
      • Slawson C.
      Cycling of O-linked beta-N-acetylglucosamine on nucleocytoplasmic proteins.
      ,
      • Rexach J.E.
      • Clark P.M.
      • Mason D.E.
      • Neve R.L.
      • Peters E.C.
      • Hsieh-Wilson L.C.
      Dynamic O-GlcNAc modification regulates CREB-mediated gene expression and memory formation.
      ). Despite the importance of glycosylation for neuronal and synaptic function, very little is still known on the exact composition and regulation of glycosylation of synaptic proteins because of the heterogeneity and complexity of the static glycosylation patterns as well as the dynamics of simple O-GlcNAc glycosylation. The lab of Alma Burlingame recently presented the combined analyses of the phospho- and O-glycoproteome as well as the O- and N-glycoproteomes of murine synaptosomes (
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ,
      • Trinidad J.C.
      • Schoepfer R.
      • Burlingame A.L.
      • Medzihradszky K.F.
      N- and O-glycosylation in the murine synaptosome.
      ) using a sequential purification strategy for glycosylated, phosphorylated, and unmodified peptides. As mentioned above, over 1750 O-GlcNAcylated and 16,500 phosphorylated sites were identified in the first study (
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ) and a total of over 2500 unique N- and O-linked glycopeptides with remarkable microheterogeneity of attached oligosaccharides on 453 proteins was discovered in the second study (
      • Trinidad J.C.
      • Schoepfer R.
      • Burlingame A.L.
      • Medzihradszky K.F.
      N- and O-glycosylation in the murine synaptosome.
      ). Considering the issue of missing PTM-peptides of lower abundance proteins, the authors estimate the content of O-glycosylated proteins to be 19% and the phosphorylated proteins at 63% of the total synaptosome proteome (
      • Trinidad J.C.
      • Barkan D.T.
      • Gulledge B.F.
      • Thalhammer A.
      • Sali A.
      • Schoepfer R.
      • Burlingame A.L.
      Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse.
      ). Combination of filter-aided lectin affinity purification and high-accuracy MS revealed 3162 N-glycosylation sites in mouse brain, with 1140 being unique to the brain and allowed the simultaneous quantification of the N-glycoproteome via SILAC during age (
      • Zielinska D.F.
      • Gnad F.
      • Wisniewski J.R.
      • Mann M.
      Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints.
      ). However, also this seminal study falls short to reflect the wide diversity and likely dynamics of oligosaccharide structures and subtle glycosylation changes could not be detected. Especially the very terminal ends consisting of sialic acids and to a lesser extend l-Fucose of glycans are of critical importance for glycoprotein function and specificity. NCAM is a polysialylated (polySia) glycoprotein and the polySia serves as a negative regulator of cell–cell apposition, interferes with cis- and trans-interactions of NCAM. Moreover, it modulates as a scavenger of soluble factors receptor activation (reviewed in
      • Schnaar R.L.
      • Gerardy-Schahn R.
      • Hildebrandt H.
      Sialic acids in the brain: gangliosides and polysialic acid in nervous system development, stability, disease, and regeneration.
      ). Fucosylated carbohydrate structures in the brain have been implicated in molecular mechanisms that underlie neuronal development, learning, and memory. Plasticity phenomena including hippocampal LTP and memory formation, for instance, are accompanied by a transient increase in fucose incorporation into membrane glycoproteins (
      • Jork R.
      • Grecksch G.
      • Matthies H.
      Impairment of glycoprotein fucosylation in rat hippocampus and the consequences on memory formation.
      ,
      • Angenstein F.
      • Matthies Jr., H.
      • Staeck S.
      • Reymann K.G.
      • Staak S.
      The maintenance of hippocampal long-term potentiation is paralleled by a dopamine-dependent increase in glycoprotein fucosylation.
      ,
      • Matthies H.
      • Staak S.
      • Smalla K.H.
      • Krug M.
      Enhancement if hippocampal long-term potentiation in vitro by fucosyl-carbohydrates.
      ). Most notably, inhibition of protein fucosylation does not interfere with LTP induction or memory acquisition, but prevents specifically the maintenance of LTP and long-term memory (
      • Angenstein F.
      • Matthies Jr., H.
      • Staeck S.
      • Reymann K.G.
      • Staak S.
      The maintenance of hippocampal long-term potentiation is paralleled by a dopamine-dependent increase in glycoprotein fucosylation.
      ,
      • Krug M.
      • Jork R.
      • Reymann K.
      • Wagner M.
      • Matthies H.
      The amnesic substance 2-deoxy-D-galactose suppresses the maintenance of hippocampal LTP.
      ).
      Likewise to the lectin-based approaches for the characterization of the O- and N-linked glycoproteomes, Murrey et al. (
      • Murrey H.E.
      • Ficarro S.B.
      • Krishnamurthy C.
      • Domino S.E.
      • Peters E.C.
      • Hsieh-Wilson L.C.
      Identification of the plasticity-relevant fucose-alpha(1–2)-galactose proteome from the mouse olfactory bulb.
      ) used a more l-fucose-specific lectin from Ulex europaeus to enrich for fucosylated protein from different brain regions of adult and P3 mice. The 32 identified candidates belong to the classes of cell adhesion molecules, ion channels and solute carriers/transporters, ATP-binding proteins, synaptic-vesicle associated proteins and mitochondrial proteins, and most of them are predominantly expressed and developmentally regulated in the olfactory bulb. It is highly likely that with more specific enrichment approaches such as other lectins and in combination with bio-orthogonal labeling strategies (see below) and improved sensitivity of the mass spectrometers the repertoire of fucosylated proteins and the understanding of fucosylation dynamics will expand dramatically in the near future.

      Metabolic Labeling

      To tackle dynamics of post-translational modifications or alterations in protein expression patterns and to enrich for these subproteomes bioorthogonal labeling approaches have emerged during the last 15 years that use the cell's own biosynthetic machinery. In these approaches that mainly use azide or alkynes as chemical handles proteins are endowed with this novel azide or alkyne functionality that serves to distinguish them from the pool of pre-existing or recently unmodified proteins. Employing either copper-catalyzed azide-alkyne ligation (commonly referred to as “click chemistry”) or strain-promoted cycloaddition, the reactive azide or alkynes groups can be covalently coupled to respective alkyne- or azide bearing tags in the second step enabling subsequent imaging, affinity purification, and MS identification procedures of tagged proteins (
      • Dieterich D.C.
      Chemical reporters for the illumination of protein and cell dynamics.
      ). As shown for the de novo synthesized proteome using the noncanonical amino acids azidohomoalanine (AHA) or homopropargylglycine (HPG) in combination with BONCAT (bioorthogonal amino acid tagging) or FUNCAT (fluorescent noncanonical amino acid tagging) the presence and incorporation of AHA and HPG are nontoxic and do not affect global rates of protein synthesis or degradation. Moreover, a broad range of functional and biochemically diverse proteins have been identified and temporally and spatially visualized by these techniques (
      • Dieterich D.C.
      • Link A.J.
      • Graumann J.
      • Tirrell D.A.
      • Schuman E.M.
      Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT).
      ,
      • Dieterich D.C.
      • Hodas J.J.
      • Gouzer G.
      • Shadrin I.Y.
      • Ngo J.T.
      • Triller A.
      • Tirrell D.A.
      • Schuman E.M.
      In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons.
      ,
      • Hodas J.J.
      • Nehring A.
      • Hoche N.
      • Sweredoski M.J.
      • Pielot R.
      • Hess S.
      • Tirrell D.A.
      • Dieterich D.C.
      • Schuman E.M.
      Dopaminergic modulation of the hippocampal neuropil proteome identified by bioorthogonal noncanonical amino acid tagging (BONCAT).
      ). With BONCAT the dopaminergic subproteome in rat hippocampal neuropil was assessed (
      • Hodas J.J.
      • Nehring A.
      • Hoche N.
      • Sweredoski M.J.
      • Pielot R.
      • Hess S.
      • Tirrell D.A.
      • Dieterich D.C.
      • Schuman E.M.
      Dopaminergic modulation of the hippocampal neuropil proteome identified by bioorthogonal noncanonical amino acid tagging (BONCAT).
      ). Many of the candidate proteins identified in the dopamine agonist-treated sample belonged to Gene Ontology (GO) categories specific for protein synthesis and synaptic function. The introduction of another noncanonical amino acid azidonorleucine (ANL) for labeling newly synthesized proteins might pave the way for an advancement of the NCAT technologies. Link et al. modified the methionine binding pocket of E. coli Methinonyl-t-RNA synthetase to allow binding of ANL that otherwise can not be processed by the endogenous synthesis machinery because of a large side chain (
      • Link A.J.
      • Vink M.K.
      • Agard N.J.
      • Prescher J.A.
      • Bertozzi C.R.
      • Tirrell D.A.
      Discovery of aminoacyl-tRNA synthetase activity through cell-surface display of noncanonical amino acids.
      ). The mutants E. coli MetRSL13G and E. coli MetRSNLL were found to effectively activate ANL and cell specific expression of these mutants made cell-specific labeling of protein synthesis possible (
      • Ngo J.T.
      • Champion J.A.
      • Mahdavi A.
      • Tanrikulu I.C.
      • Beatty K.E.
      • Connor R.E.
      • Yoo T.H.
      • Dieterich D.C.
      • Schuman E.M.
      • Tirrell D.A.
      Cell-selective metabolic labeling of proteins.
      ,
      • Ngo J.T.
      • Schuman E.M.
      • Tirrell D.A.
      Mutant methionyl-tRNA synthetase from bacteria enables site-selective N-terminal labeling of proteins expressed in mammalian cells.
      ). In very recent work, two groups presented with such an genetically introduced amino acid tagging cell-specific metabolic labeling with spatiotemporal resolution in living Drosophila melanogaster and C. elegans using ANL and Azidophenylalanine, respectively, in combination with genetically engineered aminoacyl tRNA synthetases (
      • Erdmann I.
      • Marter K.
      • Kobler O.
      • Niehues S.
      • Abele J.
      • Müller A.
      • Bussmann J.
      • Storkebaum E.
      • Ziv T.
      • Thomas U.
      • Dieterich D.C.
      Cell-selective labeling of proteomes in Drosophila melanogaster.
      ,
      • Yuet K.P.
      • Doma M.K.
      • Ngo J.T.
      • Sweredoski M.J.
      • Graham R.L.
      • Moradian A.
      • Hess S.
      • Schuman E.M.
      • Sternberg P.W.
      • Tirrell D.A.
      Cell-specific proteomic analysis in Caenorhabditis elegans.
      ). The future implementation of this cell-selective metabolic labeling approach into a mammalian context in combination with live-tagging methods (strain-promoted cycloaddition, see below), promises to track proteome dynamics of distinct neurons or astrocytes in coculture systems or even in living rodents. This is of special importance in particular for understanding the reciprocal interplay between neurons and astrocytes. Both cell-types share despite their functional differences in large parts identical proteins including neurotransmitter receptors, cell adhesion molecules, and signal transduction proteins. Therefore, it is not surprising that in recent years a tight reciprocal relation between neurons and astrocytes has been disclosed covering general aspects of cellular activity but also the astroglial secretion of synaptogenic factors (
      • Hama H.
      • Hara C.
      • Yamaguchi K.
      • Miyawaki A.
      PKC signaling mediates global enhancement of excitatory synaptogenesis in neurons triggered by local contact with astrocytes.
      ,
      • Elmariah S.B.
      • Oh E.J.
      • Hughes E.G.
      • Balice-Gordon R.J.
      Astrocytes regulate inhibitory synapse formation via Trk-mediated modulation of postsynaptic GABAA receptors.
      ). This now well recognized concept of the “tripartite synapse” points to the importance of glia cells for neuronal function and development. A large body of literature shows that astrocytes participate in all essential brain functions, for example they are important for the formation and maintenance of synaptic contacts, sense neuronal activity, and actively participate in homeostatic scaling. However, it is still unclear what proteins are indeed unique to astrocytes and neurons, if the astroglial proteome is as dynamic as it has been shown for the proteomes of different neuronal subtypes, and what the common and unique modes of regulation of the neuronal and astroglial proteome indeed are. Answering these questions will unarguably deepen our understanding of synapse biology and will pave the way to ultimately understand the complex and heterogeneous nature of the brain itself.
      Critically, metabolic labeling of proteins using bioorthogonal chemical reporters is not restricted to monitor global de novo protein synthesis as other classes of biomolecules such as glycans and lipids can be targeted with the same chemistry as well. Carolyn Bertozzi and colleagues applied the metabolic labeling strategy to monitor glycoproteins with different noncanonical monosaccharides in cells, tissues and in zebrafish (
      • Vocadlo D.J.
      • Hang H.C.
      • Kim E.J.
      • Hanover J.A.
      • Bertozzi C.R.
      A chemical approach for identifying O-GlcNAc-modified proteins in cells.
      ,
      • Hang H.C.
      • Yu C.
      • Kato D.L.
      • Bertozzi C.R.
      A metabolic labeling approach toward proteomic analysis of mucin-type O-linked glycosylation.
      ,
      • Laughlin S.T.
      • Baskin J.M.
      • Amacher S.L.
      • Bertozzi C.R.
      In vivo imaging of membrane-associated glycans in developing zebrafish.
      ,
      • Chang P.V.
      • Prescher J.A.
      • Sletten E.M.
      • Baskin J.M.
      • Miller I.A.
      • Agard N.J.
      • Lo A.
      • Bertozzi C.R.
      Copper-free click chemistry in living animals.
      ,
      • Dehnert K.W.
      • Baskin J.M.
      • Laughlin S.T.
      • Beahm B.J.
      • Naidu N.N.
      • Amacher S.L.
      • Bertozzi C.R.
      Imaging the sialome during zebrafish development with copper-free click chemistry.
      ). Moreover, lipid-containing molecules can be tracked via azidolipid precursors tackling N-myristoylation, S-palmitoylation, or farnesylation in living systems (
      • Kho Y.
      • Kim S.C.
      • Jiang C.
      • Barma D.
      • Kwon S.W.
      • Cheng J.
      • Jaunbergs J.
      • Weinbaum C.
      • Tamanoi F.
      • Falck J.
      • Zhao Y.
      A tagging-via-substrate technology for detection and proteomics of farnesylated proteins.
      ,
      • Yap M.C.
      • Kostiuk M.A.
      • Martin D.D.
      • Perinpanayagam M.A.
      • Hak P.G.
      • Siddam A.
      • Majjigapu J.R.
      • Rajaiah G.
      • Keller B.O.
      • Prescher J.A.
      • Wu P.
      • Bertozzi C.R.
      • Falck J.R.
      • Berthiaume L.G.
      Rapid and selective detection of fatty acylated proteins using omega-alkynyl-fatty acids and click chemistry.
      ).

      Imaging Mass Spectrometry

      In the last decade, Imaging MS (IMS) opened a new avenue of bioanalytical research by matching histological features of a tissue sample to molecular localization patterns (recently reviewed in
      • Hanrieder J.
      • Malmberg P.
      • Ewing A.G.
      Spatial neuroproteomics using imaging mass spectrometry.
      ). Although, there are still major technical challenges to be resolved including sample preparation and throughput as well as comprehensive protein identification itself, a few studies using MALDI IMS have already shown its potential for the investigation of spatiotemporal neuropeptide and protein regulation in brain tissues, which is complementary and in some cases even superior to conventional approaches using antibody-based imaging and proteomics techniques. For instance, Hanrieder et al. (
      • Hanrieder J.
      • Ljungdahl A.
      • Falth M.
      • Mammo S.E.
      • Bergquist J.
      • Andersson M.
      L-DOPA-induced dyskinesia is associated with regional increase of striatal dynorphin peptides as elucidated by imaging mass spectrometry.
      ) detected elevated levels of the two neuropeptides dynorphin B and alpha neoendonorphin specifically in the striatum of mice suffering from l-DOPA induced dyskinesia. For both neuropeptides no specific antibodies exist. Secondary-Ion MS (SIMS) detecting the isotopic composition of the sample or material with its high spatial resolution of about 50 nm in the lateral and 10 nm in the z direction, here then referred to as Nano-SIMS, has been recently used in combination with STED super resolution microscopy to quantify protein and organelle turnover in organelles including synaptic vesicles, mitochondria, or Golgi compartments of hippocampal neurons (COIN,
      • Saka S.K.
      • Honigmann A.
      • Eggeling C.
      • Hell S.W.
      • Lang T.
      • Rizzoli S.O.
      Multi-protein assemblies underlie the mesoscale organization of the plasma membrane.
      ,
      • Saka S.K.
      • Vogts A.
      • Krohnert K.
      • Hillion F.
      • Rizzoli S.O.
      • Wessels J.T.
      Correlated optical and isotopic nanoscopy.
      ). Recently, Nano-SIMS was coupled with click-chemistry based labeling of individual proteins (SPILL,
      • Vreja I.C.
      • Kabatas S.
      • Saka S.K.
      • Krohnert K.
      • Hoschen C.
      • Opazo F.
      • Diederichsen U.
      • Rizzoli S.O.
      Secondary-ion mass spectrometry of genetically encoded targets.
      ) allowing their precise visualization and cellular protein turnover. This clearly opens new possibilities for not only the identification and quantification of a particular cellular proteome but also to address its dynamics in situ.

      Conclusions and Future Perspectives

      Despite 15 years of progress proteomics of the synapse is still an evolving field. The technological advances described above and the combination of different omics will allow for much deeper insights into signaling networks and the topology of signaling pathways in the near future. To this end the full gamut of approaches will also include systems biology. It is possible that exchange rates at synapses, de novo protein synthesis and degradation are overrated as the key determinants of plasticity in synaptic function and that post-translational modifications well beyond phosphoproteomics have their share in altering synaptic strength even at time scales of hours and longer. The appealing concept of the tri- and tetrapartite synapse has gained considerable interest in recent years and some of the technological advances that will contribute to a deeper appreciation how different cellular membranes interact with matrix components to establish changes in neuronal connectivity. Several lines of evidence suggest that mainly mRNA splicing and much less gene transcription is altered in response to enhanced synaptic activity. Modern proteomic approaches with a much better peptide coverage will help to determine the role of alternative splicing or the usage of alternative start codons for activity-dependent protein expression in synaptic function.

      Acknowledgments

      We thank Dr. Karin Richter for contributing Fig. 1 and Dr. Marina Mikhaylova for help with Fig. 2 and critical comments on the manuscript. We apologize to those colleagues whose work we were unable to cite owing to space limitation.

      REFERENCES

        • Walikonis R.S.
        • Jensen O.N.
        • Mann M.
        • Provance Jr., D.W.
        • Mercer J.A.
        • Kennedy M.B.
        Identification of proteins in the postsynaptic density fraction by mass spectrometry.
        J. Neurosci. 2000; 20: 4069-4080
        • Satoh K.
        • Takeuchi M.
        • Oda Y.
        • Deguchi-Tawarada M.
        • Sakamoto Y.
        • Matsubara K.
        • Nagasu T.
        • Takai Y.
        Identification of activity-regulated proteins in the postsynaptic density fraction.
        Genes Cells. 2002; 7: 187-197
        • Jordan B.A.
        • Fernholz B.D.
        • Boussac M.
        • Xu C.
        • Grigorean G.
        • Ziff E.B.
        • Neubert T.A.
        Identification and verification of novel rodent postsynaptic density proteins.
        Mol. Cell. Proteomics. 2004; 3: 857-871
        • Li K.W.
        • Hornshaw M.P.
        • Van Der Schors R.C.
        • Watson R.
        • Tate S.
        • Casetta B.
        • Jimenez C.R.
        • Gouwenberg Y.
        • Gundelfinger E.D.
        • Smalla K.H.
        • Smit A.B.
        Proteomics analysis of rat brain postsynaptic density. Implications of the diverse protein functional groups for the integration of synaptic physiology.
        J. Biol. Chem. 2004; 27: 987-1002
        • Li K.
        • Hornshaw M.P.
        • Van Minnen J.
        • Smalla K.H.
        • Gundelfinger E.D.
        • Smit A.B.
        Organelle proteomics of rat synaptic proteins: correlation-profiling by isotope-coded affinity tagging in conjunction with liquid chromatography-tandem mass spectrometry to reveal postsynaptic density specific proteins.
        J. Proteome Res. 2005; 4: 725-733
        • Peng J.
        • Kim M.J.
        • Cheng D.
        • Duong D.M.
        • Gygi S.P.
        • Sheng M.
        Semiquantitative proteomic analysis of rat forebrain postsynaptic density fractions by mass spectrometry.
        J. Biol. Chem. 2004; 279: 21003-21011
        • Yoshimura Y.
        • Yamauchi Y.
        • Shinkawa T.
        • Taoka M.
        • Donai H.
        • Takahashi N.
        • Isobe T.
        • Yamauchi T.
        Molecular constituents of the postsynaptic density fraction revealed by proteomic analysis using multidimensional liquid chromatography-tandem mass spectrometry.
        J. Neurochem. 2004; 88: 759-768
        • Liu S.H.
        • Cheng H.H.
        • Huang S.Y.
        • Yiu P.C.
        • Chang Y.C.
        Studying the protein organization of the postsynaptic density by a novel solid phase- and chemical cross-linking-based technology.
        Mol. Cell. Proteomics. 2006; 5: 1019-1032
        • Collins M.O.
        • Husi H.
        • Yu L.
        • Brandon J.M.
        • Anderson C.N.
        • Blackstock W.P.
        • Choudhary J.S.
        • Grant S.G.
        Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome.
        J. Neurochem. 2006; 97: 16-23
        • Cheng D.
        • Hoogenraad C.C.
        • Rush J.
        • Ramm E.
        • Schlager M.A.
        • Duong D.M.
        • Xu P.
        • Wijayawardana S.R.
        • Hanfelt J.
        • Nakagawa T.
        • Sheng M.
        • Peng J.
        Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum.
        Mol. Cell. Proteomics. 2006; 5: 1158-1170
        • Distler U.
        • Schmeisser M.J.
        • Pelosi A.
        • Reim D.
        • Kuharev J.
        • Weiczner R.
        • Baumgart J.
        • Boeckers T.M.
        • Nitsch R.
        • Vogt J.
        • Tenzer S.
        In-depth protein profiling of the postsynaptic density from mouse hippocampus using data-independent acquisition proteomics.
        Proteomics. 2014; 14: 2607-2613
        • Bayés A.
        • van de Lagemaat L.N.
        • Collins M.O.
        • Croning M.D.
        • Whittle I.R.
        • Choudhary J.S.
        • Grant S.G.
        Characterization of the proteome, diseases and evolution of the human postsynaptic density.
        Nat. Neurosci. 2011; 14: 19-21
        • Caroni P.
        • Donato F.
        • Muller D.
        Structural plasticity upon learning: regulation and functions.
        Nat. Rev. Neurosci. 2012; 13: 478-490
        • Hanus C.
        • Schuman E.M.
        Proteostasis in complex dendrites.
        Nat. Rev. Neurosci. 2013; 14: 638-648
        • Ebrahimi S.
        • Okabe S.
        Structural dynamics of dendritic spines: molecular composition, geometry, and functional regulation.
        Biochim. Biophys. Acta. 2014; 1838: 2391-2398
        • Rosenberg T.
        • Gal-Ben-Ari S.
        • Dieterich D.C.
        • Kreutz M.R.
        • Ziv N.E.
        • Gundelfinger E.D.
        • Rosenblum K.
        The roles of protein expression in synaptic plasticity and memory consolidation.
        Front. Mol. Neurosci. 2014; 7: 86
        • Sala C.
        • Segal M.
        Dendritic spines: the locus of structural and functional plasticity.
        Physiol. Rev. 2014; 94: 141-188
        • Hanus C.
        • Ehlers M.D.
        Secretory outposts for the local processing of membrane cargo in neuronal dendrites.
        Traffic. 2008; 9: 1437-1445
        • Ehlers M.D.
        Dendritic trafficking for neuronal growth and plasticity.
        Biochem. Soc. Trans. 2013; 41: 1365-1382
        • Maeder C.I.
        • Shen K.
        • Hoogenraad C.C.
        Axon and dendritic trafficking.
        Curr. Opin. Neurobiol. 2014; 27: 165-170
        • O'Rourke N.A.
        • Weiler N.C.
        • Micheva K.D.
        • Smith S.J.
        Deep molecular diversity of mammalian synapses: why it matters and how to measure it.
        Nat. Rev. Neurosci. 2012; 13: 365-379
        • Busse B.
        • Smith S.
        Automated analysis of a diverse synapse population.
        PLoS Comput. Biol. 2013; 9: e1002976
        • Sorra K.E.
        • Harris K.M.
        Overview on the structure, composition, function, development, and plasticity of hippocampal dendritic spines.
        Hippocampus. 2000; 10: 501-511
        • Carlisle H.J.
        • Kennedy M.B.
        Spine architecture and synaptic plasticity.
        Trends Neurosci. 2005; 28: 182-187
        • Ventura R.
        • Harris K.M.
        Three-dimensional relationships between hippocampal synapses and astrocytes.
        J. Neurosci. 1999; 19: 6897-6906
        • Higley M.J.
        • Sabatini B.L.
        Calcium signaling in dendritic spines.
        Cold Spring Harb. Perspect. Biol. 2012; 4: a005686
        • Raghuram V.
        • Sharma Y.
        • Kreutz M.R.
        Ca(2+) sensor proteins in dendritic spines: a race for Ca(2+).
        Front. Mol. Neurosci. 2012; 5: 61
        • Noguchi J.
        • Matsuzaki M.
        • Ellis-Davies G.C.
        • Kasai H.
        Spine-neck geometry determines NMDA receptor-dependent Ca2+ signaling in dendrites.
        Neuron. 2005; 46: 609-622
        • Grunditz A.
        • Holbro N.
        • Tian L.
        • Zuo Y.
        • Oertner T.G.
        Spine neck plasticity controls postsynaptic calcium signals through electrical compartmentalization.
        J. Neurosci. 2008; 28: 13457-13466
        • Colgan L.A.
        • Yasuda R.
        Plasticity of dendritic spines: subcompartmentalization of signaling.
        Annu. Rev. Physiol. 2014; 76: 365-385
        • Sheng M.
        • Hoogenraad C.C.
        The postsynaptic architecture of excitatory synapses: a more quantitative view.
        Annu. Rev. Biochem. 2007; 76: 823-847
        • Sheng M.
        • Kim E.
        The postsynaptic organization of synapses.
        Cold Spring Harb. Perspect. Biol. 2011; 3 (pii: a005678)
        • Fischer M.
        • Kaech S.
        • Knutti D.
        • Matus A.
        Rapid actin-based plasticity in dendritic spines.
        Neuron. 1998; 20: 847-854
        • Hotulainen P.
        • Hoogenraad C.C.
        Actin in dendritic spines: connecting dynamics to function.
        J. Cell Biol. 2010; 189: 619-629
        • Dent E.W.
        • Merriam E.B.
        • Hu X
        The dynamic cytoskeleton: backbone of dendritic spine plasticity.
        Curr. Opin. Neurobiol. 2011; 21: 175-181
        • Star E.N.
        • Kwiatkowski D.J.
        • Murthy V.N.
        Rapid turnover of actin in dendritic spines and its regulation by activity.
        Nat. Neurosci. 2002; 5: 239-246
        • Oertner T.G.
        • Matus A.
        Calcium regulation of actin dynamics in dendritic spines.
        Cell Calcium. 2005; 37: 477-482
        • Gundelfinger E.D.
        • Fejtova A.
        Molecular organization and plasticity of the cytomatrix at the active zone.
        Curr. Opin. Neurobiol. 2012; 22: 423-430
        • Sigrist S.J.
        • Schmitz D.
        Structural and functional plasticity of the cytoplasmic active zone.
        Curr. Opin. Neurobiol. 2011; 21: 144-150
        • Bayés A.
        • Grant S.G.
        Neuroproteomics: understanding the molecular organization and complexity of the brain.
        Nat. Rev. Neurosci. 2009; 10: 635-646
        • Emes R.D.
        • Pocklington A.J.
        • Anderson C.N.
        • Bayés A.
        • Collins M.O.
        • Vickers C.A.
        • Croning M.D.
        • Malik B.R.
        • Choudhary J.S.
        • Armstrong J.D.
        • Grant S.G.
        Evolutionary expansion and anatomical specialization of synapse proteome complexity.
        Nat. Neurosci. 2008; 11: 799-806
        • Laβek M.
        • Weingarten J.
        • Volknandt W.
        The synaptic proteome.
        Cell Tissue Res. 2015; 359: 255-265
        • Weingarten J.
        • Lassek M.
        • Mueller B.F.
        • Rohmer M.
        • Lunger I.
        • Baeumlisberger D.
        • Dudek S.
        • Gogesch P.
        • Karas M.
        • Volknandt W.
        The proteome of the presynaptic active zone from mouse brain.
        Mol. Cell. Neurosci. 2014; 59: 106-118
        • Takamori S.
        • Holt M.
        • Stenius K.
        • Lemke E.A.
        • Grønborg M.
        • Riedel D.
        • Urlaub H.
        • Schenck S.
        • Brügger B.
        • Ringler P.
        • Müller S.A.
        • Rammner B.
        • Gräter F.
        • Hub J.S.
        • De Groot B.L.
        • Mieskes G.
        • Moriyama Y.
        • Klingauf J.
        • Grubmüller H.
        • Heuser J.
        • Wieland F.
        • Jahn R.
        Molecular anatomy of a trafficking organelle.
        Cell. 2006; 127: 831-846
        • Grønborg M.
        • Pavlos N.J.
        • Brunk I.
        • Chua J.J.
        • Münster-Wandowski A.
        • Riedel D.
        • Ahnert-Hilger G.
        • Urlaub H.
        • Jahn R.
        Quantitative comparison of glutamatergic and GABAergic synaptic vesicles unveils selectivity for few proteins including MAL2, a novel synaptic vesicle protein.
        J. Neurosci. 2010; 30: 2-12
        • Boyken J.
        • Grønborg M.
        • Riedel D.
        • Urlaub H.
        • Jahn R.
        • Chua J.J.
        Molecular profiling of synaptic vesicle docking sites reveals novel proteins but few differences between glutamatergic and GABAergic synapses.
        Neuron. 2013; 78: 285-297
        • Selimi F.
        • Cristea I.M.
        • Heller E.
        • Chait B.T.
        • Heintz N.
        Proteomic studies of a single CNS synapse type: the parallel fiber/purkinje cell synapse.
        PLos Biol. 2009; 7: e83
        • Husi H.
        • Ward M.A.
        • Choudhary J.S.
        • Blackstock W.P.
        • Grant S.G.
        Proteomic analysis of NMDA receptor-adhesion protein signaling complexes.
        Nat. Neurosci. 2000; 3: 661-669
        • Farr C.D.
        • Gafken P.R.
        • Norbeck A.D.
        • Doneanu C.E.
        • Stapels M.D.
        • Barofsky D.F.
        • Minami M.
        • Saugstad J.A.
        Proteomic analysis of native metabotropic glutamate receptor 5 protein complexes reveals novel molecular constituents.
        J. Neurochem. 2004; 91: 438-450
        • Fukata Y.
        • Tzingounis A.V.
        • Trinidad J.C.
        • Fukata M.
        • Burlingame A.L.
        • Nicoll R.A.
        • Bredt D.S.
        Molecular constituents of neuronal AMPA receptors.
        J. Cell Biol. 2005; 169: 399-404
        • von Engelhardt J.
        • Mack V.
        • Sprengel R.
        • Kavenstock N.
        • Li K.W.
        • Stern-Bach Y.
        • Smit A.B.
        • Seeburg P.H.
        • Monyer H.
        CKAMP44: a brain-specific protein attenuating short-term synaptic plasticity in the dentate gyrus.
        Science. 2010; 327: 1518-1522