Name: high resolution quantitative synaptic proteome profiling of mouse brain regions after auditory discrimination learning
Uploaded: 2016-12-15T15:00:00
Duration: 10 min 36 s
Description: Watch this Scientific Journal Video about High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning at JoVE.com

We wish to thank Yvonne Ducho and Kathrin Pohlmann for excellent technical assistance. This work was supported by the Deutsche Forschungsgemeinschaft (SFB 779) and by the State Saxony-Anhalt / European Regional Development Fund (ERDF) via the Center for Behavioral Brain Sciences (CBBS).

<ol>
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The authors have nothing to disclose.

The study presents a methodological workflow optimized for an accurate quantitative profiling of synaptic protein expression changes during learning and memory consolidation in different brain areas of mice. The setup provides the opportunity to study the protein expression on the level of a single animal despite of the required application of at least three technical replicates per sample for mass spectrometric analysis.
The methodology takes into account the particular protein composition of the pre- and postsynapse consisting of high molecular weight scaffold proteins but also of important mediator proteins bearing medium or lower molecular weights. The in-solution digests of synaptosomal preparations result in an efficient generation and, hence, an over-representation of scaffold-derived peptides. This, in turn, may suppress the analysis of smaller or lower abundant proteins. The suggested preparation of SDS-PAGE fractions from an aliquot of each sample combined with an in-gel digestion procedure in parallel facilitates the analysis of medium and low abundance proteins and represents a highly recommended complementary method. After separate mass spectrometric application of all fractions derived from a sample (e.g. in-solution digest, in-gel digest, combined phospho-enriched fractions) the corresponding MS/MS data sets can be combined and further calculated for protein identification and quantification by PEAKS software or alternative popular software packages.
Alternatively, the individual application of in-gel-digestion-derived fractions of a sample (separately processed gel-areas of a sample lane) and fractions generated of the in-solution digested sample (e.g. by ion exchange chromatography) to mass spectrometry can increase the analytical depth. However, this extended workflow dramatically increases the required time for LS-MS/MS data acquisition. For generation of a detailed molecular sequence of synaptic protein rearrangements during learning and memory formation a specified time course of the proteomic profiling is required. This time course may start immediately after or even during the first training session and covers a close-meshed time frame until the animals&#39; performance reached the asymptotic level of the learning curve after approx. 8 - 10 days of training (see Figure 2 for details).
The analysis of phosphorylation changes of synaptic proteins requires a particular focus on the selected time frames during FMTD learning. On the one hand signaling cascades initiating synaptic protein rearrangements known to be triggered by protein phosphorylations and dephosphorylations are expected at very early stages of animal training. On the other hand, there are long lasting modifications of multiple phosphorylated synaptic proteins known which regulate the connectivity and assembly within the synaptic architecture19, 20. Those posttranslational modifications are expected even at later time points of memory consolidation.
The complex datasets generated by this proteomic workflow require bioinformatic processing to identify participating molecular pathways and key molecules. Meta-analysis shows significant overrepresented pathways, which play a role in learning and memory processes.

Learning is based on the formation of memory traces and their maintenance. It is widely accepted that one underlying mechanism may represent an activity-dependent formation of new and/or rearrangement of existing synaptic contacts between neurons. On the molecular level, various protein modifications, subcellular relocalizations and changes in the turnover of synaptic proteins have been described1-4(Lamprecht, 2004 #8). However, most studies so far focused on selected proteins rather than on the global but complex synaptic proteome composition. The present approach allows an unbiased screening for synaptic proteome changes in mouse brain regions after a learning experiment. It is suitable to render time-point dependent molecular snapshots of the learning-induced reorganization of the synaptic architecture. The described workflow requires a particular teamwork of different specialists in animal behavior, protein biochemistry, mass spectrometry and bioinformatics.
The chosen learning paradigm, i.e. frequency-modulated tone discrimination (FMTD), is a well-characterized auditory discrimination task in rodents5. Learning and long-term memory formation in this shuttle box Go/No-Go-task involves mechanisms depending on increased cortical dopamine signaling and protein synthesis. Accordingly, recent proteomic studies on gerbils and mice revealed dopamine- and learning-induced plastic rearrangements of synaptic components in cortical, but also in more basal brain regions that supposedly interact during FMTD learning and memory6-8. This illustrates that memory formation involves a complex interplay of various brain regions and thus, might be differentially regulated within these regions on the proteome level. Therefore, dissection of selected cortical and subcortical mouse brain regions is included in the workflow.
Furthermore, the reliable characterization even of weak changes in synaptic protein composition requires an enrichment of pre- and postsynaptic compartments rather than the analysis of homogenates or crude membrane fractions9. Therefore, the preparation of synaptosomes utilizing established protocols prior to proteomic analysis is described in order to increase the detection level and the dynamic range for synapse-specific proteins10,11.
An essential prerequisite to use label-free high-resolution mass spectrometry for quantitative purposes is a high degree of similarity of protein samples. As rather minor changes in synaptic protein composition are expected to occur after learning, a label-free approach will be appropriate to compare corresponding protein samples obtained from trained and na&#239;ve mice. Alternatively, condition-specific label strategies of proteins/peptides using stable isotopes (e.g. TMT, iTRAQ , ICPL and SILAC) as well as MS2-based label free quantification (SWATH) are useful, but they are more expensive than the chosen label-free approach or need special mass spectrometric hardware.
Since proteomic screenings often yield complex data sets, bioinformatic processing is recommended for appropriate data interpretation. Additional meta-analyses may support a better understanding of potential molecular mechanisms underlying paradigm-related changes and the identification of involved key cellular processes and signaling pathways. Appropriate methodologies are also described below.

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			<td>Quantity One 4.5.1</td>
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			<td>Tetramethyl ethylene -1,2-diamine (TEMED)</td>
			<td>Sigma-Aldrich</td>
			<td>T9281</td>
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			<td>Thermomixer basic</td>
			<td>CallMedia</td>
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		</tr>
		<tr>
			<td><a name="RANGE!A60">Titansphere TiO 5&#181;m</a></td>
			<td>GL Sciences Inc. Japan</td>
			<td>502075000</td>
			<td></td>
		</tr>
		<tr>
			<td>TLA 100.1 rotor</td>
			<td>Beckman Coulter</td>
			<td>343840</td>
			<td></td>
		</tr>
		<tr>
			<td>Trifluoro acetic acid&#160; (TFA)</td>
			<td>Sigma-Aldrich</td>
			<td>T6508</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris ( hydroxymethyl) aminomethane (TRIS)</td>
			<td>AppliChem</td>
			<td>A1086</td>
			<td></td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma-Aldrich</td>
			<td>T8532</td>
			<td></td>
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			<td>Trypsin Gold</td>
			<td>Promega</td>
			<td>V5280</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultimate 3000 Ultra HPLC&#160;</td>
			<td>Dionex/Thermo Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Ultracentrifuge tube</td>
			<td>Beckman Coulter</td>
			<td>343776</td>
			<td></td>
		</tr>
		<tr>
			<td>Unijet II Refrigerated Aspirator</td>
			<td>Uniequip Laborger&#228;te- und Vertriebs GmbH</td>
			<td></td>
			<td></td>
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			<td>UNIVAPO 100 H Concentrator Centrifuge&#160;</td>
			<td>Uniequip Laborger&#228;te- und Vertriebs GmbH</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>AppliChem</td>
			<td>A1049</td>
			<td></td>
		</tr>
		<tr>
			<td>Water (high quality purifed)</td>
			<td></td>
			<td></td>
			<td>Resistivity: &#62; 18.2 M&#937;*cm at 25 &#176;C&#160; Pyrogens: &#60; 0.02 EU/ml&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; TOC: &#60; 10 ppb</td>
		</tr>
		<tr>
			<td>Xcalibur 3.0.63</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ZipTipC18 Pipette Tips</td>
			<td>MILLIPORE</td>
			<td>ZTC18S960</td>
			<td></td>
		</tr>
	</tbody>
</table>

high resolution quantitative synaptic proteome profiling of mouse brain regions after auditory discrimination learning

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory discrimination learning in mice is described. In this learning paradigm, mice are trained in a shuttle box Go/NoGo-task to discriminate between rising and falling frequency-modulated tones in order to avoid a mild electric foot-shock. The protocol involves the enrichment of synaptosomes from four brain areas, namely the auditory cortex, frontal cortex, hippocampus, and striatum, at different stages of training. Synaptic protein expression patterns obtained from trained mice are compared to na&#239;ve controls using a proteomic approach. To achieve sufficient analytical depth, samples are fractionated in three different ways prior to mass spectrometry, namely 1D SDS-PAGE/in-gel digestion, in-solution digestion and phospho-peptide enrichment. 
High-resolution proteomic analysis on a mass spectrometer and label-free quantification are used to examine synaptic protein profiles in phospho-peptide-depleted and phospho-peptide-enriched fractions of synaptosomal protein samples. A commercial software package is utilized to reveal proteins and phospho-peptides with significantly regulated relative synaptic abundance levels (trained/na&#239;ve controls). Common and differential regulation modes for the synaptic proteome in the investigated brain regions of mice after training were observed. Subsequently, meta-analyses utilizing several databases are employed to identify underlying cellular functions and biological pathways.

Figure 1 summarizes the complete workflow of quantitative synaptic proteome profiling of mouse brain regions after auditory discrimination learning. It starts with the animal training in a shuttle box. In the example shown in Figure 2, mice started to show significant FM tone discrimination in the 4th training session, indicating efficient learning. Animals are sacrificed at selected time points for brain area dissection. The required enrichment of synapses can either be achieved by the preparation of synaptosomes or alternatively by the preparation of a PSD-enriched fraction, both described in detail in Figure 3. The PSD-enrichment method has been developed for low tissue amounts, e.g. 1 - 2 hippocampal slices from rat brain12, 18. It requires small tubes, PTFE pestles fitting to these tubes, and a laboratory drilling drive for powering the pestle.
Due to the particular protein composition of synaptosomes, it is strongly recommend to perform the sample preparation in two different but complementary ways. Scaffolds of the PSDs are often very high molecular weight proteins occurring in high stoichiometry. In-solution digest is the best way to extract them efficiently but may lead to an oversampling of the generated peptide mixture. The in-gel digest performed of the same sample in parallel can exclude those high molecular weight proteins and favor the analysis of proteins with medium and lower molecular weight. For a comprehensive analysis both types of proteolytic digests are recommended.
The different amounts of tissues of the brain areas investigated require an adjustment of the applied material for better comparison. Within the four investigated brain areas the auditory cortex is generally the limiting factor. The material of all other brain areas should carefully be adjusted to the amount of the auditory cortex after preparation of synaptosomes or PSD-enriched fractions (see 3.1.1.). Typical weights of freshly prepared brain areas from mice are as following: auditory cortex (AC): ~ 50 mg; hippocampus (HIP): ~ 90 mg; striatum (STR): ~ 120 mg and frontal cortex (FC): ~ 100 mg.
The PSD-enrichment method described in section 2.3 allowed the identification of approximately 1500 different proteins and approximately 250 different phospho-peptides per brain region on the level of a single animal (Table 1). Proteomic analysis 24 h after the first training session revealed that 7.3% of the identified proteins and 5.8% of the phospho-peptides showed significant (p&#60; 0.05) quantitative changes in their synaptic expression compared to na&#239;ve controls (Table 1). A conspicuous tendency for down regulation of synaptic scaffolds may point to a pronounced rearrangement of the synaptic architecture during early stages of FMTD learning. The vast majority of the regulated proteins were altered in a brain region-specific manner, whereas only 22% were found to be regulated in two or more brain areas. Six selected examples are shown in Figure 4.
Meta-analysis of the complex results by IPA provides evidence for the particular participation/manipulation of the following canonical pathways: &#34;Clathrin-mediated Endocytosis Signaling&#34;, &#34;Axonal Guidance Signaling&#34;, &#34;Calcium Signaling&#34;, &#34;RhoA Signaling&#34;, &#34;Notch Signaling&#34;, &#34;Remodeling of Epithelial Adherens Junctions&#34;, &#34;Glutamate Receptor Signaling&#34;, &#34;GABA Receptor Signaling&#34;, &#34;Dopamine Receptor Signaling&#34; and &#34;Synaptic Long-Term Potentiation&#34;.
Single enrichment analysis revealed significant overrepresented biological processes in the frontal cortex concerning protein transport, cell adhesion, phosphorylation, endocytosis, vesicle-mediated transport, forebrain development and axonogenesis (Figure 5). In the auditory cortex biological processes including ion transport, translation, mRNA transport, protein transport and learning were noticeable. The analysis of the protein fraction of the hippocampus detects significantly enriched processes related to ion transport, cell cycle, translation, phosphorylation and nervous system development. In the striatum, overrepresented biological processes including mRNA transport, vesicle-mediated transport, axonogenesis, proteolysis, protein transport and endocytosis were found.
<img alt="Figure 1" src="/files/ftp_upload/54992/54992fig1.jpg" /> 
Figure 1: Systematic Workflow of the Methodological Approach. This figure schematically summarizes the workflow of high resolution quantitative profiling of brain area specific synaptic protein composition. <a href="http://cloudflare.jove.com/files/ftp_upload/54992/54992fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54992/54992fig2.jpg" /> 
Figure 2: Example of the Performance of Mice in the FM Tone Discrimination Task. Animals show an increasing rate of hits (blue curve) and a decreasing rate of false alarms (black curve) in the course of training sessions. Significant discrimination occurs from the fourth session. Error bars are provided as SEM. <a href="http://cloudflare.jove.com/files/ftp_upload/54992/54992fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54992/54992fig3.jpg" /> 
Figure 3: Preparation of the Synaptosome and the PSD-enriched Fraction. A: Synaptosome preparation. B: PSD-enriched fraction preparation. Both figures explain the detailed workflow of preparation of synaptosomes or alternatively PSD-enriched fractions from brain tissues. <a href="http://cloudflare.jove.com/files/ftp_upload/54992/54992fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54992/54992fig4.jpg" /> 
Figure 4: Selected Quantitative Proteomic Results. The relative synaptic abundances of selected proteins are compared between mice trained on the FMTD task (AV, n= 6) and na&#239;ve control mice (NV, n= 6) 24 hr after the first training session. The abundance values were calculated as median of the peak areas of the three most intense peptides of a protein. Proteins with significant abundance changes (AV/NV; t-test) are marked within the plots: * p&#60; 0.05, ** p&#60; 0.01, *** p&#60; 0.005. Error bars are provided as SD. <a href="http://cloudflare.jove.com/files/ftp_upload/54992/54992fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54992/54992fig5.jpg" /> 
Figure 5: Visualization of Biological Pathways for Frontal Cortex by GeneCodis/Gephi. Only significant terms of the Gene Ontology (GO) database (http://geneontology.org) related to &#8220;Biological process&#8221; with a minimum protein number of three are shown here. Nodes represent GO terms, the size of the node, the line width and number of connections of a certain node depict the number of proteins, which share this GO term with other nodes. Due to the &#8220;Force Atlas&#8221; method of Gephi, related nodes are clustering closely together.&#160;<a href="http://cloudflare.jove.com/files/ftp_upload/54992/54992fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Brain region</td>
			<td>AC</td>
			<td>FC</td>
			<td>HIP</td>
			<td>STR</td>
			<td>&#8721;</td>
		</tr>
		<tr>
			<td>identified&#160; proteins</td>
			<td>1435</td>
			<td>1758</td>
			<td>1572</td>
			<td>1507</td>
			<td>6272</td>
		</tr>
		<tr>
			<td>regulated proteins (p&#60;0.05)</td>
			<td>59</td>
			<td>130</td>
			<td>162</td>
			<td>108</td>
			<td>459</td>
		</tr>
		<tr>
			<td>&#8593; AV/NV</td>
			<td>8</td>
			<td>4</td>
			<td>76</td>
			<td>35</td>
			<td>123</td>
		</tr>
		<tr>
			<td>&#8595; AV/NV</td>
			<td>51</td>
			<td>126</td>
			<td>86</td>
			<td>73</td>
			<td>336</td>
		</tr>
		<tr>
			<td>identified phosphomotifs</td>
			<td>197</td>
			<td>361</td>
			<td>273</td>
			<td>278</td>
			<td>1109</td>
		</tr>
		<tr>
			<td>regulated phosphomotifs (p&#60;0.05)</td>
			<td>8</td>
			<td>22</td>
			<td>21</td>
			<td>14</td>
			<td>65</td>
		</tr>
		<tr>
			<td>&#8593; AV/NV</td>
			<td>4</td>
			<td>17</td>
			<td>5</td>
			<td>9</td>
			<td>35</td>
		</tr>
		<tr>
			<td>&#8595; AV/NV</td>
			<td>4</td>
			<td>5</td>
			<td>16</td>
			<td>5</td>
			<td>30</td>
		</tr>
	</tbody>
</table>
Table 1: Summary of a Proteomic Result. This table summarizes a representative proteomic experiment of trained mice (AV, n= 6) 24 hr after the first training session compared to their na&#239;ve controls (NV, n= 6). The sum of 459 regulated proteins includes overlapping regulations. 283 different regulations were determined as brain specific. In detail, 57 proteins are regulated in two brain regions, 18 protein regulations were detected in three brain regions and only 2 proteins are regulated in all four investigated brain areas.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Error tolerances</td>
		</tr>
		<tr>
			<td>precursor mass (fourier transformation mass spectrometry)</td>
			<td>10 ppm</td>
		</tr>
		<tr>
			<td>fragment ion mass (linear ion trap)</td>
			<td>0.6 Da</td>
		</tr>
		<tr>
			<td>Maximum missed cleavages per peptide</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Fixed modifications</td>
		</tr>
		<tr>
			<td>for in-gel-digested samples</td>
			<td>Carbamidomethylation of Cysteine</td>
		</tr>
		<tr>
			<td>for in-solution-digested samples</td>
			<td>Methylthiolation of Cysteine</td>
		</tr>
		<tr>
			<td>Variable modifications</td>
			<td>Oxidation of Methionine</td>
		</tr>
		<tr>
			<td></td>
			<td>Deamidations of Asparagin and/or Glutamine&#160;</td>
		</tr>
		<tr>
			<td>Database</td>
			<td>Uniprot/Sprot&#160;</td>
		</tr>
		<tr>
			<td>Taxonomy</td>
			<td>mouse</td>
		</tr>
		<tr>
			<td>Statistical identification-acceptance settings</td>
		</tr>
		<tr>
			<td>de novo average local confidence (ALC)</td>
			<td>&#160;&#62; 50%</td>
		</tr>
		<tr>
			<td>Peptide-false discovery rate (FDR, based on est. decoy-fusion)</td>
			<td>&#160;&#60; 1%&#160;</td>
		</tr>
		<tr>
			<td>Protein significance (-10logP, based on modified T-test)</td>
			<td>&#62; 20&#160;</td>
		</tr>
		<tr>
			<td>unique peptides / protein</td>
			<td>&#160;&#8805; 1&#160;</td>
		</tr>
		<tr>
			<td>Quantification settings:</td>
		</tr>
		<tr>
			<td>Peptides used for quantification if:</td>
		</tr>
		<tr>
			<td>Peptide significance (-10logP)</td>
			<td>&#62; 30</td>
		</tr>
		<tr>
			<td>Peptide identification in&#160;</td>
			<td>&#160;&#8805; 50% of samples</td>
		</tr>
		<tr>
			<td>Peptide signal quality</td>
			<td>&#62;1</td>
		</tr>
		<tr>
			<td>Peptide average area</td>
			<td>&#160;&#62; 1E5</td>
		</tr>
		<tr>
			<td>Peptide retention time tolerance</td>
			<td>&#160;&#60; 5 min</td>
		</tr>
		<tr>
			<td>Normalization&#160;</td>
			<td>by total ion current (TIC)</td>
		</tr>
	</tbody>
</table>
Table 2: Settings for Protein Identification (step 4.2.2).

Watch this Scientific Journal Video about High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning at JoVE.com

High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

The identification of molecules and pathways controlling synaptic plasticity and memory is still a major challenge in neuroscience. Here, a workflow is described addressing the relative quantification of synaptic proteins supposedly involved in the molecular reorganization of synapses during learning and memory consolidation in an auditory learning paradigm.

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory ...

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