A comparative analysis of the mobility of 45 proteins in the synaptic bouton
2020; Springer Nature; Volume: 39; Issue: 16 Linguagem: Inglês
10.15252/embj.2020104596
ISSN1460-2075
AutoresSofiia Reshetniak, Jan‐Eike Ußling, Eleonora Perego, Burkhard Rammner, Thomas Schikorski, Eugenio F. Fornasiero, Sven Truckenbrodt, Sarah Köster, Silvio O. Rizzoli,
Tópico(s)Biotin and Related Studies
ResumoResource6 July 2020Open Access Transparent process A comparative analysis of the mobility of 45 proteins in the synaptic bouton Sofiia Reshetniak orcid.org/0000-0003-4847-4144 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany International Max Planck Research School for Molecular Biology, Göttingen, Germany Search for more papers by this author Jan-Eike Ußling Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Eleonora Perego Institute for X-Ray Physics, University of Göttingen, Göttingen, Germany Search for more papers by this author Burkhard Rammner Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Thomas Schikorski Department of Neuroscience, Universidad Central del Caribe, Bayamon, PR, USA Search for more papers by this author Eugenio F Fornasiero orcid.org/0000-0001-7643-4962 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Sven Truckenbrodt Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany International Max Planck Research School for Molecular Biology, Göttingen, Germany Search for more papers by this author Sarah Köster Institute for X-Ray Physics, University of Göttingen, Göttingen, Germany Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany Search for more papers by this author Silvio O Rizzoli Corresponding Author [email protected] orcid.org/0000-0002-1667-7839 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany Search for more papers by this author Sofiia Reshetniak orcid.org/0000-0003-4847-4144 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany International Max Planck Research School for Molecular Biology, Göttingen, Germany Search for more papers by this author Jan-Eike Ußling Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Eleonora Perego Institute for X-Ray Physics, University of Göttingen, Göttingen, Germany Search for more papers by this author Burkhard Rammner Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Thomas Schikorski Department of Neuroscience, Universidad Central del Caribe, Bayamon, PR, USA Search for more papers by this author Eugenio F Fornasiero orcid.org/0000-0001-7643-4962 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Search for more papers by this author Sven Truckenbrodt Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany International Max Planck Research School for Molecular Biology, Göttingen, Germany Search for more papers by this author Sarah Köster Institute for X-Ray Physics, University of Göttingen, Göttingen, Germany Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany Search for more papers by this author Silvio O Rizzoli Corresponding Author [email protected] orcid.org/0000-0002-1667-7839 Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany Search for more papers by this author Author Information Sofiia Reshetniak1,2, Jan-Eike Ußling1, Eleonora Perego3, Burkhard Rammner1, Thomas Schikorski4, Eugenio F Fornasiero1, Sven Truckenbrodt1,2, Sarah Köster3,5 and Silvio O Rizzoli *,1,5 1Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, Göttingen, Germany 2International Max Planck Research School for Molecular Biology, Göttingen, Germany 3Institute for X-Ray Physics, University of Göttingen, Göttingen, Germany 4Department of Neuroscience, Universidad Central del Caribe, Bayamon, PR, USA 5Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany *Corresponding author. Tel: +49551395911; E-mail: [email protected] EMBO J (2020)39:e104596https://doi.org/10.15252/embj.2020104596 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Many proteins involved in synaptic transmission are well known, and their features, as their abundance or spatial distribution, have been analyzed in systematic studies. This has not been the case, however, for their mobility. To solve this, we analyzed the motion of 45 GFP-tagged synaptic proteins expressed in cultured hippocampal neurons, using fluorescence recovery after photobleaching, particle tracking, and modeling. We compared synaptic vesicle proteins, endo- and exocytosis cofactors, cytoskeleton components, and trafficking proteins. We found that movement was influenced by the protein association with synaptic vesicles, especially for membrane proteins. Surprisingly, protein mobility also correlated significantly with parameters as the protein lifetimes, or the nucleotide composition of their mRNAs. We then analyzed protein movement thoroughly, taking into account the spatial characteristics of the system. This resulted in a first visualization of overall protein motion in the synapse, which should enable future modeling studies of synaptic physiology. Synopsis Live imaging reveals global protein dynamics in the synaptic bouton, providing a first visualization of overall protein motion in the synapse and showing connections between various protein characteristics and mobility in vivo. Membrane proteins display lower mobility rates than soluble proteins. Proteins are less mobile in synaptic boutons than in axons. Protein mobility is strongly influenced by the protein association with synaptic vesicles. Amino acid composition, mRNA nucleotide composition, and protein lifetimes correlate with mobility parameters. Provided diffusion coefficients for 45 synaptic proteins can be used to generate models of synaptic physiology. Introduction Synaptic transmission is one of the best-known cellular pathways, with most of its components being thoroughly annotated in functional terms (Koopmans et al, 2019). Within the synapse, the synaptic vesicle recycling pathway has been analyzed in very high detail, for several decades. This pathway involves the fusion of synaptic vesicles at the active zone (exocytosis), which is followed by the retrieval of the fused vesicle molecules (endocytosis), and by the reformation of new fusion-competent vesicles (Sudhof, 2004; Haucke et al, 2011; Rizzoli, 2014). The copy numbers of the molecules involved in synaptic vesicle recycling are known relatively well (Takamori et al, 2006; Wilhelm et al, 2014). Many other features of these proteins have also been analyzed in systematic studies, ranging from their overall spatial distributions (Wilhelm et al, 2014) to their translation in relation to synaptic function (Schanzenbächer et al, 2016) or to their lifetimes, both in vitro (Dörrbaum et al, 2018) and in vivo (Fornasiero et al, 2018). Such systematic studies have revealed numerous unexpected features, including strong correlations between protein functions and their lifetimes (Dörrbaum et al, 2018), or links between the protein and mRNA structures and a many functional parameters such as the translation rates (Mandad et al, 2018). However, one important characteristic of synaptic proteins, their mobility, has not been the subject of large systematic studies. The movement of synaptic organelles, and especially of synaptic vesicles, has been thoroughly investigated (Rothman et al, 2016). Active transport of molecules to and from synapses has also been measured in numerous studies (Hirokawa et al, 2010; Roy, 2014). The movement of individual proteins in synapses has been less investigated, in studies that typically only targeted one or a handful of presynaptic molecules (e.g., Kamin et al, 2010; Ribrault et al, 2011; Albrecht et al, 2016). Such studies resulted in valuable insights for the respective proteins, but did not enable further analyses of, for example, protein structure in relation to synaptic mobility. Many important questions could only be approached by systematic works targeting multiple proteins simultaneously. For example, is the synaptic protein mobility determined by their size, or is their movement dominated by specific interactions with other synaptic components, rendering size effects irrelevant? As another example, several biochemical and imaging experiments have demonstrated thoroughly that the vesicle cluster binds to substantial amounts of cofactor proteins (Shupliakov, 2009; Denker et al, 2011a; Fornasiero et al, 2012; Milovanovic & Camilli, 2017). How does this relate to the protein movement? Is this effect relevant for both soluble and membrane proteins? At the same time, many functional protein parameters are known to depend on the respective protein and mRNA sequences, as mentioned above (Mandad et al, 2018). Could one determine such correlations also for protein movement parameters? Such questions are difficult to explore in the absence of a large protein movement dataset. To address this challenge, we aimed to measure the mobility of multiple proteins in the synaptic bouton and in the axon. We obtained measurements for 47 proteins, including controls such as free cytosolic GFP, or membrane-bound GFP. We relied on the overexpression of GFP-tagged variants of the proteins, which is the only efficient solution when large numbers of constructs need to be analyzed. To minimize, as much as possible, the deleterious effects of GFP fusion and overexpression, we only used GFP chimeras that had been validated in the past, and we made efforts to only investigate neurons with mild expression levels. We found that the average overexpression levels ranged from ~1.2-fold to 2-fold, compared to the normal expression levels, for multiple tested proteins (albeit overexpression could reach higher values in individual neurons and synapses). We also controlled for possible connections between overexpression levels and protein mobility behaviors, and found no substantial correlations for any of the analyzed proteins. Finally, the motion measurements we obtained could reproduce well several similar measurements of (i) fluorophore-tagged native proteins and vesicles; (ii) GFP-tagged proteins expressed in mice after knock-in procedures. Overall, this suggests that our measurements reproduce well the behavior of the native proteins. Having thus obtained a large dataset of comparative movement measurements for synaptic proteins, we proceeded to solve the questions posed above. Our results demonstrated that, for example, protein size has a very limited effect on synaptic mobility and that protein movement parameters correlate to many other cell biology parameters. We then analyzed the movement data by a modeling approach, based on the structural features of the synapses. This resulted in movement rate estimates (diffusion coefficients) for the different proteins in the axon, in the synapse, and in the vesicle cluster. These movement rates (and/or similar movement rates obtained by more complex models, which can be readily performed using our data) will be employed in the future in investigating the molecular kinetics of synaptic function (e.g., exo- or endocytosis) with higher precision than currently possible. Results An overview of the proteins analyzed The mobility of membrane proteins has been analyzed by quantum dot tracking in the past (e.g., Ribrault et al, 2011; Albrecht et al, 2016). As this is not a feasible labeling option for cytosolic proteins, and as its use for tracking membrane proteins in synapses has also been recently criticized (Lee et al, 2017), we decided to pursue this study mainly by fluorescence recovery after photobleaching (FRAP) (Axelrod et al, 1976). Commercial quantum dots have a relatively large size (~20 nm in diameter) and are typically coupled to their targets using antibodies (~10–15 nm in diameter). This renders the labels substantially larger than their targets, which may influence the target movement. Moreover, such labels may be unable to penetrate in areas as the synaptic cleft (Lee et al, 2017). GFP, with a diameter of 2–3 nm, is substantially smaller than even low-size, non-commercial quantum dots (~5–10 nm). Moreover, GFP does not require bridging molecules, as antibodies, for linking to the target protein. Therefore, GFP is expected to affect the protein behavior to a substantially lower extent than the quantum dots. We thus expressed 45 different proteins tagged with monomeric enhanced GFP (mEGFP) in mature hippocampal cultured neurons, focusing on proteins known to participate either in exo- or in endocytosis. We employed proteins whose tagging has been tested and validated in the past in various assays (Fig 1, Table EV1). All of the tagged proteins we employed have been demonstrated to localize in the expected areas, and many have been used to rescue function in cells or animals lacking the wild-type protein (Fig 1, Table EV1). We have also analyzed how proteins were differentially distributed in the synapse and in the axon, both for the tagged proteins (measuring the mEGFP fluorescence in the two compartments) and for the same untagged endogenous proteins (relying on immunostainings; Appendix Figs S1 and S2). The measurements obtained with tagged or untagged proteins correlate well, suggesting that the presence of the mEGFP moiety does not induce major effects on protein localization. Overall, we analyzed proteins involved in exo- and endocytosis, along with bona fide synaptic vesicle proteins, endosomal proteins, cytoskeletal components, and different trafficking proteins located both in the cytosol and in the plasma membrane (Fig 1). Figure 1. Overview of proteins analyzed here and previous validation of the GFP chimeras we used, according to the literatureProtein categories according to their function and/or localization are indicated. We generated validation scores for all of the GFP-fused constructs we employed, as follows: 0) The tagged protein has not been tested before. Does not apply to any of the proteins we used. 1) The correct protein localization upon tagging is verified, but the function was not tested. 2) The correct protein localization upon tagging is verified, but function was difficult to test, due to the presence of the untagged protein. The appropriate function-related changes in the localization of the GFP-tagged proteins took place upon manipulations. 3) The appropriate protein function was verified for the tagged protein, typically in cell cultures (e.g., primary neuronal cultures). 4) The endogenous protein can be replaced by the tagged protein in cells in culture, with appropriate functional replacement. 5) The endogenous protein can be replaced by the tagged protein in living animals, with appropriate functional replacement. Most of the analyzed proteins have a score of 2 and more, meaning the correct localization and function of the tagged proteins have been shown previously. In detail, 4 proteins have a score of 1; 16 proteins have a score of 2; 14 proteins have a score of 3; 6 proteins have a score of 4; 5 proteins have a score of 5. The average score is 2.82. See Table EV1 for more details and for the references used. Download figure Download PowerPoint The basic results: FRAP recovery rates and immobile fractions for the different proteins Tagged proteins typically localized both to synaptic boutons and to the axonal compartment (Fig 2A and B). This enabled us to bleach both synaptic and axonal areas in live neurons, and to monitor the FRAP behavior of the proteins (Fig 2B) for both compartments. Fitting FRAP recovery curves with exponential rise to maximum equations (Fig 2C) provided recovery time constants (τ) and immobile fractions in both axons and synapses (Fig 1D–F). Figure 2. An overview of FRAP experiments A. Typical wide-field image of a neuron expressing the synaptic vesicle-binding protein synapsin coupled to mEGFP. Scale bar, 100 μm. B. Top panels: a cartoon explaining the FRAP procedure. Fluorescent molecules are shown in green. The mEGFP molecules in a defined area are photobleached (gray molecules), and then, the entry of non-bleached molecules from the neighboring areas is measured. Middle and bottom panels: typical results in an axonal segment and in a synaptic bouton of a neuron expressing synapsin coupled to mEGFP. Scale bar, 500 nm. C. An explanation of the FRAP analysis procedure. The FRAP recovery curves could be well fit by single exponential functions, which provide the time constant of recovery, as well as the fraction of the molecules that is not replaced (immobile fraction). D. Exemplary results showing FRAP curves, time constants, and immobile fractions of synapsin in axons and synapses. Symbols indicate means ± SEM. The box plots are organized as follows: The middle line shows the median; the box edges indicate the 25th percentile; the error bars show the 75th percentile; and the symbols show the 90th percentile. Asterisk denotes significant difference. Wilcoxon rank-sum test with using the Benjamini–Hochberg procedure for multiple testing correction, FDR = 0.05. N (axons) = 17, N (synapses) = 24. Also presented in Appendix Fig S3. E. Time constants of all analyzed proteins in axons and in synapses. The two parameters correlate significantly, albeit not very strongly (R = 0.3182, P = 0.04). This correlation is only observed for soluble proteins (R = 0.6134, P = 0.0005), and not for membrane proteins (R = 0.0338, P = 0.9086). F. Immobile fractions in axons and synapses. No correlation was observed (R = 0.0451, P = 0.7769). Symbols indicate means ± SEM; all data are shown as box plots in Appendix Fig S3, numbers of replicates for each protein are shown in Appendix Fig S3, panels E and F are also presented in Appendix Fig S4 with protein names indicated next to symbols. Download figure Download PowerPoint These values are presented in Table EV2 and are also shown in full detail in the large Appendix Fig S3. We used neurons that were allowed to behave normally, and to fire bursts of action potentials freely (at about 0.1 Hz, Truckenbrodt et al, 2018). This implies that the protein motion behavior we observed conforms to conditions of mild activity, which should involve, for example, some level of release of soluble proteins from the vesicle cluster [driven by rises in the Ca2+ concentration and by the phosphorylation of key molecules such as synapsin (Cesca et al, 2010; Rizzoli, 2014; Milovanovic & Camilli, 2017)]. Heavy stimulation or activity inhibition may provide different results, but the results of such experiments would not be physiologically relevant (Denker et al, 2011b). Since high expression levels can affect protein mobility (e.g., via saturation of binding sites on the cofactors of the respective proteins), we only analyzed cells with moderate expression of tagged proteins, as shown in Fig 3A–C). Figure 3. Analysis of protein overexpression A. Typical images of a neuron expressing alpha-SNAP fused to mEGFP (green), which was also immunostained for the same protein (blue), and for the synaptic vesicle marker synaptophysin (red), to detect synaptic boutons. Scale bar, 20 μm. B. The levels of the proteins of interest were measured (relying on the immunostaining) in the transfected boutons, as well as in the non-transfected boutons (detected by the synaptophysin immunostaining). The overexpression levels are shown, obtained by dividing the immunostaining intensity in the overexpressing boutons by that in the non-overexpressing boutons. Only boutons with moderate expression levels have been considered in this work. N = 3 independent experiments, with ˜6 independent fields of view (containing different neurons) per experiment. C. Percentage of GFP-positive spots that are also immunostained for synaptophysin. N = 3 independent experiments, with ˜6 independent fields of view (containing different neurons) per experiment. Data information: The box plots were organized as follows: The middle line shows the median; the box edges indicate the 25th percentile; the error bars show the 75th percentile. Download figure Download PowerPoint Additionally, to evaluate a potential correlation between the expression levels and protein mobility, we compared the protein abundance and the time constants obtained for each individual synapse or axonal region we analyzed (Appendix Fig S5). We found no significant correlation for any of the proteins. This suggests that the mobility rates we measured are not drastically affected by the protein concentration changes produced by the expression (within the range caused by overexpression in our experiments). We next aimed to determine whether the mobility of ectopically overexpressed mEGFP fusions would be different from that of the native proteins, or from that of knock-in proteins expressed at physiological levels (Appendix Fig S6). We compared our results with FRAP analyses of the following proteins. (i) Native synaptotagmin 1, tagged using a fluorescently conjugated antibody against its intravesicular domain, which we analyzed in the past (Kamin et al, 2010). (ii) Genomically labeled, knock-in vGlut1Venus (Herzog et al, 2011). (iii) Knock-in alpha-synuclein-GFP, expressed in mouse brains at levels comparable to those observed in human disease cases (Spinelli et al, 2014). In addition, we also compared the FRAP curves of the proteins that are known to be exceptionally enriched in synaptic vesicles, and are not present at substantial levels in any other synaptic compartment, to FRAP curves of synaptic vesicles, obtained after labeling the vesicles with an FM dye (Shtrahman et al, 2005). All of these measurements were similar or nearly identical to our observations (Appendix Fig S6), which allows us to conclude that in our experimental setup neither mEGFP fusion, nor overexpression influences protein distribution and mobility in a major fashion. The synaptic protein mobility correlates to their presence in synaptic vesicles, but not to their sizes To extract biological insight from the FRAP experiments, we first considered the potential interactions of proteins with synaptic organelles, and especially with synaptic vesicles. A comparison of the mobility parameters of all proteins showed that proteins located in the synaptic vesicles and in endosomes have substantial immobile fractions in synapses (Fig 1E and F, Appendix Fig S4). Moreover, the FRAP time constants of the membrane proteins localized in synaptic vesicles correlated well with their enrichment in purified synaptic vesicles (Takamori et al, 2006, Appendix Fig S7). This confirmed the expectation that proteins that tend to localize to substantial levels in the plasma membrane had faster recovery kinetics than the proteins predominantly localized in the largely immobile vesicles (Appendix Fig S7). Interestingly, vesicular proteins also have higher time constants in axons, compared to other membrane proteins, although they are present in the axons mostly as proteins in the plasma membrane, and not as vesicles (Appendix Fig S8). An interesting case was that of VAMP4, whose recovery was substantially slower in axons than in the synapse, against the trend observed for most other proteins. VAMP4 tends to be found in endosomes in the axon, but not in the synapse, as observed in our immunostainings for this protein (Appendix Fig S2), and therefore, its axonal FRAP values are probably influenced by the slow recovery of endosomes through active transport. Additionally, a strong correlation is observed between the time constant and the immobile fraction in synapses, but not in axons (Appendix Fig S9). We then proceeded to test whether protein mobility can be linked to previously known protein characteristics such as structure, size, or localization. We found that for membrane proteins, both the time constants and the immobile fractions correlate positively with the number of transmembrane domains (Fig 4A, Appendix Fig S10A), in agreement with an expectation that the presence of multiple transmembrane domains would slow down diffusion (Kumar et al, 2010). For soluble proteins, however, we did not observe a correlation between molecular weight and the time constant (Fig 4B), as observed, for example, in bacteria (Kumar et al, 2010). Another simple observation was that membrane proteins, on average, were slower compared to soluble ones, which is in good agreement with the literature (e.g. Kumar et al, 2010). Both protein classes showed significantly higher time constants in synapses than in axons (Appendix Fig S10B), suggesting that the synaptic environment slows the movement of both protein classes. Figure 4. Correlation of protein mobility to various parameters A. Correlation of FRAP time constants in synapses with the number of transmembrane domains, for the different membrane proteins. A significant correlation can be observed, which agrees with the previous literature, and with the expectation that proteins with large numbers of membrane domains diffuse more slowly. B. No correlation between the FRAP time constants in synapses and the molecular weight of the soluble proteins could be observed. C. Correlation between the immobile fraction in synapses (for the membrane proteins) and the percentage of phenylalanine residues in the protein sequence. D. Correlation between the immobile fraction in axons and the percentage of adenine in the mRNA sequences. E. Correlation between time constants in synapses and the predicted fraction of unstructured coils in the protein structure. F. Correlation between immobile fractions in axons and protein lifetimes. See Appendix Figs S10–S14 for more details. Download figure Download PowerPoint Synaptic protein mobility correlates to several other cell biology parameters, including structural features of the proteins and their lifetimes We next aimed to determine whether the amino acid composition or the presence of certain structural motives can influence protein mobility. Such parameters have been linked to numerous features of the proteins in the past, such as their abundances or lifetimes (as mentioned in the introduction), which makes such a comparison also interesting for the protein mobility. We first compared the mobility parameters of the proteins to the amino acid composition of their sequences (Appendix Fig S11). Numerous correlations were found. For example, the synapse FRAP time constant was negatively correlated with the percentage of aspartate residues in the protein sequences (Appendix Fig S12A). As it bears a negatively charged side chain, aspartate is expected to increase protein solubility, which provides an explanation for this observation. Glutamate shows a similar trend, albeit this correlation was not statistically significant (Appendix Fig S11). In contrast, we observed strong positive correlations between the percentage of phenylalanine residues in the protein sequence and the synapse FRAP time constant (Appendix Fig S11). A similarly strong influence of the phenylalanine content was observed on the immobile fraction in synapses (Appendix Fig S11, Fig 4C). The effects of this amino acid were due to the contribution of membrane proteins, since no such correlation could be observed when only soluble proteins were considered (Appendix Fig S11). Moreover, the presence of other hydrophobic amino acids, as tryptophan, also correlated with low protein mobility in synapses. Overall, these observations are in agreement with the idea that proteins with higher numbers of transmembrane domains will contain proportionally more hydrophobic amino acids, while also being less mobile (Fig 4A, Appendix Fig S10A). Having noted these correlations, we next turned to test whether such observations would also hold true at the mRNA level. We analyzed the correlation between mobility parameters and the percentage of different nucleotides in the respe
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