Deep and Precise Quantification of the Mouse Synaptosomal

Article pubs.acs.org/jpr

Deep and Precise Quantification of the Mouse Synaptosomal Proteome Reveals Substantial Remodeling during Postnatal Maturation Kaja Ewa Moczulska,†,∥ Peter Pichler,†,∥ Michael Schutzbier,‡ Alexander Schleiffer,† Simon Rumpel,*,†,⊥ and Karl Mechtler*,†,§,⊥ †

Research Institute of Molecular Pathology, Dr. Bohr-Gasse 7, 1030 Vienna, Austria Gregor Mendel Institute of Molecular Plant Biology, Dr. Bohr-Gasse 3, 1030 Vienna, Austria § Institute of Molecular Biotechnology, Dr. Bohr-Gasse 3, 1030 Vienna, Austria ‡

S Supporting Information *

ABSTRACT: During postnatal murine maturation, behavioral patterns emerge and become shaped by experience-dependent adaptations. During the same period, the morphology of dendritic spines, the morphological correlates of excitatory synapses, is known to change, and there is evidence of concurrent alterations of the synaptosomal protein machinery. To obtain comprehensive and quantitative insights in the developmental regulation of the proteome of synapses, we prepared cortical synaptosomal fractions from a total of 16 individual juvenile and adult mouse brains (age 3 or 8 weeks, respectively). We then applied peptide-based iTRAQ labeling (four pools of 4 animals) and high-resolution twodimensional peptide fractionation (99 SCX fractions and 3 h reversed-phase gradients) using a hybrid CID−HCD acquisition method on a Velos Orbitrap mass spectrometer to identify a comprehensive set of synaptic proteins and to quantify changes in protein expression. We obtained a data set tracking expression levels of 3500 proteins mapping to 3427 NCBI GeneIDs during development with complete quantification data available for 3422 GeneIDs, which, to the best of our knowledge, constitutes the deepest coverage of the synaptosome proteome to date. The inclusion of biological replicates in a single mass spectrometry analysis demonstrated both high reproducibility of our synaptosome preparation method as well as high precision of our quantitative data (correlation coefficient R = 0.87 for the biological replicates). To evaluate the validity of our data, the developmental regulation of eight proteins identified in our analysis was confirmed independently using western blotting. A gene ontology analysis confirmed the synaptosomal nature of a large fraction of identified proteins. Of note, the set of the most strongly regulated proteins revealed candidates involved in neurological processes in health and disease states. This highlights the fact that developmentally regulated proteins can play additional roles in neurological disease processes. All data have been deposited to the ProteomeXchange with identifier PXD000552. KEYWORDS: Quantitative proteomics, mouse synaptosomal proteome, postnatal maturation, high-resolution strong cation exchange chromatography, iTRAQ

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INTRODUCTION

structure, called the postsynaptic density (PSD) in central glutamatergic synapses, which serves as the main scaffold for neurotransmitter receptors. Additionally, other organelles like mitochondria and the endoplasmic reticulum can localize to the synapse.1,2 Physiological studies have revealed a high degree of functional heterogeneity among synapses, even when originating from the same neuron3 or residing on the same neuron.4 Furthermore, chronic imaging studies in mice have shown that synapses are dynamic structures that turn over under basal

The main property that distinguishes the nervous tissue from all other tissues is the high degree of interconnectedness among cells. These physical contacts, i.e., synapses, are essential for the network function of neuronal circuits, and long-lasting plastic adaptations in synapses are believed to represent a neuronal correlate of memories. Chemical synapses in the mammalian brain show a complex microanatomy. The presynaptic compartment typically consists of a synaptic bouton filled with presynaptic vesicles, some of which are docked in the active zone, regulating the temporally highly precise release of vesicles into the synaptic cleft upon calcium increases. On the opposing postsynaptic side resides a complex molecular © 2014 American Chemical Society

Received: May 6, 2014 Published: August 26, 2014 4310

dx.doi.org/10.1021/pr500456t | J. Proteome Res. 2014, 13, 4310−4324

Journal of Proteome Research

Article

labeling to quantitatively analyze changes in the synaptic proteome during development of the mouse brain to identify strongly regulated proteins. In order to expand the sensitivity and therefore the protein detection range, we developed an improved variant of multidimensional protein identification technology (MudPIT).41 Specifically, we applied two-dimensional peptide fractionation with high resolution in both the first and the second dimensions. Using a special 25 cm × 1 mm Polysulfethyl-A column for strong cation exchange chromatography in the first dimension permitted us to analyze 99 disparate SCX fractions (Figure 1b). For data acquisition on a Velos Orbitrap mass spectrometer, we applied our previously described hybrid data acquisition method, acquiring two tandem spectra for each precursor ion: an ion trap fragment mass spectrum using collision-induced dissociation (CID) for identification and an Orbitrap fragment mass spectrum using higher energy collisional dissociation (HCD) for quantification.42 The high precision and technical reproducibility of the method was described previously.40 We here evaluated the biological reproducibility by comparing two biological replicates within a single iTRAQ experiment: The high correlation coefficient of R = 0.87 confirmed excellent reproducibility of our approach (Figure 2a). In summary, we detected 3500 synaptosomal proteins in a single experiment and quantified the developmental expression level of 3422 NCBI GeneIDs during early postnatal development from adolescence (3 weeks) to early adulthood (8 weeks).

conditions or in response to behaviorally relevant experiences.5−9 Interestingly, the cellular and anatomical structure of the brain changes during development and stabilizes during adulthood.10 Mirroring this, the population of postsynaptic spines, the morphological correlates of glutamatergic synapses on pyramidal neurons in the cortex,11,12 changes in its composition during development. During early development, spines typically turn over at much higher rates and feature more elongated and thin morphologies; at more mature ages, an increasing fraction of spines becomes more persistent and shows larger sizes and more mushroom-like morphologies.13−16 How are these structural and functional changes represented on the molecular level? In this study, we aimed to quantitatively and comprehensively investigate the protein changes correlated with brain development, using large-scale high-throughput analysis of the synaptic proteome during maturation (Figure 1a). Quantitative proteomics studies of neural tissue are considered to bear potential in providing insight into neurobiology in health and disease states.17−25 An essential prerequisite to obtain mechanistic insight into the functional and morphological heterogeneity of synapses, at a single time point as well as during development, is the identification of the key proteins constituting synaptic structures. The full complexity of synaptic structures is revealed on the molecular level: The synaptic proteome likely evolved from a set of interacting proteins that existed even before the evolution of the nervous system. A significant fraction (∼25%) of PSD proteins have direct orthologues in the unicellular organism Saccharomyces cerevisiae.26,27 A major breakthrough in the identification of proteins present in the synapse was the application of mass spectrometry (MS) methods, often applied to synaptosomal fractions prepared from dissociated brain tissue. The synaptosomal fraction is a membranous fraction highly enriched in structures that previously formed synapses.28,29 Previous proteomic analyses of synaptosomal samples revealed a high complexity of its proteome.30−32 Different approaches were applied to improve protein detection from these structures. Synaptosomes can be further subfractionated into PSD,30,33 membrane rafts,33 synaptic membranes,34 active zone,35 synaptic vesicles,30,36,37 and synaptic mitochondria.30 Also, detection of proteins was performed by gel electrophoresis separation and subsequent analysis of separate fractions obtained from gel slices.35 It should be kept in mind that further fractionation can significantly improve the detection of the protein content in samples, but the approach suffers the potential drawback that some proteins may get lost during the more intricate preparation procedure and that high biological reproducibility might be difficult to ensure. To date, more than 1000 different proteins in the PSDs and a number of proteins in other synaptic compartments have been identified.27 Earlier studies addressing changes in the cortical synaptic proteome upon development32,34 opened the door to more detailed investigations of this interesting aspect of synaptic proteome regulation. The ongoing generation of novel tools in the dynamic field of protein mass spectrometry allows increasingly more sensitive and detailed analyses of complex proteomes. Quantitative proteomics using labeling with isobaric tags for relative and absolute quantification (iTRAQ) is a potent method of proteome analysis allowing comparison of multiple proteomes in single experiments.38−40 Here, we applied iTRAQ

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MATERIALS AND METHODS

Mouse Strains

Male C57BL/6J wild-type mice bred in-house were used. Animals were divided in two age groups: 3 and 8 weeks old (wo). Antibodies

The following primary antibodies were used for protein band detection by western blotting: myelin oligodendrocyte protein (MOG; 1:1000, Abcam, ab32760), oligodendrocyte specific protein, Claudin11 (Cldn11; 1:1000, Abcam, ab53041), Doublecortin (Dcx; 1:1000 Abcam, ab18723), Synapsin 1 (Syn1; 1:4000, Millipore AB1543P), v-erb-a erythroblastic leukemia viral oncogene homologue 4 (ErbB-4; 1:200, Santa Cruz, sc-283), discs, large homologue 4 (Dlg4 or PSD-95; 1:2000, Cell Signaling Technology, 3450), Fyn proto-oncogene (Fyn; 1:500, Millipore, 04−353), and synaptic Ras GTPase activating protein 1 homologue (SynGAP; 1:1000, Thermo Fisher Scientific, PA1-046); an antibody against actin (1:2000, Abcam, ab3280) was used as a loading control. Synaptosomal Preparation

We followed a previously published protocol that was slightly modified.28,29 Briefly, mice were sacrificed, and the cortex was dissected and transferred to ice-cold synaptosomal buffer (10 mM HEPES, 1 mM EDTA, 2 mM EGTA, 0.5 mM DTT, 0.32 M sucrose, pH 7) containing protein inhibitors (1 tablet/ 10 mL, Roche). Samples were homogenized using a bead homogenizer (Precellys 24, Precellys) 3 times for 30 s with 1 min breaks at 6000 rpm. Homogenized samples were centrifuged for 10 min at 1000g and 4 °C. The supernatant was transferred to a new tube and centrifuged for 40 min at 10 000g and 4 °C. The obtained pellet was resuspended in cold synaptosomal buffer, layered over a discontinuous sucrose gradient (0.85 M/1 M/1.18 M sucrose), and centrifuged at 4311



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20 000 rpm (50 512g), 4 °C, for 2 h (Optima L-90K ultracentrifuge, Beckman Coulter). We collected the fraction from the 1 M/1.18 M sucrose interface (synaptosomal fraction) and stored it at −80 °C.

equilibration. SCX solvent solutions were A: 5 mM sodium phosphate buffer, pH 2.7, containing 15% acetonitrile (ACN); B: same as A, supplemented with 0.5 M NaCl; C: same as A but pH 6.0. One hundred forty three 1 min fractions were collected. The volume was reduced in a speed-vac. Fractions were resuspended in 0.1% trifluoroacetic acid (TFA). Fractions with low UV signal were pooled. In total, 99 samples were analyzed by LC−MS.

Protein Preparation for Mass Spectrometry and Western Blot

On the day before isobaric tagging, synaptosomal samples were thawed on ice, diluted to 1 mL with water, and pipetted dropwise into 9 mL of 90% ice-cold acetone (Applichem) to a final acetone concentration of approximately 80%. The samples were gently mixed, and proteins were precipitated overnight (o/n) at −20 °C. The next day, the samples were centrifuged for 10 min at 3500g. The supernatant was discarded, and the pellet was washed twice with 80% acetone and once with pure acetone to remove remaining membranes and sucrose. Proteins were resuspended in ∼30 μL of 0.5 M triethylammonium bicarbonate (TEAB) and 1% Rapigest (Waters) for mass spectrometry analysis and in ∼100 μL of phosphate buffered saline (PBS) containing 0.1% Rapigest for western blot. The protein concentration was measured using bicinchoninic acid (BCA) assay with reducing agent (Pierce) for mass spectrometry or Bradford assay (Biorad) for western blot. The same amount of protein from 4 independent preparations (cortices from 4 individual animals of the same age group) was mixed to prepare a sample further analyzed by mass spectrometry or western blot. In total, we used 16 cortices to prepare 4 samples: two samples, 3wo-1 and 3wo-2, were each prepared from 4 cortices from 3 wo animals, and two samples, 8wo-1 and 8wo-2, were each prepared from 4 cortices from 8 wo animals.

Second Dimension Peptide Separation by Ultra-High-Pressure LC using 3 h Gradients

SCX fractions were separated on an Ultimate 3000 RSLCnano system (Thermo Fisher Scientific) equipped with a 25 cm × 75 μm ID Acclaim PepMap RSLC C18 column (2 μm, 100 Å), operated at a column flow rate of 275 nL/min. Peptides were first concentrated on a 2 cm × 100 μm ID Acclaim PepMap C18 precolumn (5 μm, 100 Å) with 0.1% TFA at a loading pump flow rate of 5 μL/min for 20 min. At this time point, the precolumn was switched in-line with the analytical column. The LC gradient started with 98% A and 2% B for 20 min, followed by a linear gradient to 25% B at 195 min, then to 90% B at 200 min maintained for 5 min, and then decreased to 2% B within 2 min, which was maintained for 23 min to equilibrate the analytical column. Solvent solutions were A: 2% ACN, 0.1% formic acid (FA) and B: 80% ACN, 10% trifluoroethanol, and 0.08% FA. Trifluoroethanol was included in order to reduce memory effects (carry-over).43 Data acquisition was started by a contact closure signal from the LC at 25.0 min. Mass Spectrometry

An LTQ Velos Orbitrap ETD (Thermo Fisher Scientific) mass spectrometer was coupled online with the RSLC via a nanoelectrospray ion source (Proxeon, Thermo Fisher Scientific). The mass spectrometer was operated in positive ion mode with a spray voltage of 2.0 kV, a capillary temperature of 250 °C, and an acquisition time of 200 min. An AGC target value of 1 × 106 and a maximum ion collection time of 500 ms were set for the MS1 scan, which was acquired at a resolution of 60 000 (at 400 m/z) in the range 350−2000 m/z. Lock mass option was enabled, and the polydimethylcyclosiloxane ions at m/z 445.120024 were used for internal calibration. A hybrid data acquisition method was used42 where both a CID and HCD scan were acquired for the six most abundant precursor ions, excluding singly charged ions. Monoisotopic precursor selection, FT master scan preview mode, and chromatography mode were enabled (10 s expected peak width, correlation 0.8). AGC target value and maximum ion collection time were 1 × 104 and 200 ms for CID scans, and 1 × 105 and 250 ms for HCD scans. For the CID scan, normalized collision energy (CE) was set to 35, activation time was 10 ms, and activation Q was 0.25. For the HCD scan, CE was set to 50, and HCD spectra were recorded at a resolution of 7500 (at 400 m/z) starting at 100 m/z to ensure coverage of the iTRAQ reporter ion range. Multistage activation was enabled for CID scans with neutral loss mass list 32.6, 49, and 98. Isolation width was 1.6 m/z for both CID and HCD scans. Dynamic exclusion was enabled with a window of ±7.5 ppm and 180 s duration.

iTRAQ Labeling

The four samples, 3wo-1, 3wo-2, 8wo-1, and 8wo-2, were processed individually. Proteins were reduced by addition of 1:10 (v/v) 50 mM tris(2-carboxyethyl)phosphine (TCEP) and incubation at 60 °C for 1 h and were alkylated by adding 1:20 (v/v) 200 mM methylmethanethiosulfonate (MMTS) for 15 min. Samples were digested o/n at 37 °C using Trypsin (1:20 w/w). iTRAQ labeling was performed by adding one of the four iTRAQ reagents supplemented with 70 μL of ethanol to each of the 4 samples and incubation for 2 h. Samples were labeled in the following way: 3wo-1, iTRAQ 114; 3wo-2, iTRAQ 115; 8wo-1, iTRAQ 116; and 8wo-2, iTRAQ 117. Labeling efficiency was monitored by measuring each labeled sample individually to ensure that the percentage of labeled peptides detected in a 90 min gradient analysis was above 99% when iTRAQ labels were searched as variable modifications. The 4 iTRAQ-labeled samples were then mixed to obtain a combined iTRAQ sample, which was acidified with formic acid, lyophylized, and redissolved in strong cation exchange (SCX) solvent solution A. First Dimension Peptide Separation by High-Resolution SCX

High-resolution SCX was performed on an UltiMate nano LC system (Thermo Fisher Scientific) equipped with a 25 cm × 1 mm Polysulfethyl-A (3 μm) column (PolyLC), which was operated at a flow rate of 50 μL/min using a ternary salt and pH gradient: 20 min 100% A, followed by linear gradients first to 10% B and 50% C in 80 min, then to 25% B and 50% C in 10 min, and subsequently to 50% B and 50% C in 5 min, which was maintained for a further 15 min, and then back to 100% A within 5 min and maintained for further 14 min for column re-

Peptide Identification and Quantification

Proteome Discoverer 1.2.0.208 was used for the analysis of mass spectrometry data: After peak picking within Proteome Discoverer (no filtering of peaks or spectra: s/n, 0; min, 1 peak; precursor mass range, 300−10 000), ion trap CID spectra were searched with the SEQUEST version implemented in 4312



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Table 1. Proteins Regulated during Postnatal Maturationa a

b

GeneID

symbol

a1/y1

a2/y2

mean

var. %

GeneID

symbol

y1/a1

y2/a2

mean

var. %

12955 226115 12521 193740 66222 14862 17441 72948 11484 19293 215814 14555 77767 18952 27984 105853 22145 76376 16409 12490 12032 20265 13430 16485 212531 319991 68404 58233 19290 12799 14707 17876 20525 56839 18377 23969 21960 12260 76238 226180 16522 230904 76886 635960 13807 13003 11423 20322 380836 69017 12349 53857 20964 11829 72961 74326 22139 56216 170719

Cryab Opalin Cd82 Hspa1a Serpinb1a Gstm1 Mog Tppp Aspa Pvalb Ccdc28a Gpd1 Ermn Sept4 Efhd2 Mal2 Tuba4a Slc24a2 Itgam Cd34 Bcan Scn1a Dnm2 Kcna1 Sh3bgrl2 Kif6 Nrn1 Dnaja4 Pura Cnp Gng5 Myef2 Slc2a1 Lgi1 Omg Pacsin1 Tnr C1qb Grhpr Ina Kcnj6 Fbxo2 Fam81a Ak3l2 ps Eno2 Vcan Ache Sord Mrs2 Prrt2 Car2 Tuba8 Syn1 Aqp4 Slc17a7 Hnrnpr Ttr Stx1b Oxr1

4.60 3.13 2.77 2.41 2.26 2.09 2.67 2.20 2.16 1.74 2.28 1.90 2.09 1.84 1.75 1.79 1.81 1.60 1.63 1.91 1.76 1.65 1.59 1.63 1.81 1.57 1.73 1.62 1.62 1.95 1.50 1.94 1.64 1.63 1.61 1.64 1.76 1.67 1.62 1.72 1.90 1.58 1.58 1.68 1.60 1.64 1.61 1.64 1.54 1.57 1.57 1.48 1.56 1.55 1.55 1.67 1.44 1.54 1.50

4.64 2.37 2.60 2.46 2.29 2.33 1.79 2.13 2.01 2.39 1.82 2.06 1.63 1.85 1.87 1.77 1.73 1.85 1.80 1.54 1.65 1.75 1.81 1.74 1.56 1.80 1.63 1.73 1.71 1.41 1.82 1.40 1.66 1.67 1.68 1.65 1.53 1.61 1.64 1.55 1.39 1.66 1.66 1.55 1.62 1.58 1.60 1.54 1.63 1.60 1.59 1.68 1.58 1.59 1.58 1.46 1.66 1.54 1.58

4.62 2.73 2.68 2.43 2.27 2.21 2.19 2.16 2.08 2.04 2.04 1.98 1.85 1.84 1.81 1.78 1.77 1.72 1.71 1.71 1.70 1.70 1.69 1.68 1.68 1.68 1.67 1.67 1.67 1.66 1.65 1.65 1.65 1.65 1.64 1.64 1.64 1.64 1.63 1.63 1.62 1.62 1.62 1.61 1.61 1.61 1.60 1.59 1.58 1.58 1.58 1.58 1.57 1.57 1.56 1.56 1.55 1.54 1.54

1 20 5 1 1 8 29 2 5 23 16 6 18 0 5 1 3 10 7 16 4 4 9 5 11 10 4 5 4 23 14 23 1 2 3 1 10 3 1 7 23 3 3 6 1 3 0 4 4 2 1 9 1 2 2 9 10 0 4

13435 17357 13052 13193 12140 633752 12367 21974 56702 19395 666634 13121 73710 404634 22240 97165 76142 216188 215690 15078 269695 22239 65254 269639 320827 56473 18846 78303 21923 81910 72542 17967 260315 319187 14137 94062 108682 15441 228071 14432 72075 271711 68087 94064 16592 76976 56457 20588 216197 53421 69737 15925 16906 93843 13589 21429 209630 20692 97122

Dnmt3a Marcksl1 Cxadr Dcx Fabp7 Gm7125 Casp3 Top2b Hist1h1b Rasgrp2 Gm8203 Cyp51 Tubb2b H2afy2 Dpysl3 Hmgb2 Ppp1r14c Aldh1l2 Nav1 H3f3a Rnft2 Ugt8a Dpysl5 Zfp512 C530008M17Rik Fads2 Plxna3 Hist3h2ba Tnc Rrbp1 Pgam5 Ncam1 Nav3 Hist1h2bn Fdft1 Mrpl3 Gpt2 Hp1bp3 Sestd1 Gap43 Ogfr Tmem169 Dcakd Mrpl27 Fabp5 Arxes2 Clptm1 Smarcc1 Ckap4 Sec61a1 Ttl Ide Lmnb1 Pnck Mapre1 Ubtf Frmd4a Sparc Hist2h4

4.17 3.12 3.25 3.33 3.18 2.65 2.42 2.11 1.98 2.21 1.89 1.92 2.03 2.36 1.97 1.90 1.69 1.87 1.74 1.87 1.63 1.75 1.72 1.77 1.65 1.64 1.77 1.72 1.78 1.70 1.73 1.64 1.74 1.61 1.57 1.58 1.56 1.51 1.64 1.56 1.60 1.66 1.69 1.54 1.61 1.56 1.58 1.56 1.71 1.72 1.73 1.52 1.52 1.50 1.58 1.45 1.80 1.44 1.52

5.18 3.50 3.08 2.86 2.92 3.08 2.64 2.58 2.54 2.14 2.29 2.22 2.07 1.78 2.05 2.06 2.20 1.98 2.11 1.92 2.13 1.94 1.90 1.82 1.95 1.92 1.77 1.81 1.73 1.79 1.75 1.81 1.68 1.81 1.81 1.78 1.80 1.86 1.71 1.80 1.72 1.64 1.60 1.76 1.67 1.72 1.69 1.72 1.56 1.56 1.54 1.73 1.73 1.75 1.64 1.77 1.43 1.78 1.69

4.65 3.30 3.16 3.09 3.05 2.86 2.52 2.33 2.25 2.18 2.08 2.06 2.05 2.05 2.01 1.98 1.93 1.92 1.92 1.89 1.86 1.84 1.81 1.80 1.79 1.78 1.77 1.76 1.75 1.75 1.74 1.72 1.71 1.71 1.68 1.68 1.68 1.68 1.68 1.67 1.66 1.65 1.64 1.64 1.64 1.64 1.64 1.64 1.64 1.63 1.63 1.62 1.62 1.62 1.61 1.60 1.60 1.60 1.60

16 8 4 11 6 11 6 14 18 2 13 10 1 20 3 6 19 4 13 2 19 7 7 2 12 11 0 4 2 3 1 7 3 9 10 8 10 15 3 10 5 1 4 10 2 7 5 7 6 7 8 9 9 11 3 14 16 15 8

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Table 1. continued a GeneID

symbol

227613 18164 72168 12322 20404 140559 20910 20203 237831 20511 12757 21991

Tubb4b Nptx1 Aifm3 Camk2a Sh3gl2 Igsf8 Stxbp1 S100b Slc13a5 Slc1a2 Clta Tpi1

b

a1/y1

a2/y2

mean

var. %

GeneID

1.55 1.38 1.54 1.49 1.54 1.53 1.50 1.43 1.68 1.46 1.51 1.48

1.53 1.71 1.52 1.57 1.52 1.53 1.54 1.59 1.35 1.55 1.49 1.52

1.54 1.53 1.53 1.53 1.53 1.53 1.52 1.51 1.50 1.50 1.50 1.50

1 15 1 4 1 0 1 8 16 4 1 2

67041 74126 20503 224805 27374 19299 20364 12359 12417 12764 268396 107932 66493 19242 16439 210853 15926 71795 15374 11640 27395 18120 15354 12321 66241 68816 26874 75805 21681 67437

symbol Oxct1 Syvn1 Slc16a7 Aars2 Prmt5 Abcd3 Sepw1 Cat Cbx3 Cmas Sh3pxd2b Chd4 Mrpl51 Ptn Itpr2 Zfp947 Idh1 Pitpnc1 Hn1 Akap1 Mrpl15 Mrpl49 Hmgb3 Calu Tmem9 Ppil1 Abcd2 Nln Alyref Ssr3

y1/a1

y2/a2

mean

var. %

1.62 1.78 1.46 1.64 1.63 1.53 1.67 1.51 1.58 1.51 1.68 1.36 1.47 1.36 1.43 1.54 1.51 1.56 1.43 1.60 1.54 1.43 1.43 1.44 1.49 1.50 1.57 1.48 1.34 1.46

1.58 1.43 1.74 1.54 1.56 1.65 1.51 1.66 1.57 1.64 1.46 1.79 1.64 1.76 1.67 1.55 1.58 1.50 1.62 1.45 1.50 1.61 1.61 1.59 1.53 1.51 1.43 1.52 1.68 1.53

1.60 1.59 1.59 1.59 1.59 1.59 1.59 1.58 1.57 1.57 1.57 1.56 1.56 1.55 1.55 1.55 1.54 1.53 1.52 1.52 1.52 1.52 1.52 1.51 1.51 1.50 1.50 1.50 1.50 1.50

2 16 12 5 3 5 7 7 0 6 10 20 8 18 11 0 3 3 9 7 2 8 9 7 2 0 7 1 16 3

a

GeneIDs and regulatory ratios of proteins upregulated 1.5-fold or higher in adult animals (a) or in young animals (b) with variability below 30% in the two biological replicate experiments (a1, adult1; a2, adult2; y1, young1; y2, young2). NCBI GeneIDs were assigned according to the NCBI database (http://www.ncbi.nlm.nih.gov/gene/). Geometric mean of ratios and measure of variability were calculated as described in Materials and Methods. Data was sorted according to geometric mean.

probability scores were calculated. Filter settings for peptide− spectrum matches (PSM) were as follows: search engine rank, 1; length, 7; Xcorr, 2.8; and SEQUEST probability, 25. Protein grouping was enabled in Proteome Discoverer, and apply strict maximum parsimony principle was set on. In case of the same set of peptides mapping to more than one protein sequence in the database, Proteome Discoverer selected a “group representative protein” based on criteria such as sequence coverage. Of note, we mapped group representative proteins to their corresponding NCBI GeneIDs. False discovery rates at the protein, peptide, and peptide−spectrum match levels were calculated as FDR = no. rev/no. fwd. Quantification was based on extraction of iTRAQ reporter ions from HCD Orbitrap spectra. Thermo Discoverer quantification settings were as follows: exact iTRAQ 4-plex reporter ion masses, 5 mmu integration window (centroid sum), apply quan value correction and normalize on protein median were selected. No minimum threshold or outlier removal procedures were applied. The quantitative ratio for each protein was calculated within Discoverer as the median of all iTRAQ ratios of PSMs passing the filter criteria (no outlier removal). To provide a measure of the variability of quantitative measurements, protein variability was calculated within Proteome Discoverer as sqrt(exp(stdev(r.i)2) − 1) where r.i

Proteome Discoverer 1.2.0.208. The search database contained all sequences from ENSMUSP.NCBIM37.59 supplemented with a set of common contaminants (together, 51 577 sequences). All sequences were included both in forward and reverse order, for a total of 103 154 sequences. Settings for peptide identification were as follows: precursor mass tolerance, 8 ppm; fragment mass tolerance, 0.6 Da; trypsin allowing max. two missed cleavage sites; scoring of b and y ions; static modifications, iTRAQ 4plex (peptide N-terminus and Lys) and Methylthio modification of Cys residues; dynamic modifications, oxidation (Met), deamidation (Asn, Gln) and iTRAQ 4plex (Tyr); max. 4 modifications per peptide. Because our sample preparation method involved overnight incubation for acetone precipitation, we evaluated the data set for the presence of acetone modification of Glycine (mass shift corresponding to a Gly → Pro conversion).44 We found evidence of acetone modification on approximately 5% of peptide−spectrum matches. However, because the extent of acetone modification appeared to be similar across the four iTRAQ samples and because acetone modification did not seem to introduce bias with regard to relative quantification ratios (data not shown), acetone modification was not included in the set of variable modifications for the SEQUEST search of the complete data set (all SCX fractions). In addition to Xcorr, SEQUEST 4314



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is the natural logarithm of the peptide ratios (details described in the Proteome Discoverer manual). Protein accessions mapped to NCBI GeneIDs are provided in Supporting Information Table S1 together with quantitative data for each GeneID. Mean designates the geometric mean of protein ratios determined in the biological replicates. To provide a measure of biological variance, the expression variability between the biological replicates was calculated for each GeneID, applying the formula implemented within Proteome Discoverer as mentioned above: in this case, r.i reflected the natural logarithm of the protein ratio data from the individual iTRAQ experiments. Identical calculations were used for the data presented in Table 1. A cutoff of 1.5-fold upor downregulation of the adult/young mean ratio was used to classify a protein as strongly regulated, and a variability below 30% was considered as an indication of consistent regulation. All identification and quantification data on the protein and peptide levels (peptide sequences, indication of which peptides are shared between groups and which are unique, information on missed cleavage, precursor charge and m/z, list of all observed modifications for each PSM, number of matched vs total ions, peptide identification scores Xcorr and SEQUEST peptide probability, protein accession, number of unique peptides assigned to a protein, percent sequence coverage, iTRAQ reporter ion intensities (isotope corrected), iTRAQ ratios on both PSM and protein level, protein ratio, and protein variability) were exported from Proteome Discoverer in xlsx format and are provided as Supporting Information Table S2.

terms was also applied to the subset of proteins found to be regulated according to Table 1, leading to Figure 2, panels c (upregulated in adult mice) and d (upregulated in young mice). For Table 1, the list of GeneIDs was filtered for strong (1.5-fold or higher) and consistent (variability in biological replicate experiments below 30%) regulation (variability calculated as described above). For comparison with other studies, the respective data sets were mapped to NCBI Entrez GeneIDs of the respective organisms in a similar manner, via conversion tables provided by the NCBI and MGI (mouse genome informatics, http:// www.informatics.jax.org/) or by NCBI-BLASTP. Studies that were not originally performed in mouse were mapped to the mouse NCBI GeneIDs via HomoloGene.47 In addition to HomoloGene, 26 rat GeneIDs were mapped to mouse GeneIDs by blast or via NCBI database links. With all data mapped to NCBI GeneID identifiers, comparative plots, as illustrated in Figures 3 and 5, were generated. Statistics for figures were calculated using the R programming language.48 The correlation of protein ratios between biological replicate experiments and the correlation of gene expression ratios between this data set and previously published results was calculated as Pearson product−moment correlation coefficient in log-transformed space. Western Blotting

Protein samples were prepared by mixing the protein solution with NuPAGE lithium dodecyl sulfate (LDS) sample buffer (Life Technologies) followed by denaturation of the sample at 90 °C for 30 min. Protein samples were loaded on the precast 10% polyacrylamide gel (NuPAGE Novex 10% Bis-Tris Gels, Life Technologies) next to 8 μL of the SeeBlue Plus2 prestained standard (Life Technologies). Protein electrophoresis was performed in running buffer (NuPAGE MES SDS Running Buffer, Life Technologies) using 120 V at room temperature. Proteins were transferred to poly(vinylidene fluoride) (PVDF) membrane (Millipore, Immobilon-FL). The membrane was incubated for 30 s to 1 min with methanol, quickly washed with deionized water, and incubated in transfer buffer (NuPAGE Transfer Buffer, Life Technologies, with 10% methanol for transferring one gel or 20% methanol for transferring 2 gels) for at least 5 min. Proteins were transferred to the membrane using wet transfer in the XCell II Blot Module (Life Technologies) for 2 h at room temperature using 30 V or overnight at 4 °C using 14 V. Western Dot 625 goat anti-mouse and anti-rabbit kits (Life Technologies) were used to detect protein bands. Western blotting was performed according to the manufacturer’s manual. Briefly, after protein transfer, the PVDF membranes were incubated for 2 h at room temperature or overnight at 4 °C with blocking buffer provided by manufacturer. Membranes were cut according to the corresponding bands of protein standard to enable the detection of the protein of interest and the detection of the loading control (actin) on the membrane of the same blot. Membranes were incubated with primary antibodies diluted in blocking buffer for 1 h at room temperature or overnight at 4 °C. Membranes were then washed with washing buffer 3 times for 5 min. Membranes were incubated with secondary antibodies (anti-mouse or anti-rabbit, Life Technologies) followed by washing with washing buffer for 5 min. Fluorescent Qdot 625 was conjugated to secondary antibodies by incubating the membrane for 45 min in 1:2000 concentration of Qdot 625 streptavidin conjugate diluted in

Bioinformatic Analysis

To facilitate further bioinformatic analyses such as GO annotation and comparisons with other data sets, the group representative proteins of the 3500 Ensembl protein groups were mapped to NCBI GeneIDs in the following way: 2619 proteins were mapped via a direct conversion table provided by ENSEMBL for conversion of EMSEMBL protein IDs to NCBI Entrez GeneIDs. 874 proteins were mapped via GeneSymbols, using the gene symbol derived from the description column, and matched to the NCBI GeneID via a conversion table provided by the NCBI (National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov/). The remaining 7 proteins were matched manually via NCBI-BLASTP against the NCBI REFSEQ database. In case of several protein isoforms mapping to the same GeneID, the protein with the more extreme quantitative regulation was selected. Gene Ontology analysis (Figure 2b) was performed with DAVID as follows:45,46 First, GO terms for cellular components were associated with all proteins. Then, for the list of GO terms depicted in Figure 2b, the GO term mapping to the largest number of proteins was determined. The respective proteins were counted and removed from the dataset, and the GO term mapping to the next largest number of proteins was determined. This ensured that proteins with multiple GO associations were assigned to a single category. Finally, proteins not mapping to any of the GO terms depicted in Figure 2b were counted and classified as “others”. Categories endoplasmic reticulum and Golgi were combined into ER−Golgi. Because the GO terms used for Figure 2b reflect a list of cellular locations and structures that may be associated with synaptosomal proteins according to previous reports, Figure 2b depicts a classification of the identified proteins into GO term categories that may be associated with synaptosome preparations. An analogous classification based on the same GO 4315



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Figure 1. Sample preparation and experimental workflow incorporating high-resolution strong cation exchange. (a) Mouse cortex from 16 animals was isolated, homogenized, and differentially centrifuged at 1000g and 10 000g. The pellet obtained after 10 000g centrifugation was redissolved and layered on a discontinuous sucrose gradient. The synaptosomal fraction was collected from the 1.18 M/1 M interface, and proteins were subsequently precipitated by cold acetone. The protein concentration was measured, and four pools (two young and two adult) were obtained by combining an equal amount of protein from individual synaptosomal preparations of four brains of identical age. The four protein samples were trypsinized and labeled with one of the four iTRAQ reagents. Subsequently, labeled samples were mixed, followed by off-line separation using strong cation exchange (SCX) chromatography. SCX fractions were analyzed by HPLC−MS using a 3 h gradient on a high-resolution nano HPLC system that was coupled online to a Velos Orbitrap mass spectrometer. Proteome Discoverer was used for peptide identification, protein inference was accomplished according to maximum parsimony principle, and quantification was based on iTRAQ reporter ion intensities. (b) High-resolution strong cation exchange permitted collection of 99 SCX fractions. More than half of all identified peptides were detected in only one or two SCX fractions.

where r, ratio; p1, protein of interest in sample 1; p2, protein of interest in sample 2; c1, loading control in sample 1; c2, and loading control in sample 2 Each protein was analyzed by western blot at least twice, and data was presented using geometric means. The geometric standard deviation was calculated as a measure of variability.

blocking buffer. Membranes were washed 2 times for 5 min with washing buffer and one time for 15 min in deionized water. Protein bands were directly visualized on membranes using UV detection. The fluorescence signal corresponding to the amount of detected proteins was quantified using ImageJ software. In order to calculate the actual amount of protein, signals of proteins of interest were normalized to the loading control (actin), and the ratio of the normalized values between two samples was calculated.

r=

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RESULTS

Deep and Precise Quantification of the Synaptosomal Proteome

Synaptosomal fractions were isolated individually from 16 cortices dissected from 8 juvenile (3 wo) and 8 adult (8 wo) mice. Two samples, 3wo-1 and 3wo-2, from juvenile animals and two samples, 8wo-1 and 8wo-2, from adult animals were generated by pooling an identical amount of protein from the

p1 p 2 / c1 c 2 4316



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Figure 2. Proteins detected by mass spectrometry. (a) 3500 mouse proteins mapping to 3427 NCBI GeneIDs were detected in cortical synaptosomal samples, with complete quantification data available for 3422 GeneIDs. Protein level ratios detected in independently prepared samples exhibit similar levels of regulation for duplicate analysis (correlation coefficient R = 0.87). Data are presented on a logarithmic scale. (b) GO term analysis of proteins detected in the sample. Please note that a considerable part of the detected proteins localizes to synapses or to structures know to be associated with synapses and synaptosomal preparations such as mitochondria. Interestingly, the category “other” may contain candidate proteins previously not yet associated with synapses. Of note, the subgroups of proteins found to be upregulated in adult mice (c) or in young mice (d) according to Table 1 also classify into these categories.

Our experimental approach allowed us to obtain a quantitative analysis of the proteomic composition of the synaptosomal fractions obtained from the mouse neocortex at 3 and 8 weeks of age, with two biological replicates analyzed within the same experiment. In total, we detected 3500 mouse proteins in our samples at 1% FDR on the protein level, corresponding to 3422 NCBI GeneIDs for which full quantitative information was available. Our data indicated considerable developmental regulation for many of the proteins in the sample. We used the biological duplicates within each of the analyzed age groups to assess how much of this variability may be attributed to methodological noise (combined biological and technical variability). Indeed, the differences of protein level expression between adult and juvenile animals detected in independently prepared samples were highly repetitive. To quantify this observation, we calculated the correlation coefficient of independently detected protein level ratios between both age groups. We observed a strong correlation in the developmental ratios of protein levels calculated from the two pairs of independent samples (R = 0.87) demonstrating a high degree of reproducibility in our approach (Figure 2a). Additionally, to analyze the relevance of detected proteins and to better estimate the novelty of obtained information, we compared the data set with previously published data. A number of proteomics studies were conducted to analyze the proteomes obtained from synaptosomes,30−32 subfractions of synaptosomes,30,33−35 and immunoprecipitated receptor complexes.30,50 Our list of detected proteins was consistent with prior studies and largely overlapped with the protein sets detected previously. Importantly, our data set significantly extends previously published findings by the detection of additional new targets (Figure 3). This confirmed first that our approach was suitable for the detection of synapse-associated proteins already published in previous studies and second that

synaptosomal preparations of four individual mice of the same age group (see Materials and Methods and Figure 1a). This experimental design allowed us on one hand to minimize variability arising from interindividual differences and sample preparation. On the other hand, two biological replicates were analyzed within the same iTRAQ experiment, allowing us to assess the precision and reproducibility of our approach and thus its usefulness for the analysis of cortical synaptosomal preparations. To ensure deep proteome coverage, we developed an improved variant of two-dimensional chromatography separation for peptide fractionation: The application of highresolution chromatography already in the first dimension using a ternary combined salt and pH gradient and a 25 cm × 1 mm Polysulfethyl-A strong cation exchange column providing adequate resolution allowed an off-line collection of 143 disparate SCX fractions. Fractions with low UV signal were pooled, and the resulting 99 SCX fractions were further subjected to high-resolution reversed-phase liquid chromatography using 3 h gradients,49 coupled online to mass spectrometry. Of note, more than half of all peptides were detected in only one or two of these SCX fractions (Figure 1b). Applying PSM filters and protein grouping according to maximum parsimony principle as described in Materials and Methods, we identified in total 129 872 peptide−spectrum matches (129 669 from mouse proteins and 203 from contaminants) and 59 decoy peptide−spectrum matches. These PSMs reflected 17 572 unique peptide sequences (17 558 mouse and 14 contaminant) and 33 unique decoy peptide sequences. Proteome Discoverer grouped the unique peptide sequences into 3506 protein groups (3500 mouse protein groups and 6 contaminants) and 33 decoy proteins. These values corresponded to an FDR at the PSM level of 0.05% (59/129 872), a peptide FDR of 0.19% (33/17 572), and a protein FDR below 1% (33/3506 = 0.94%). 4317



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Figure 3. Venn diagrams showing overlap of detected protein set with previously published results. Numbers indicate unique number of proteins in a given field: the relative component or the intersection of protein sets. Data is largely overlapping with protein sets detected in synaptosomes (a), synaptosomal fractions (b), coimmunoprecipitation of receptor complexes (c), and the combined protein set from all above-mentioned studies (d).

GeneIDs whose expression was strongly and consistently regulated in the two biological replicates in Table 1. The full list of detected proteins including quantitative and peptide-level data is available in the Supporting Information. Proteins that were found to be regulated according to Table 1 were also classified into Gene Ontology “cellular components” categories (Figure 2c, upregulated in adult mice; Figure 2d, upregulated in young mice). For instance, among proteins found to be upregulated in adult mice, Synapsin1 (Syn1) localizes to synapse and Myelin oligodendrocyte glycoprotein (Mog) localizes to (myelin) membrane, whereas Doublecortin (Dcx), which is annotated to cytoplasm, was found to be upregulated in young mice. Although statistical interpretations of category size must be considered with caution due to the considerably smaller numbers used for the generation of Figure 2c,d, a comparison with the complete set of identified proteins (Figure 2b) indicates that regulated proteins seem to appear in

the novel approach applied in our study allowed the detection of an even wider spectrum of synaptosomal proteins. It is important to keep in mind that synapses are not closed organelles but are open subcellular compartments. Depending on the size and age of synapses, they can contain other organelles. Cellular components frequently associated with synapses are the endoplasmic reticulum, mitochondria, membrane, cytosol, ribosomes, and diverse vesicles.1,2,51 In order to explore the identities of the detected protein content in our sample and to estimate its relevance to synapses, we analyzed the list of detected proteins by mapping Gene Ontology terms for cellular components using DAVID functional analysis tool.45,46 A considerable part of the detected proteome was assigned to the organelles and cellular structures frequently, but not exclusively, found in synapses (Figure 2b), suggesting a high coverage of the synaptosomal proteome in our cortical synaptosomal preparation. We further show a list of 4318



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the same categories (synapses and their closely associated structures), suggesting again that the respective GO terms may be regarded as cellular components associated with synapses from a functional point of view. Western Blotting Confirms Proteomics Results

Large-scale proteomic mass spectrometry analysis is an invaluable tool to explore complex samples containing large sets of proteins. Nevertheless, the application of independent methods is advisible to confirm the proteomics findings.52 Here, we tested the developmental regulation of a subset of proteins from our MS data set with an independent approach, western blotting. Eight proteins were manually selected on the basis of considerations such as regulatory ratio, potentially interesting biological function in brain development based on previous reports, previous identification as a synapse-associated protein, and the availability of suitable antibodies. We prepared synaptosomal proteins in an analogous way, similar to that for mass spectrometry analysis. Again, cortical synaptosomal protein fractions from 3 wo and 8 wo mice were isolated independently, and after protein quantification, equal amounts of protein isolated from 4 individuals of the same age group were pooled and analyzed by western blot. The entire data set of all western blots that we analyzed is depicted in Figure 4. Indeed, our results confirmed the developmental regulation of both postsynaptic (Discs, large homologue 4, PSD-95; Synaptic Ras GTPase activating protein 1, SynGAP; Fyn protooncogene, Fyn; V-erb-a erythroblastic leukemia viral oncogene homologue 4, ErbB4) and presynaptic (Synapsin I, Syn1) proteins found in the proteomics screen (Figure 4a). Moreover, we found that regulation of neuronal protein (Doublecortin, Dcx) and even glial proteins (Myelin oligodendrocyte glycoprotein, MOG; Claudin 11, Cldn11) could be confirmed by western blotting. Actin was used as a loading control, and it showed very little fluctuation between samples. All proteins analyzed by western blot were up- and downregulated in the same direction as that in the MS analysis. Moreover, the developmental regulation of protein levels detected independently by both methods was highly correlated (R = 0.85; Figure 4b). In summary, western blot analysis independently confirmed the high fidelity of our MS data set.

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Figure 4. Western blotting confirmed proteomics results. (a) Several synaptic (Fyn, ErbB4, PSD-95, Syn1, and SynGAP) as well as neuronal (Dcx) and glial (Cldn11 and MOG) proteins with regulation of protein level detected by mass spectrometry (gray bars) were also tested for developmental regulation using western blotting. The samples for western blots (white bars) were prepared from different subjects in the same way as that for MS. Western blots of adult, a, and young, y, samples are depicted below the bar plot. (b) Protein level regulation detected by both techniques is correlated (R = 0.85). Bar and scatter plot points are the geometric mean across repetitions, error bars are the geometric standard deviation, and the broken line is the linear regression.

DISCUSSION

Our study provides a comprehensive analysis of the synaptosomal proteome obtained from the cortices of 3 and 8 week old mice, with high-resolution peptide separation in both the first and second dimension providing the experimental basis for deep proteome coverage. The comparison of biological replicates demonstrates a high precision of the measurements, and the independent analysis of the developmental regulation of a subset of proteins using western blot indicates a high level of fidelity in our data set. Furthermore, the large number of identified proteins in a single experiment makes our data set, to the best of our knowledge, the most comprehensive proteomic analysis of synaptosomes at present, representing significant methodological progress. Because the number of detected proteins in our data set is comparatively high, indicating a high sensitivity of our approach, the data set possibly comprises novel synaptosomal proteins even if these proteins have not been associated with synapses previously. However, it should be kept in mind that synaptosomal preparation leads to an enrichment of synaptic structures but may also include proteins derived from other,

possibly associated, structures. For instance, Myelin oligodendrocyte glycoprotein (MOG) and Claudin 11 (Cldn11), for which consistent regulation was observed by both mass spectrometry and western blotting, are glial, i.e., non-neuronal, proteins. Nevertheless, the noticeable observation that these proteins are frequently detected in synaptosomal fractions30,50 suggests that these proteins might be derived from structures closely associated with synapses. Therefore, the synaptosomal preparation should be considered as a preparation enriched not only in connections between neurons but also in synapseassociated structures that can be of different origin. 4319



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advantages or disadvantages.57−63 All of the above-mentioned disparities are likely sources for the variability observed across the data sets. Despite these differences, we detected a moderate positive correlation between the described protein sets, suggesting that detection of protein expression during development is consistent between different studies. Besides being consistent with other published data sets, our mass spectrometry data was also confirmed by western blotting. From the list of genes found to be developmentally regulated upon mass spectrometry analysis, we selected 8 genes that were previously described as potentially playing a role in brain development, and we evaluated the expression levels of these genes by western blot analysis using biological replicate samples. Figure 4 illustrates that regulatory ratios obtained by mass spectrometry as compared to quantitative western blot analysis were in good concordance for all proteins evaluated. The correlation observed between the two different methods corroborates the quality of the quantitative mass spectrometry data. Of note, for most proteins, changes in protein levels detected by western blot were larger than those revealed by mass spectrometry (with one exception of Dcx), an observation that was described previously.64 Although western blots are certainly useful for confirmatory studies involving a few proteins, the method seems inappropriate for a discovery study aiming at the quantification of several thousands of proteins. PSAQ requires the generation of an isotope-labeled internal standard protein for each protein that should be quantified, which makes the technique costly, laborious, and therefore less appealing for the quantification of a large number of proteins. Although SILAM using in vivo 15N labeling is suitable for discovery studies, the method is also costly and technically challenging and is not free of potential issues. Artificial conditions during isotope labeling may lead to biological effects,62 although this becomes less of an issue when SILAM-labeled tissue is used merely as an internal standard for pairwise comparisons. Moreover, an isotope effect on chromatographic retention times and peak widths was described for 15N metabolic labeling recently.61 For comparison, the low cost and simplicity of chemical labeling, the completeness of quantitative data (all 4 iTRAQ reporter ions are usually observed in HCD spectra measured on Orbitrap instruments), and the ability to compare multiple biological replicates without an increase in sample complexity were important considerations in our decision to use iTRAQ as a methodology for the present study. In comparison to previously published data sets in older mice, our study provides quantitative information on early developmental maturation from adolescence (3 wo) to early adult (8 wo) mice. This period is characterized by considerable developmental changes. Genes differentially regulated during this period might play a role in defects of developmental processes. Importantly, many of the detected proteins annotated to synapses by GO terms were not only previously reported to be regulated with age but also to play important roles in brain development and disease. In the set of most strongly regulated proteins with GO annotation to synapse, there are striking examples such as Slc17a7 (also known as vGlut1), Syn1, Syn2, or Camk2. Expression of Slc17a7 has been previously shown to be strongly developmentally upregulated65 and is believed to play an important role in neural circuit maturation.66,67 Deficits of Slc17a7 expression in animals at postweaning age lead to severe neurological and behavioral phenotypes.66,68 Interestingly, altered expression of this protein

Recently, two studies were published investigating changes in the synaptic proteome during later stages of animal development. Synaptosomal proteins obtained from different brain areas (including cortex) of a rat brain were quantitatively compared to a standard sample in multiple pairwise experiments using stable isotope labeling in mammals (SILAM) technique. The summative detected proteome was rich (2590 detected proteins); however, this technique allowed a comparison of only two protein samples in parallel (a standard control and an actual analytical sample). Consequently, as multiple pairwise comparisons were obtained in this study, expression levels of only a subfraction of proteins that were independently detected in different experiments could be quantitatively compared to each other.32 Another interesting study used iTRAQ labeling to obtain information on protein level changes (673 proteins) during development in synaptic membrane fraction isolates from mouse synaptosomes.34 We mapped all data to NCBI GeneIDs and compared our data set (protein expression of 3422 GeneIDs) with these previous studies. In the overlapping fraction of detected proteins we observed a moderate positive correlation in the developmental regulation of protein expression between our data and the data from both previous studies (Figure 5). The correlation

Figure 5. Detected protein expression level ratios are positively correlated with previous findings. Scatter plots describing synaptosomal protein ratios of adult (8 wo) to young (3 wo) mice (data from this study) were compared to (a) synaptic membrane protein ratios of young adult (46 do) to young (30 do) mice34 and (b) synaptosomal protein ratios of young adult (45 do) to young (20 do) rats.32 Number of proteins shown on scatter plots is 543 (a) and 193 (b). Broken lines mark linear regression. Correlation coefficient for (a) is R = 0.40 and for (b) is R = 0.52. Data are shown on a logarithmic scale.

coefficient between our results and the data set from the mouse membrane fraction proteome is R = 0.40, and the correlation coefficient between our results and the data set from the rat synaptosomal proteome is R = 0.52. It should be kept in mind that the data sets were obtained with three independent approaches using different animal species (mouse or rat) and, moreover, that different ages of the animals (8wo/3wo, 46do/ 30do, or 45do/20do) were compared, different cortical fractions were analyzed (synaptosomes or synaptic membranes), and the techniques of labeling and analysis (iTRAQ or SILAM) were different. Ratio distortion by reporter ions derived from co-eluting peptides within the precursor isolation window was reported to occur in iTRAQ studies53−56 so that lower regulatory ratios might be expected for iTRAQ experiments as compared to experiments using other techniques such as PSAQ, SILAM and 15N labeling, or western blots, with all approaches known to be associated with certain 4320



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*(K.M.) E-mail: [email protected]. Tel.: +43-1-797304280. Fax: +43-1-79044-110.

in anterior cingulated cortex was found in schizophrenia patients. In addition, synapsins 1 and 2 (Syn1 and Syn2), which play a role in neuronal morphology, and in the formation and maturation of synapses, have also been associated with diverse disorders such as schizophrenia and autism.69,70 Moreover, Camk2, a well-studied developmentally regulated71 kinase, is crucial for synaptic placticity72 and plays an important role in learning and memory formation.73−75 Among proteins for which regulation was confirmed by western blot analysis, mutations in Doublecortin (Dcx) are associated with severe developmental aberrations that can lead to epilepsy and altered brain morphology (lissencephaly, subcortical band heterotopia).76,77 We also confirmed regulation of synaptic proteins that are involved in synapse maturation: PSD-95,78 ErbB4,79 Fyn,80 and SynGAP.81 The importance of these proteins is further underscored by their role in the pathology of schizophrenia,82,83 Alzheimer’s disease,84 and intellectual disability and autism spectrum disorders.81 Interestingly, developmentally regulated myelin proteins were also detected in the sample: Cldn1185 and MOG are involved in central nervous system function and play a role in the pathogenesis of demyelinating diseases such as multiple sclerosis.86,87 The presence of known candidates in the set of detected proteins corroborates the relevance of the applied approach and promises to be a fruitful resource for the detailed analysis of new candidates to date not yet implicated to play a role in synaptic maturation and associated diseases.

Author Contributions ∥

⊥

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank colleagues from the Rumpel and Mechtler laboratories for valuable discussions and critical reading of the manuscript. Otto Hudecz helped with data analysis and upload to ProteomeXchange. The research leading to these results has received funding from the Austrian Science Fund FWF (SFB F3402-B03, P2465-B24, TRP 308-N15) and the European Commission via the Seventh Framework Programme (FP7/ 2007-2013), projects MEIOsys (222883-2) and PRIME-XS (262067).

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ABBREVIATIONS

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REFERENCES

ACN, acetonitrile; AGC, automatic gain control; BCA, bicinchoninic acid; CID, collision-induced dissocation; DTT, dithiothreitol; EDTA, ethylenediaminetetraacetic acid; EGTA, ethylene glycol tetraacetic acid; FA, formic acid; FDR, false discovery rate; GO, gene ontology; HCD, higher-energy collisional dissociation; HEPES, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; HPLC, high-performance liquid chromatography; ID, identifier or inner diameter; iTRAQ, isobaric tags for relative and absolute quantification; LC, liquid chromatography; LDS, lithium dodecyl sulfate; MMTS, methylmethanethiosulfonate; MS, mass spectrometry; MGI, mouse genome informatics; NCBI, National Center for Biotechnology Information; o/n, overnight; PBS, phosphate buffered saline; PSD, postsynaptic density; PSM, peptidespectrum match; PVDF, poly(vinylidene fluoride); RSLC, rapid separation liquid chromatography; SCX, strong cation exchange; TEAB, triethylammonium bicarbonate; TCEP, tris(2carboxyethyl)phosphine; TFA, trifluoroacetic acid; wo, weeks old

CONCLUSIONS In this work, the expression levels of 3422 GeneIDs were studied upon regulation during early murine development, creating a rich and novel data set. This data set may serve as a starting point for further studies analyzing maturation processes in the neocortex. Furthermore, we propose that this method can be used to obtain a detailed analysis of changes in the synaptic proteome associated with physiological processes such as behavioral learning or pathological processes related to neurological diseases. The entire data set, comprising instrument raw files, search input tandem mass spectra, the Proteome Discoverer search and quantification result file, an archive of all identified and annotated spectra, and identification and quantification results (including iTRAQ reporter ion data) on peptide and protein levels exported as an xlsx file, is provided as a resource to the community via the ProteomeXchange repository.88

(1) Okabe, S. Molecular anatomy of the postsynaptic density. Mol. Cell Neurosci. 2007, 34, 503−18. (2) Harris, K. M.; Weinberg, R. J. Ultrastructure of synapses in the mammalian brain. Cold Spring Harbor Perspect. Biol. 2012, 4, a005587. (3) Markram, H.; Lubke, J.; Frotscher, M.; Roth, A.; Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. 1997, 500, 409−40. (4) Varga, Z.; Jia, H.; Sakmann, B.; Konnerth, A. Dendritic coding of multiple sensory inputs in single cortical neurons in vivo. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 15420−5. (5) Holtmaat, A.; Svoboda, K. Experience-dependent structural synaptic plasticity in the mammalian brain. Nat. Rev. Neurosci. 2009, 10, 647−58. (6) Fu, M.; Zuo, Y. Experience-dependent structural plasticity in the cortex. Trends Neurosci. 2011, 34, 177−87. (7) Caroni, P.; Donato, F.; Muller, D. Structural plasticity upon learning: regulation and functions. Nat. Rev. Neurosci. 2012, 13, 478− 90.

ASSOCIATED CONTENT

S Supporting Information *

Protein accessions mapped to NCBI GeneIDs and all identification and quantification data on the protein and peptide levels. This material is available free of charge via the Internet at http://pubs.acs.org. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository88 with the data set identifier PXD000552.

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S.R. and K.M. are equally contributing corresponding authors.

Notes

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K.E.M. and P.P. are equally contributing first authors.

Author Contributions

AUTHOR INFORMATION

Corresponding Authors

*(S.R.) E-mail: [email protected]. Tel.: +43-1-797303470. Fax: +43-1-79871-53. 4321



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