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Research Articles: Cellular/Molecular

Transcriptomic Analysis of Ribosome-Bound mRNA in Cortical Neurites In Vivo

Rebecca Ouwenga1,2,3, Allison M. Lake2,3, David O'Brien2,3, Amit Mogha4, Adish Dani5,6 and Joseph D. Dougherty2,3,6

1Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA 2Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA 3Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA 4Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA 5Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA 6Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA

DOI: 10.1523/JNEUROSCI.3044-16.2017

Received: 29 September 2016

Revised: 29 June 2017

Accepted: 21 July 2017

Published: 8 August 2017

Author Contributions: RO: Developed and conducted SynapTRAP, data analysis, validation studies, and writing of manuscript; AL: Contributed to data analysis and writing of manuscript; AM: Conducted EM imaging.; DO: Contributed to data analysis; AD: Conducted STORM imaging; JD: Conceived of study, contributed to data analysis, and writing of manuscript.

Conflict of Interest: JDD has received royalties related to TRAP in the past. No other authors declare a conflict of interest.

We would like to thank M. Wong for Thy-1 mice, K. Monk, C. Weichselbaum and members of the Dougherty lab for helpful comments, N. Pisat, N. Kopp, K. Sakers, and S. Pyfrom for training, advice, and assistance.

This work was supported by the CDI (MD-II-2013-269), and NIH (R21DA038458, R21MH099798, R01NS102272). Key technical support was provided by the Genome Technology Resource Center at Washington University (supported by NIH grants P30 CA91842 and UL1TR000448). RO was supported by T32 GM081739. JDD is a NARSAD investigator.

Correspondence should be addressed to Contact: Dr. Joseph Dougherty, Dougherty Lab, Department of Genetics, 660 S. Euclid Ave, Campus Box 8232, St. Louis, MO 63110-1093, P: 314-286-0752, F: 314-362-7855, E: [email protected]

Cite as: J. Neurosci ; 10.1523/JNEUROSCI.3044-16.2017

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1 Title: Transcriptomic Analysis of Ribosome-Bound mRNA in Cortical Neurites In Vivo 2 3 Running Title: In Vivo Analysis of mRNA in Cortical Neurites 4 5 Authors: Rebecca Ouwenga1-3, Allison M. Lake2,3, David O’Brien2,3, Amit Mogha4, Adish Dani5,6, 6 Joseph D. Dougherty*2,3,6 7 8 1Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. 9 Louis, MO, USA 10 2Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA 11 3Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA 12 4Department of Developmental Biology, Washington University School of Medicine, St. Louis, 13 MO, USA 14 5Department of Pathology and Immunology, Washington University School of Medicine, St. 15 Louis, MO, USA 16 6Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, 17 MO, USA 18 19 Contact: 20 Dr. Joseph Dougherty 21 Dougherty Lab, Department of Genetics 22 660 S. Euclid Ave, Campus Box 8232 23 St. Louis, MO 63110-1093 24 P: 314-286-0752 25 F: 314-362-7855 26 E: [email protected] 27 28 Number of Pages: 29 29 Number of Figures: 10 30 Number of Tables: 8 31 Abstract: 198 words 32 Introduction: 678 words 33 Discussion: 1373 34 35 CONFLICT OF INTEREST 36 37 JDD has received royalties related to TRAP in the past. No other authors declare a conflict of 38 interest. 39 40 ACKNOWLEDGEMENTS 41 42 We would like to thank M. Wong for Thy-1 mice, K. Monk, C. Weichselbaum and members of 43 the Dougherty lab for helpful comments, N. Pisat, N. Kopp, K. Sakers, and S. Pyfrom for training, 44 advice, and assistance.

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45 ABSTRACT 46 47 Localized translation in neurites helps regulate synaptic strength and development. 48 Dysregulation of local translation is associated with many neurological disorders. However, due 49 to technical limitations, study of this phenomenon has largely been limited to brain regions 50 with laminar organization of dendrites such as the hippocampus or cerebellum. It has not been 51 examined in the cortex, a region of importance for most neurological disorders, where 52 dendrites of each neuronal population are densely intermingled with cell bodies of others. 53 Therefore, we have developed a novel method, SynapTRAP, which combines 54 synaptoneurosomal fractionation with Translating Ribosome Affinity Purification to identify 55 ribosome bound mRNA in processes of genetically defined cell types. We demonstrate 56 SynapTRAP’s efficacy and report local translation in the cortex of mice, where we identify a 57 subset of mRNAs that are translated in dendrites by neuronal ribosomes. These mRNAs have 58 disproportionately longer lengths, enrichment for FMRP binding and G-quartets, and their 59 genes are under greater evolutionary constraint in humans. In addition, we show that 60 alternative splicing likely regulates this phenomenon. Overall, SynapTRAP allows for rapid 61 isolation of cell-type specific localized translation and is applicable to classes of previously 62 inaccessible neuronal and non-neuronal cells in vivo. 63 64 65 SIGNIFICANCE STATEMENT 66 67 Instructions for making proteins are found in the genome, housed within the nucleus of each 68 cell. These are then copied as RNA and exported to manufacture new proteins. However, in the 69 brain, memory is thought to be encoded by strengthening individual connections (synapses) 70 between neurons far from the nucleus. Thus, to efficiently make new proteins specifically 71 where they are needed, neurons can transport RNAs to sites near synapses to locally produce 72 proteins. Importantly, several mutations that cause autism disrupt this process. It has been 73 assumed this process occurs in all brain regions, but has never been measured in the cortex. 74 We applied a newly developed method measure to study, for the first time, local translation in 75 cortical neurons. 76 77 INTRODUCTION 78 79 As all mRNA must come from a single cellular location (the nucleus) there is extensive post- 80 transcriptional regulation of RNA within cells, including localization of mRNA to specific 81 subcellular compartments. Localized translation of mRNA in specific subcellular compartments 82 allows more precise regulation of local protein concentrations, and thus modifies the functional 83 capacity of the compartment. A clear example exists in the nervous system where neurons 84 demonstrate remarkable capacity for regulated local translation with individual mRNAs 85 accumulating near activated synapses (Oswald Steward et al. 2014). Local translation in these 86 dendrites supports synaptic strengthening (Kang and Schuman 1996). While ultrastructural 87 evidence for localized translation in dendrites was first provided over thirty years ago (O.

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88 Steward and Levy 1982), it is still not clear which mRNAs are translated in cortical neurites nor 89 how this translational profile changes across cell types. 90 91 In addition, several psychiatric diseases have been observed to have perturbations in neuronal 92 local translation. For example, Fragile X syndrome, an Autism Spectrum Disorder (ASD)-related 93 syndrome, and other ASD-associated disorders, are caused by mutations in known local 94 translational regulators (Kelleher and Bear 2008; Ronesi and Huber 2008). Interestingly, it has 95 been shown that the degree of translational perturbation in models of Fragile X can vary across 96 brain regions (Qin et al. 2005); however, it is unclear the extent to which local translation also 97 differs across cell types in response to disease, development, or activity. To study these kinds of 98 perturbations, a method is needed to enrich for both processes-localized and cell-type specific 99 mRNA. 100 101 Previously, studies have utilized cell-culture based methods for examining the RNAs found in 102 neurites in vitro (Poon et al. 2006; Taliaferro et al. 2016) in addition to physical methods (LCM 103 and manual dissection) to isolate processes of certain populations in vivo. In vivo, these 104 isolation techniques were limited to cell types with neurites that grow in a physical layer distant 105 from the cell body, including the CA1 synaptic neuropil of the hippocampus (Cajigas et al. 2012; 106 Poon et al. 2006; Ainsley et al. 2014; Zhong, Zhang, and Bloch 2006), and the Purkinje cell layer 107 of the cerebellum (Kratz et al. 2014). Likewise, a recent study was able to capture translating 108 mRNAs from retinal-geniculate axons because only retinal cells expressed the necessary tag 109 (Shigeoka et al. 2016). While valuable for assessing local translation in these limited neuronal 110 cell types, this approach is unable to assess localization in the intermingled dendrites of 111 neurons found in most regions of the brain, and provides no evidence as to whether the mRNAs 112 are on ribosomes, a prerequisite for local translation. The development of a method to isolate 113 the ribosome bound mRNAs from neurites of densely intermingled cells would allow analysis of 114 local translation across a larger number of cell types in the central nervous system (CNS). 115 116 Here, we describe SynapTRAP, a novel method that permits the harvesting of ribosomes from 117 the intermingled processes of specific cell types in vivo. This method combines subcellular 118 fractionation on a sucrose-percoll gradient with Translating Ribosome Affinity Purification 119 (TRAP) to identify ribosome bound mRNA from neurons in the synaptoneurosomal fraction 120 (SNF). Because of the ability to harvest neuronal processes from a variety of neuronal types, cell 121 fractionation is effective for isolating neuronal projections of cell types that are intermingled 122 with other cells’ bodies, but fractionation alone provides no cell type specificity. The TRAP 123 technique utilizes a neuronal cell-type specific promoter to express an eGFP tagged ribosome in 124 only the cell type of interest (Doyle et al. 2008). Combining subcellular fractionation and TRAP, 125 SynapTRAP allows for the identification of ribosome bound mRNA from neural projections of 126 transgene expressing cells. This builds upon prior work combining TRAP with laminar ore 127 regional dissections (Ainsley et al. 2014; Kratz et al. 2014; Shigeoka et al. 2016), now extending 128 the methodology to allow for analysis of a myriad of CNS cell types where dendrites are not in 129 clearly dissectible lamina. 130

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131 Next, we demonstrate the utility of SynapTRAP by harvesting, for the first time, the RNA from 132 the intermingled processes of cortical neurons in vivo. We validate these findings with 133 independent methods: SynapTRAP results are consistent with in situ RNA localization, and 134 bioinformatics approaches can identify motifs and other features of the candidate local 135 translation transcripts that are consistent with previous findings. Finally, we show that splicing 136 does appear to regulate several mRNAs’ localization to neuronal processes, however there is no 137 overall significant enrichment for differential splicing in the localization of RNAs within the cell. 138 Overall, SynapTRAP allows for the high throughput investigation of localized translation in cell- 139 types never previously accessible in vivo. 140 141 MATERIAL AND METHODS 142 143 Animals 144 145 All procedures were performed in accordance with the guidelines of Washington University’s 146 Institutional Animal Care and Use Committee. Mice were maintained in standard housing 147 conditions with food and water provided ad libitum. The Tg(Snap25-eGFP/Rpl10a)JD362Jdd 148 (RRID:IMSR_JAX:030273) (Dougherty et al. 2012) bacTRAP mouse was used for SynapTRAP 149 experiments and the Tg(Thy1-EGFP)MJrs/J (RRID:IMSR_JAX:007788) line was utilized for FISH 150 analysis. 151 152 Sucrose Percoll Gradient Preparation of a SN Fraction 153 154 The sucrose-percoll gradient was modified from (Westmark et al. 2011). Briefly, each column 155 was loaded with 2mL of homogenated sample in a modified Homogenization Buffer (5mM Tris- 156 Cl pH 7.5, 250mM sucrose, 0.5mM DTT, 100ug/mL cycloheximide, 1mM tetrodotoxin (Tocris 157 #1069), 35 units rRNAsin (Promega #N2511), 35 units SUPERaseIn (Ambion #AM2694) and 1 158 tablet/10mL cOmplete mini EDTA free protease inhibitor cocktail tablets (Roche 159 #4693159001)). Gradients were made as described (Westmark et al. 2011) in open-top tubes 160 (Seton #5042) and spun at 32,500xg for 5 minutes in a Sorvall RC-5C without brake. To harvest 161 the SN fraction (SNF) a 16g needle was used to puncture the bottom of the tube for collection 162 of sequential fractions. The initial 1mL consists of large organelles and was discarded. The next 163 3.5mL was collected as the SN-containing fractions. A Salt Lysis Buffer (100mM HEPES, 1.5M 164 KCL, 10% NP-40, 50mM MgCl2, and 30mM DHPC) was added to the SNF at 10% volume to lyse 165 the SN membranes and stabilize the exposed ribosomes. 166 167 SynapTRAP and Library Preparation 168 169 For each of three replicates, the cortices of three Tg(Snap25-eGFP/Rpl10a)JD362Jdd bacTRAP mice 170 at 21 days post birth were pooled (not separated by sex), and homogenized in 3.5mL modified 171 Homogenization Buffer (described above) in glass homogenizer (10 strokes each pestle, Kontes 172 7mL), and spun at 1000xg in a Sorval RT7 for 10 minutes at 4˚C. The supernatant was split into 173 two samples. For the whole cell homogenate (WCH) samples, 500uL of the supernatant was 174 incubated with 50uL Salt Lysis Buffer (described above) for 15 minutes and spun at 20,000xg for

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175 15 minutes at 4˚C to reduce cell debris. The remaining supernatant (2mL), for the SN samples, 176 was fractionated on a discontinuous sucrose-percoll gradient to harvest the SN rich sample as 177 described above. Then, a portion of the WCH and SNF samples underwent TRAP for affinity 178 purification of neuronal ribosomes, as described (Heiman et al. 2008). Briefly, samples were 179 incubated with anti-eGFP coated biotinylated magnetic beads (30uL beads and 50 ug each of 180 two anti-GFP antibodies (clone 19f7, 19fc8) per sample). After a 4-hour incubation at 4°C, 181 samples were washed using a high salt wash and resuspended. RNA from the parallel affinity 182 purified samples (Whole cell TRAP and SynapTRAP) and matched background controls (WCH 183 and SNF) was harvested using a Qiagen RNEasy MinElute kit. RNA concentration was measured 184 using a Nanodrop and diluted to <5ng/uL before being assessed for quality and concentration 185 using an Agilent RNA 6000 Pico Kit on an Agilent Bioanalyzer. RNA samples were reverse 186 transcribed into cDNA and amplified using Nugen Ovation RNA-seq System V2 kit (Nugen 7102), 187 per the manufacturer’s instructions. cDNA was fragmented using a Covaris E210 sonicator using 188 duty cycle 10, intensity 5, cycles/burst 200, time 180 seconds. cDNA was blunt ended, had an A 189 base added to the 3’ ends, and then had Illumina sequencing adapters ligated to the ends. 190 Ligated fragments were then amplified for twelve cycles using primers incorporating unique 191 index tags. Libraries were normalized and sequenced on an Illumina HiSeq-2500 using single 192 reads extending 50 bases by the Genome Technology Access Center (GTAC) at Washington 193 University. 194 195 Sequencing, Analysis, and Data Availability 196 197 Analysis is as previously described (Reddy et al. 2016). Reads were trimmed of adapter 198 sequences with Trimmomatic (Bolger, Lohse, and Usadel 2014) (v. 0.32, RRID:SCR_011848), 199 ribosomal RNAs were removed by mapping to rRNA sequences using Bowtie2 (Langmead and 200 Salzberg 2012) (RRID:SCR_005476), and remaining reads were aligned to Ensembl version 75 of 201 the mouse genome using STAR (Dobin et al. 2013). Counts were performed by HTseq (Anders, 202 Pyl, and Huber 2015) (RRID:SCR_005514). Because of varying levels of mitochondrial rRNA 203 between sample types, tRNA, mitochondrial and remaining eurkaryotic rRNA reads were 204 excluded, as were genes without at least two counts per million (CPM) in two samples. Counts 205 were then normalized to final CPM based on the final library sizes. Differential expression was 206 identified using edgeR (McCarthy, Chen, and Smyth 2012) (RRID:SCR_012802). All data are 207 available at GEO: GSE74506. 208 209 For downstream analysis, two gene lists were defined. The first consists of candidates for local 210 translation in neuronal processes (Local Translation Candidates). These 153 genes had 211 transcripts enriched both in the discontinuous percoll gradient (higher expression in the SNF 212 than in the WCH, p<.05) and through TRAP (higher expression in the SynapTRAP sample than in 213 the Fraction, p<.05) (equation (1)). 214 215 (1) Local Translation Candidates=(CPMSynapTRAP > CPMSNF)ˆ(CPMSNF > CPMWCH) 216 217 The second list consists of candidates for transcripts that are neuronal, but not transported to 218 cellular processes (Somatic Translation Candidates). These 315 genes had transcripts that were

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219 depleted by the discontinuous percoll gradient (lower expression in the SNF than in the Whole 220 Cell Homogenate, p<.05) but still enriched by TRAP (higher expression in the TRAP sample than 221 in the Whole Cell Homogenate, p<.05) (equation (2)). 222 223 (2) Somatic Translation Candidates=(CPMWCH>CPMSNF) ˆ(CPMTRAP>CPMWCH) 224 225 Fluorescent In Situ Hybridization (FISH) 226 227 In situ hybridizations were performed on 20uM coronal brain slices from three male five-week- 228 old Tg(Thy1-EGFP)MJrs/J mice following 4% paraformaldehyde transcardial perfusion, 12 hour 229 incubation in 15% Sucrose in PBS, and 12 hours in 30% Sucrose in PBS at 4°C. Sections on slides 230 were post-fixed in 4% paraformaldehyde and acetylated (800uL Triethanolanime, 110uL 10N 231 NaOH, 45mL H20, 125uL Acetic Anhydride). Slides were hybridized at 63C with 100ng Dig 232 labeled antisense RNA probe created with T7 polymerase (Promega #P2075), from PCR 233 products using primer sequences from Allen Brain Atlas (Lein et al. 2007), and DIG RNA Labeling 234 Mix (Roche #11277073910) according to manufacturer’s protocol. Probe detection was 235 performed using Sheep Anti-Dig- POD (Roche #11207733910) followed by Tyramide Signal 236 Amplification Cyanine 3 Tyramide (PerkinElmer #NEL704A001KT). Samples were imaged on a 237 Perkin Elmer UltraView Vox spinning-disk confocal microscope. For each probe, nine total slices 238 from three five-week-old mice were imaged for GFP positive neurites using a 63X oil lens. 239 Images were quantified using ImageJ software using macros that were consistent across 240 probes. GFP images were converted to a black and white image using a threshold on brightness 241 and then measured for the percent area of GFP. Any soma in the images were removed from 242 analysis by masking. Probe images were then also converted to a black and white image using a 243 threshold on brightness. Each probe and corresponding masked GFP image were overlaid and 244 all the probe puncta not overlapping with the GFP image were thus masked. The remaining 245 overlapping probe puncta were quantified. The number of puncta were divided by the area of 246 the GFP signal for final analysis. Significance of overlap was determined with two tailed t-test 247 between each probe and the no probe control. 248 249 Camk2a (amplified from pcTOPOII plasmid - cut with SpeI) (Dani et al. 2010) 250 F- AGT CTC CAA GCC AAC CCC 251 R- CCT GGT GTG CGC TCT AT 252 Snph 253 F- AGA GTC TCT GAG TGT GCT TCC C 254 R- TAA TAC GAC TCA CTA TAG GGA GTT GGA CTA AAT GCC AGT GGT 255 Nsmf 256 F- GAC TGG GAC ACA GAG AAA GGT C 257 R- TAA TAC GAC TCA CTA TAG GGA TTG TTA ATC TGG ACC ACC AGG 258 Mllt6 259 F- GTT GTC TCA ACA GCC TGA CAG A 260 R- TAA TAC GAC TCA CTA TAG GGC TCC AGT CTC TCT CCT CCA TGT 261 Hist3hba 262 F- AGC CAG TGC AGC AGG ATG

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263 R- TAA TAC GAC TCA CTA TAG GGA AGA GCC TTT GGG TTG GG 264 265 Quantitative PCR 266 267 To confirm replication of RNA enrichment by the SynapTRAP three additional independent 268 biological replicates of the WCH, SNF, and SynapTRAP were collected as described above and 269 reverse transcribed using Quanta qScript Reverse Transcriptase (Quanta 84002). Three 270 technical replicates of each of these samples were quantified with iTaq Universal Sybr green 271 (BIO-RAD 1725120) on a QuantStudio 6 Flex (Applied Biosystems) in a 10uL volume with 272 amplicons less than 200 nucleotides. βactin was used as an endogenous control. Statistical 273 testing was determined by ANOVA with 5 degrees of freedom in R statistical software. Primer 274 sequences from PrimerBank (X. Wang et al. 2012) were as follows. 275 276 Lcp1 PBID: 31543113a1 277 F- TCC GTG TCT GAC GAA GAA ATG 278 R- GCG GCC TTG AAC AAG TCA T 279 Gsn PBID: 28916693a1 280 F- ATG GCT CCG TAC CGC TCT T 281 R- GCC TCA GAC ACC CGA CTT T 282 Neurl1a PBID: 15420883a1 283 F- ACT ATC CAC GAC TCC ATC GGG 284 R- AGG ATC TGG GAG CCC TTA GTG 285 Nsmf PBID: 26334509a1 286 F- GAG GCC ATG TCC TCG GTA G 287 R- GCG GTT CTC AGG GTG ACT C 288 Myh14 PBID: 29336026a1 289 F- CAG TGA CCA TGT CCG TGT CTG 290 R- CGT AGA GGA ACG ATT GGG CTG 291 Armc6 PBID: 12846520a1 292 F- CCC AGG AAA CCT TTG ATG CTG 293 R- GCC ATC CAG TGA TAC TTT CGG TA 294 Cpne6 PBID: 6753510a1 295 F- CAA AGC CGC ATC CAT GTG TG 296 R- TTG AAC AGG AGC GAA GCA CC 297 Tesc PBID: 118130497c1 298 F- GCT GCA TCG GAG GTT CAA G 299 R- GAT TTT GGA TCG GAT CGG GTT 300 Camk2a PBID: 161086916c1 301 F- TGG AGA CTT TGA GTC CTA CAC G 302 R- CCG GGA CCA CAG GTT TTC A 303 Snap25 PBID: 6755588a1 304 F- CAA CTG GAA CGC ATT GAG GAA 305 R- GGC CAC TAC TCC ATC CTG ATT AT 306 Shank3 PBID: 255918226c1

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307 F- CCG GAC CTG CAA CAA ACG A 308 R- GCG CGT CTT GAA GGC TAT GAT 309 Sox10 PBID: 226423936c2 310 F- AGG TTG CTG AAC GAA AGT GAC 311 R- CCG AGG TTG GTA CTT GTA GTC C 312 313 Electron Microscopy of the SN Fraction 314 315 An SNF preparation was performed as described above and pelleted to concentrate the sample 316 via fifteen min at 15,000 × g (11,000 rpm in an SS-34 rotor) at 4°C. Pellets were fixed in 4% 317 paraformaldehyde in 0.1 M sodium phosphate buffer for thirty minutes. After washing with 318 phosphate buffer, pellets were placed on filter paper soaked with 0.1 M sodium phosphate 319 buffer and cut into four to six pieces with a razor blade. Each piece of filter paper was 320 processed with 0.1% osmium tetraoxide in 0.1 M sodium phosphate buffer followed by washing 321 and serial dehydration using ethanol at increasing concentrations (i. e. 25%- 50%- 75%- 95%- 322 100%) for twenty minutes each. This was followed by processing in propylene oxide for twenty 323 minutes and then propylene oxide (PO)+ EPON in 2:1 ration for one hour, followed by PO+EPON 324 (1:1) overnight. Each sample was then embedded in 100% EPON and baked at 65°C for two 325 days. Samples were sectioned at 70 nm, stained with uranyl acetate and Sato’s lead stain and 326 then visualized under a Jeol (JEM-1400) transmission electron microscope. Images were 327 recorded with an Advanced Microscopy Techniques V601 digital camera. 328 329 Stochastic Optical Reconstruction Microscopy (STORM) 330 331 STORM imaging was performed on a custom built system as described (Suleiman et al. 2013). 332 Cryosections were immunolabelled with primary antibodies, Chicken anti-GFP (Invitrogen), 333 Mouse anti-Bassoon (Novus Biologicals) and Rabbit anti-Homer1 (Synaptic Systems) followed by 334 anti-chicken, anti-rabbit and anti-mouse secondary antibodies raised in donkey (Jackson 335 Immunoresearch). Secondary reagents were custom conjugated with acceptor and reporter 336 fluorophore dye pairs Cy3-Alexa647, Cy2-Alexa647 and Alexa405-Alexa647. Immunolabelled 337 sections were overlaid with a buffer containing 100mM Tris-HCL pH8.0, 150mM NaCl and 338 containing an oxygen scavenging system comprised of glucose, glucose oxidase, catalase and 339 the reducing agent 2-mercaptoethylamine. After removing excess buffer, edges of the coverslip 340 were sealed with nail polish before imaging. Sparse single molecule images were acquired at 341 60Hz frequency using an imaging and activator laser sequence: one frame of weak activator 342 laser (561/488/405) followed by three frames of 642nm laser at 560W/cm2, repeated as a train 343 for each activator dye-antibody combination. The intensity of activation lasers was adjusted to 344 ensure sparse single molecule events in each camera frame. Raw image stacks were analyzed to 345 determine the centroid positions of fluorescent intensity peaks and these STORM localizations 346 were rendered as images using custom software. 347 348 Western Blot 349

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350 Three independent SNF preparations were performed as described above. Samples were 351 collected from total cortical dissection homogenate (Hom) in homogenization buffer, 352 supernatant that is the input to the discontinuous column after cell lysis (Load/Loading 353 Control), the membrane fraction (Mem), and the SNF. 10uL of Homogenate, 10uL Loading 354 Control, 30uL of the Mem, and 30uL of the SNF samples (diluted to 40uL) were loaded onto a 4- 355 12% polyacrylamide gel and semi-dry transferred to a PVDF membrane. Total protein was 356 quantified using ImageJ software on the intensity of the Ponceau stain from 20 to 100 kda on 357 each sample. Individual proteins were probed using Mouse PSD-95 (1:1000 Enzo VAM-PS002 358 RRID:AB_2039456), Mouse NeuN (1:500 Chemicon International MAB377 RRID:AB_2298772), 359 Chicken GFP (1:1000 Aves GFP-1020 RRID:AB_10000240), Rabbit Olig2 (1:1000 Chemicon 360 International AB 9610 RRID:AB_10141047). HRP secondaries (BIO-RAD) were used at 1:10,000. 361 Blots were developed in Clarity Western ECL Substrate (BIO-RAD) for 5 minutes before imaging 362 on a Thermoscientific MyECL imager. ImageJ was used to quantify the pixel intensity of the 363 protein band. This intensity was divided by the intensity of the total protein of the sample to 364 correct for the amount of protein loaded onto the lane and normalized to the intensity of the 365 homogenate. Analysis by T-test with 4df of SNF samples compared to loading controls. 366 367 Pathway and Cell Type Analysis 368 369 Pathway analysis for Figure 8A, B was conducted with the BINGO (3.0.3) plugin for Cytoscape 370 (2.8.2) (Maere, Heymans, and Kuiper 2005). A hypergeometric test with Benjamini-Hochberg 371 multiple testing correction was implemented to detect over-represented categories from 372 GO_MF, GO_BP, and GO_CC. Highly similar results were found using NIH DAVID (Huang, 373 Sherman, and Lempicki 2009) against a background set of neuronal genes identified in the WCH 374 above 2 CPM, and displayed in Figure 8C, D. 375 376 Initial analysis of cellular sources of mRNA from the SNF was conducted with the Cell Type 377 Specific Expression Analysis (CSEA) tool as described, using the 150 most significantly enriched 378 transcripts (Xu et al. 2014). Glial mRNAs were confirmed by downloading the Barres lab dataset 379 (barreslab_rnaseq.xlsx) from http://web.stanford.edu/group/barres_lab/brain_rnaseq.html. 380 While not limited to forebrain, this database is better age-matched to the current experiment 381 than previous TRAP analysis and includes direct measures of microglial and endothelial mRNAs. 382 To identify transcripts significantly enriched in each cell type, we used the specificity index 383 algorithm (Dougherty et al. 2010) with default settings except a p_max cutoff of pSI<.01. 384 385 CSEA was additionally applied to SNF mRNA using single-cell transcriptomic profiling data from 386 mouse cortex (Zeisel et al. 2015). Raw mRNA count data was downloaded from 387 http://linnarssonlab.org/cortex/. Counts were incremented by a pseudo-count of 1 and then 388 RPKM-normalized. RPKMs were averaged over each of the 47 cell subclasses identified by the 389 authors using the BackSPIN clustering algorithm. Significantly enriched transcripts in each 390 subclass were identified using the SI algorithm with a minimum expression value of 6 RPKM and 391 default settings otherwise. 392 393 Sequence Feature Analysis

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394 395 Analysis of sequence length, GC content, and predicted RNA secondary structure stability was 396 performed on the longest protein-coding isoform of each gene with annotated 5’ and 3’ UTRs, 397 as determined from Ensembl version 75 annotation data retrieved using the biomaRt package 398 in R. Filtering for protein coding sequences with annotated UTRs resulted in the elimination of 399 11 genes from feature analysis, resulting in n=457 total genes (146 Local Translation 400 Candidates, 311 Somatic Translation Candidates). For statistical analysis, sequence length was 401 log2-transformed to achieve a normal distribution. RNA secondary structure stability of UTRs 402 was predicted using ViennaRNA RNAfold program (Lorenz et al. 2011), which computes the 403 minimum-free energy secondary structure of each sequence. Free energy values were then 404 normalized by sequence length, and multiplied by -1 to give a positive index of RNA structure 405 stability in the UTR. Comparisons of Somatic and Local Translation Candidates were performed 406 using Welch’s t-test with Benjamini-Hochberg multiple testing correction. 407 408 Overlap Analyses 409 410 FMRP targets were downloaded from table S2 of (Darnell et al. 2011), subsetted to those with 411 p<.01 in their data, and tested for overlap with a One-Tailed Fisher’s Exact Test. To confirm this 412 result was not driven by the known length or expression biases, 1000 random gene sets, 413 sampled to match the FMRP list for transcript length and expression biases as described 414 (Ouwenga and Dougherty 2015), were also analyzed. True p-value exceeded significance of all 415 1000 permuted p-values. Constrained genes were from supplemental table 13 of (Lek et al. 416 2016) Other gene lists were from (Lein et al. 2007) supplemental table 4, (Ainsley et al. 2014) 417 supplemental table 2, and (Cajigas et al. 2012) supplemental table 10. In each case gene lists 418 were filtered to consider only those transcripts that were robustly measurable in the WCH 419 (CPM>2). 420 421 Motif Enrichment Analysis 422 423 Multiple Em for Motif Elicitation (MEME) (Bailey and Elkan 1994) was used to identify 424 overrepresented 3’UTR sequence motifs in Local Translation Candidates and Somatic 425 Translation Candidates. One 3’UTR from each candidate list was removed due to prohibitively 426 short sequence length (<15 nt). The MEME tool was run with the following parameters: zero or 427 one occurrences per sequence, min. width = 6, max. width = 50, min num. sites = 16, max 428 num. motifs = 20. The motif search was run in both normal mode and discriminative mode. In 429 discriminative mode, Local Translation Candidates were scanned using the Somatic Translation 430 Candidates as background, and vice versa. Enrichment of G-quadruplex motifs in Local 431 Translation Candidates and polyadenylation (poly(A)) signals in Somatic Translation Candidates 432 was further verified using the stringr package in R to count occurrences of regular expression 433 matches to G3+N1-7G3+N1-7G3+N1-7G3+ and AATAAA, respectively. 434 435 Alternative Splicing Analysis 436

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437 Alternative splicing analysis was conducted in MISO (v. 0.5.3) (Katz et al. 2010). RNA-seq 438 reads, filtered as described above, were aligned to the mm9 mouse genome (Ensembl v. 67) 439 using STAR. Percent spliced in (Ψ) values were calculated for each sample by running MISO on 440 each alignment file using the version 1 of the exon-centric mm9 alternative splicing events 441 compiled by Wang et al (E. T. Wang et al. 2008). All alternative event types available in the 442 annotation were analyzed: alternative 3’ splice sites (A3SS), alternative 5’ splice sites (A5SS), 443 alternative first exons (AFE), alternative last exons (ALE), mutually exclusive exons (MXE), 444 retained introns (RI), skipped exons (SE), and tandem UTRs (TandemUTR). 445 446 All downstream processing and analysis of MISO output was conducted in R. Alternative splicing 447 events were filtered by requiring that events meet the following coverage criteria for at least 9 448 of the 12 samples: at least 10 reads supporting both isoforms, with at least 1 read supporting 449 the exclusion isoform. Samples were assessed for differential Ψ (ΔΨ) analogously to the 450 differential expression analysis described above, using a permuted t-test with 5000 iterations. 451 Similarly to the definition of Local Translation Candidates, neurite-enriched alternative 452 splicing events were defined as the set of events with significantly altered Ψ (p<0.05) in both 453 SNF vs. WCH and SynapTRAP vs. SNF comparisons (Figure 9-1). Soma-enriched alternative 454 splicing events were defined analogously (Figure 9-2). For analysis of global trends in ΔΨ 455 between samples, alternative splicing events were filtered down to those located in genes 456 identified as confidently expressed in neurons, based on TRAP/WCH ratios of negative control 457 genes as described (Dougherty et al. 2010), but were not filtered based on ΔΨ p-value. 458 459 Experimental Design and Statistical Analysis 460 461 All statistical tests are reported in the Material and Methods section for each experiment. The 462 design, sample sizes, intermediate values, and results can be found in the legend of each figure 463 in which they are represented. The SynapTRAP preparations, western blot, electron 464 microscopy, and qPCR were performed with both sexes pooled in each column. ISH replication 465 used only male mice. All data are available at GEO: GSE74506. 466 467 468 RESULTS 469 470 Synaptoneurosomal Fractionation Enriches for Processes and mRNAs of Multiple Cell Types 471 472 We first sought to determine the mRNA composition of a synaptoneurosomal fraction using a 473 classic biochemical technique to harvest membrane enclosed pre and post-synaptic positions 474 (Westmark et al. 2011). SNFs previously have been shown to be competent for translation by 475 incorporation of radiolabeled methionine (Westmark et al. 2011). They have also been show to 476 contain mRNA detectably by quantitative PCR (qPCR) (Meyer-Luehmann et al. 2009), suggesting 477 they may be amenable to RNAseq analysis. Examination of our SNF with electron microscopy 478 confirmed that the sample contained synaptoneurosomes (Figure 1A), and immunoblots 479 showed the expected enrichment for the synaptic marker PSD-95, and depletion of the 480 neuronal nuclear marker NeuN and glial nuclear marker Olig2 (Figure 1B,C). We then optimized

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481 a method for RNA purification from SNFs. Three replicate samples of both the SNF and the 482 starting cortical whole cell homogenate (WCH) were analyzed by high throughput RNA- 483 sequencing. Overall, 3,408 transcripts were found to be significantly enriched by this 484 fractionation (Figure 1-1). Examination with Cell type Specific Expression Analysis (CSEA) (Xu et 485 al. 2014) suggested the SNF is enriched in mRNAs expressed in both neuronal and glial cell 486 types from adult cortex (Figure 2A). To confirm this, we also utilized the Barres lab RNA- 487 sequencing database (Zhang et al. 2014). This confirmed that almost 40% of the top SNF 488 enriched transcripts were specifically expressed in various types of glia (Figure 2B). Both the 489 Barres and original Xu analyses had limited numbers of subtypes of cells available. Therefore, 490 we updated CSEA using as a foundation a newer single cell RNAseq profiling from mouse 491 forebrain (Zeisel et al. 2015). Using this more fine-grained view of cell types, we confirmed 492 transcripts enriched in multiple types of neurons and glia are detected in the SNF (Figure 2C). 493 Thus, while SNFs enrich for a subpopulation of transcripts that include those known to be 494 localized to dendrites of hippocampus, such as CamKIIa (Burgin et al. 1990) (2.19 Fold Change, 495 p=.028), mRNA from additional cell types is enriched in the SNF as well. 496 497 Tagged Ribosomes Localize to Processes in Many Neuronal Cell Types Across the CNS 498 499 Ribosomes have long been observed near synapses by electron microscopy (O. Steward and 500 Levy 1982) and are observable in transgenic mice via GFP expression. Previously, we generated 501 dozens of ‘bacTRAP’ mouse lines which express eGFP/Rpl10a in a wide variety of neuronal types 502 for cell type specific translational profiling (Doyle et al. 2008; Dougherty et al. 2012; Dalal et al. 503 2013; Dougherty et al. 2013). While characterizing these lines we noticed that eGFP/Rpl10a, 504 which predominantly filled the soma, also extended more weakly but consistently into 505 processes connected to the cell bodies (Figure 3). Higher resolution confocal analyses 506 confirmed GFP positive puncta throughout the dendrites of Purkinje cells (Figure 3B). To 507 validate that this pattern was not unique to Purkinje cells, we also imaged a bacTRAP line 508 (Slc6a4-eGFP/Rpl10a (Dougherty et al. 2013, 6), Figure 3C-E) labeling midbrain serotonin 509 neurons. Dendrites of these neurons are also sufficiently sparse to enable super-resolution 510 analyses of individual processes. Stochastic Optical Reconstruction Microscopy (STORM) 511 revealed eGFP/Rpl10a extending into the dendrites and localizing in puncta within <1 uM of 512 synapses as defined by apposition of presynaptic and postsynaptic markers (Dani et al. 2010). 513 Thus, the eGFP ribosomal fusion protein is localized to neurites and near synapses. 514 515 SynapTRAP: A Strategy for mRNA Isolation from the Processes of Cortical Neurons 516 517 Since the eGFP/Rpl10a fusion proteins localize to neurites, utilizing TRAP on the SNF could 518 produce a sample enriched for local mRNAs on neuronal ribosomes. We first confirmed by 519 immunoblots that the SNF clearly contains eGFP/Rpl10a (Figure 1B, C). We utilized the Snap25- 520 eGFP/Rpl10a line, which labels all cortical neurons (Dougherty et al. 2012), and isolated four 521 RNA sample types from a cortical dissection (Figure 4): WCH before and after affinity 522 purification (i.e. standard ‘whole cell’ TRAP) and the SNF before and after affinity purification 523 (SynapTRAP). Bioanalyzer analysis of each confirmed that high quality RNA is present in the 524 SNF. In addition, robust detection of the 18S rRNA (which is only in the small subunit), after

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525 SynapTRAP capture affinity purification of eGFP-Rpl10a (which is on the large subunit), 526 indicates we captured intact 80s ribosomes: the large subunit does not engage the small 527 subunit until initiation of translation (Figure 4B). This indicates that the SynapTRAP method is 528 able to collect ribosome-bound mRNA from the SNF. We then sequenced the RNA from all four 529 sample types. All four conditions showed good alignment rates: at least 74% of reads mapped 530 uniquely after read trimming and rRNA removal. Greater than 86% of mapped reads were 531 exonic (Figure 4C). 532 533 First, we examined the distributions of the log2 ‘fold changes’ when comparing WCH to the 534 SNF, as well as the SynapTRAP/SNF and TRAP/WCH (Figure 5A). The distribution of the 535 SNF/WCH comparison was markedly bimodal, with a subset of the >3400 transcripts showing 536 robust enrichment in the SNF. Enrichment by TRAP from WCH was unimodal, as expected from 537 prior studies. TRAP compared to input from the SNF fraction was also unimodal, though it is 538 worth noting that the range of the distribution in the SynapTRAP/SNF comparison is attenuated 539 compared to the TRAP/WCH distribution and enrichment of neuronal genes and depletion of 540 glial genes is less robust in the SynapTRAP/SNF comparison. Quantifying this specifically (Figure 541 5B), known glial genes are significantly depleted relative to known neuronal genes in both 542 comparisons (p<2.2E-16 and p<.0001, Welch’s T-tests), as expected, though with substantially 543 lower magnitude in SynapTRAP/SNF (difference in mean log2 fold change: 0.11) than standard 544 TRAP/WCH (0.96). Therefore, we conservatively focused downstream analyses only on the most 545 significantly enriched transcripts in either comparison. 546 547 A comparative analysis from these four conditions was used to generate two lists. First, to 548 identify candidates for local translation, we selected transcripts that were enriched by both 549 TRAP and cellular fractionation (Local Translation Candidates, Figure 5C). Second, as a 550 comparison group, we identified candidates sequestered to the somatic region (Somatic 551 Translation Candidates, Figure 5D) by selecting transcripts that were neuronal (i.e. enriched by 552 TRAP/WCH) but relatively depleted in the SNF/WCH. This highlighted 153 Local Translation 553 Candidates and 315 Somatic Translation Candidates with high confidence for further analysis 554 (Figures 5-2, 5-3). 555 556 Reassuringly, the high confidence Local Translation Candidates included mRNAs known to 557 localize in neuronal processes in other brain regions such as Arc, Shank3, and CamkIIa (Epstein 558 et al. 2014; Oswald Steward et al. 2014; Burgin et al. 1990). The candidates also overlap with 559 several previous studies of localized translation. The Local Translation Candidates overlap 560 significantly (Figure 5E) with hippocampal neuropil transcripts studied by microdissection and 561 RNAseq (p<.014, Fisher’s Exact Test) (Cajigas et al. 2012), microdissection combined with 562 TRAPseq (p<2.5E-11) (Ainsley et al. 2014), and in situ hybridization (p<.0002) (Lein et al. 2007), 563 but not a recent study of axonal transcripts in the adult and postnatal day 7 retinal-thalamic 564 projection (p<.25) (Shigeoka et al. 2016). The list also included novel candidates such as Brsk1, 565 which codes for a protein involved in the polarization of cortical neurons. This kinase has a 566 nuclear role involved in DNA damage repair, but the protein also localizes to synapses for a 567 secondary role in which it mediates the phosphorylation of microtubule associated proteins and 568 aids in neurotransmitter release (Lu, Niida, and Nakanishi 2004; Müller, Lutter, and Püschel

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569 2010; Inoue et al. 2006; Yoshida and Goedert 2012). Interestingly, we also observed several 570 other transcriptional regulators in our list of Local Translation Candidates, consistent with a 571 prior report (Ainsley et al. 2014). These additional mRNAs may indicate the existence of 572 signaling pathways that employ spatial control of translation to regulate nuclear functions. 573 Alternatively, some of these proteins, like BRSK1, may have a secondary role at the synapse 574 (Bright, Carling, and Thornton 2008). SynapTRAP is a viable way to select candidates such as 575 these for further experimentation. Here we focused first on validation of this new method with 576 established assays, followed by computational analyses to identify pathways and features of 577 transcripts most enriched in local translation. 578 579 Validation of Local Translation Candidates with Independent Methods 580 581 First, to confirm reproducibility of the RNA-sequencing results, qPCR analysis was performed on 582 three additional independent replications. The results validated both the increased abundance 583 of Local Translation Candidates (Figure 6A) and decreased abundance of Somatic Translation 584 Candidates (Figure 6B) in the SynapTRAP samples. Positive and negative controls from previous 585 hippocampal studies (Ainsley et al. 2014; Cajigas et al. 2012) were also included and showed 586 the expected changes (Figure 6C). 587 588 Next, to verify the existence of these mRNAs in the processes of neurons in vivo, several Local 589 Translation Candidates were analyzed using fluorescent in-situ hybridization (FISH). In order to 590 identify single neurites among the densely interwoven neurons of the cortex, Tg(Thy1- 591 EGFP)MJrs/J mice were used as they sparsely label cortical neurons in vivo. Each Local 592 Translation Candidate showed hybridization signal in GFP labeled neurites (Figure 7) 593 significantly more than no probe controls. In contrast, analysis of a Somatic Candidate, 594 Hist3h2ba, did not significantly differ from no probe controls in the number of puncta in 595 neurites, despite a robust FISH signal present proximal to DAPI nuclei stain of adjacent cells. 596 This suggests that Hist3h2ba is sequestered to the somatic region and Local Translations 597 Candidates co-localize with neurites as predicted from RNA-sequencing results. 598 599 Pathway Analysis Highlights Morphological Regulation 600 601 As a final validation of the method, we turned to informatics approaches. We reasoned that if 602 SynapTRAP is successfully enriching for locally translated transcripts, these should be 1) 603 enriched in functional terms expected to occur in neurites and 2) have detectable sequence 604 features that might mediate their peripheral translation. Thus, we first conducted a 605 comparative pathway analysis of the two candidate lists, then tested the hypothesis that there 606 are sequence features which distinguish these two sets of genes, assessing both predicted and 607 empirically detected binding motifs for proteins and RNA. 608 609 Examination of the Local Translation Candidates highlighted several pathway enrichments 610 (Figure 8A, C). As expected there was significant enrichment of synapse (p=6.14E-07), axon 611 (p=2.04E-3), dendrite (0.030), post synaptic density (2.53E-05), and other neuronal projection 612 related terms (Figure 8C). Additionally, there was a clear enrichment of pathways related to

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613 cytoskeleton, motors, and junctions of cells. As βactin mRNA has long been noted for active 614 shuttling to subcellular compartments (Lawrence and Singer 1986), and others have shown the 615 presence of these cytoskeletal mRNAs in dendrites and axons (Ainsley et al. 2014; Cajigas et al. 616 2012; Shigeoka et al. 2016). Thus local translation of additional constituents of these pathways 617 is rational. Yet the robustness of this enrichment overall was unexpected. Cytoskeletal proteins 618 comprised 17.6% of the candidate list (p=4.43xE-05) and a 7.7 cluster enrichment score of actin 619 binding proteins from a background of genes represented in the WCH samples. Overall, these 620 findings emphasize that a primary function of local translation may be structural plasticity of 621 processes. The results are also consistent with the SynapTRAP method accurately measuring 622 classes of transcripts known or hypothesized to be locally translated. 623 624 Features of Locally Translated Transcripts 625 626 Next, we sought to determine whether there were sequence specific mechanisms of 627 localization by analyzing the candidate transcripts for novel motifs, as well as binding by known 628 regulators of local translation. First, we assessed length, GC content, and predicted secondary 629 structure stability across the transcript (Figure 9A). We found that coding sequence length and 630 GC content are substantially higher in the Local Translation Candidates than in the Somatic 631 Translation Candidates. Furthermore, in the untranslated regions (UTR), we found significant 632 differences in length, GC, and structure between Local Translation Candidates and Somatic 633 Translation Candidates in both the 3’UTR and in the 5’UTR. Longer sequences, particularly in 634 the 3’UTR, may permit a greater number of binding motifs for regulating stability, translation, 635 and localization of these transcripts (Andreassi and Riccio 2009). 636 637 One protein known to bind RNA and regulate translation, including local translation, is Fragile X 638 Mental Retardation Protein (FMRP). Its targets in the brain have previously been assessed 639 empirically (Darnell et al. 2011). If our method is indeed identifying locally translated mRNAs, 640 then it should enrich for FMRP targets. Indeed, the Local Translation Candidates are 641 significantly enriched in FMRP binding transcripts (Figure 9B, C). As both FMRP binding 642 transcripts (Ouwenga and Dougherty 2015) and Local Translation Candidates are biased 643 towards long transcripts, we confirmed that this finding was not driven by length or expression 644 biases (Figure 9C).We also analyzed the candidates for enriched motifs between samples. An 645 unbiased screen for motifs using MEME identified a G rich sequence reminiscent of a G-quartet 646 in the Local Translation Candidates compared to somatic controls. Indeed a full 31% of 647 candidates had this motif when a direct scan was conducted (X-squared=39.594, df=1, 648 p=3.127E-10). This is consistent with the increased GC content and secondary structure 649 detected above. The Somatic Translation candidates on the other hand were found to be 650 enriched for a poly(A) signal by MEME and direct scan (X-squared=4.471, df=1, p=0.03447), 651 though they do not have an increased number of poly(A) signals per transcript (Mann-Whitney 652 test, U=23841, p=0.3414). These two motifs were consistently significantly enriched whether 653 MEME was used to directly compare the two lists of local and somatic candidates 654 (discriminative mode), or to determine enrichment based solely on the nucleotide frequencies 655 in the input list (normal mode), which controls for any length bias. 656

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657 As G-quartets have been previously implicated in FMRP binding (Darnell et al. 2001), we next 658 examined the overlap between all G-quartet containing transcripts expressed in the brain, 659 FMRP binding mRNAs, and Local Translation candidates (Figure 9F). The FMRP set is enriched in 660 G-quartet containing transcripts (Fisher Exact Test, OR 2.2, p<3.1E-11), and 25% of the overlap 661 between the Local Translation Candidates and FMRP binding might be explained by the 662 presence of a G-quartet (Fisher Exact Test, OR 4.2, p<.0001). 663 664 The longer 3’UTRs and presence of consistent motifs also suggests that the Local Translation 665 Candidates might be under greater evolutionary pressure for careful regulation of their protein 666 levels in general. Indeed, examining the set of genes recently shown to be loss-of-function 667 intolerant in human populations (Lek et al. 2016) reveals that the Local Translation Candidates 668 are enriched in these evolutionarily constrained genes (Figure 9B). Previously we showed that 669 constrained genes are disproportionately expressed in the brain (Wells et al. 2015); however, 670 brain expression alone does not mediate the enrichment of evolutionarily constrained genes in 671 the Local Translation Candidates, as the neuronally expressed Somatic Translation Candidates 672 show no such enrichment (p=0.34). 673 674 Regulation by Alternative Splicing of Isoforms 675 676 An additional mechanism for regulating RNA localization could be alternative splicing of 677 isoforms. A recent study identified alternative splicing of the 3’UTR as a potential regulatory 678 mechanism for localizing transcripts to the processes of cultured neuroblastoma cells and 679 neurons, and highlighted the surprising result that distal last exons are disproportionately 680 found in processes (Taliaferro et al. 2016). Mixture of Isoforms Analysis (MISO) calculates the 681 percent spliced in (Ψ) of each pair of alternatively spliced isoforms in a sample and compares 682 these percentages across samples (ΔΨ). MISO identified dozens of neuronal transcripts that 683 may exhibit differential localization based on splicing (Figure 10A, 10-1, and 10-2) at a nominal 684 p<.05. Many of these transcripts encode for proteins with known synaptic functions, such as 685 Gria2 and Ncam1. Another example is Dtna, in which aligned reads are disproportionally 686 include the more distal last exon in the samples from cellular processes (SNF and SynapTRAP, 687 Figure 10B). While, in concordance with the in vitro study, the majority (75%) of significant 688 neurite-localized alternative last exons (ALE) demonstrated enrichment of the distal last exon, 689 we did not find that this enrichment was significantly biased towards neurites rather than 690 somatic candidates (Figure 10C, X-squared=1.102, df=1, p=0.2938). While on average, 691 neuronally expressed ALEs showed a slight increase in percent splicing of the distal last exon in 692 SNF compared to WCH, this difference was not significant (Figure 10C leftmost panel, one-tailed 693 t-test, t=-1.121, df=136, p=0.264). We did, however, detect a slight preference for distal ALEs to 694 be enriched on ribosomal bound mRNA as seen in the TRAP/WCH comparison (Figure 10C 695 middle panel, one-tailed t-test, t=-2.812, df=147, p<.01). 696 697 DISCUSSION 698 699 In this study we describe SynapTRAP, a method to harvest cell type-specific mRNA from 700 processes in vivo. We apply it to provide the first description of neurite localized and ribosome-

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701 bound transcripts in cortex, and identify sequence motifs enriched in these transcripts. 702 Translation at the synapse and in neural processes is important for synaptic formation and 703 plasticity. Understanding the specific mRNAs that are translated near the synapse could 704 uncover novel pathways of synaptic regulation. While fractionation based on density alone 705 yields a SNF that includes RNA from multiple cell types, combining it with SynapTRAP identified 706 locally translated mRNA in cell-specific projections. Analysis of the Local Translation Candidates 707 identified through SynapTRAP of cortical neurons highlighted the importance of cytoskeletal 708 regulation as well as differences in RNA sequence features that localize to neurites. This is 709 consistent with SynapTRAP successfully isolating locally translated mRNAs with shared 710 biological regulation, and indicates that transcripts locally translated in the cortex are enriched 711 for sequences under evolutionary pressure in humans. 712 713 Local Translation Measures for Intermixed Cell Types 714 715 Studies of diverse cell types in the brain have already shown cell-specific translational profiles 716 (Doyle et al. 2008), but changes in local translation across cell types have yet to be 717 systematically explored. Though substantial methodological differences preclude any 718 quantitative comparison, the Local Translation Candidates detected here from cortical neurites 719 significantly overlap with those discovered in multiple prior studies in hippocampus (Figure 5E), 720 including those generated by RNAseq and in-situ hybridization (Lein et al. 2007), though it did 721 not overlap with others (Kratz et al. 2014; Poon et al. 2006). This lack of consistently (and 722 amongst these studies themselves) may be due to methodological differences including in vitro 723 vs. in vivo, developmental time point, ribosome binding, statistical thresholds and approaches, 724 or differences between local translation of the CNS regions studied. 725 726 The ability of SynapTRAP to harvest processes from densely interwoven cell types expands the 727 number of cell types in which local translation can be studied in the CNS, beyond the prior work 728 limited to dendritic lamina. Moreover, as pointed out by Ainsley (Ainsley et al. 2014), cell types 729 that have been studied using dissection methods alone still harvests other cell bodies harvest 730 along with the neurite layers. Immunoblots of the SNF harvested by density fractionation show 731 depletion for nuclei of neurons and glia (NeuN and OLIG2: Figure 1C), indicating decreased 732 somatic presence in the samples. Coupling SynapTRAP with a variety of TRAP mouse lines 733 (Doyle et al. 2008; Dougherty et al. 2013; Dalal et al. 2013), and availability of Cre-dependent 734 reporters (Sanz et al. 2009; Zhou et al. 2013) should allow for comparative analysis of local 735 translation across a variety of cell types. We are particularly interested in comparative analysis 736 of neurons with markedly different dendritic morphologies. As Gene Ontology analysis of Local 737 Translation Candidates highlight local transcripts that are involved with regulating cell shape 738 and structure, some of the morphological differences across cell types could be due to 739 differences in regulation of local translation. 740 741 Opportunities for Further Development 742 743 The most significant opportunity for further development of this method would be in reducing 744 the background of the SynapTRAP/SNF comparison to the point that it approaches more typical

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745 TRAP/WCH comparisons. We do not think the increased background in SynapTRAP is driven by 746 low levels of eGFP-Rpl10a expression in glia because, if so, the TRAP/WCH comparisons would 747 show identical levels of background. Rather, there is either something about the physical 748 process of generating the SNF, or the nature of these distal and intermingled processes in the 749 brain that result in an increase in apparent relative levels of mRNA from other cell types being 750 captured. We speculate that the high level of background might be due to non-specific 751 interactions between RNA granules from neurons and glia post lysis, as these are formed by 752 very non-specific interactions between disordered domains in a variety of RNA binding proteins, 753 and can become very stable, even amyloid-like, under certain biophysical conditions (Lin et al. 754 2015). There were also substantially more reads from mitochondria and mitochondrial rRNA in 755 the SNF/SynapTRAP fractions. Because of this background, we deliberately focused only on the 756 most enriched transcripts for downstream analyses. These likely represent only a subset of all 757 transcripts that may contribute to translation in neurites. Thus, methodological improvements 758 may further increase the number of candidates detected with this approach. However, the 759 current approach has proven successful at detecting localized RNAs from a previously 760 inaccessible cell type. 761 762 Many different biochemical approaches for harvesting cellular processes exist. We selected this 763 density gradient technique because it has been shown to harvest viable synaptic structures that 764 are competent for translation in response to stimulation (Westmark et al. 2011); however, 765 alternative methods might allow for selection for other specialized forms of dendrites or 766 synapses (e.g. glomeruli in the cerebellum) (Viennot et al. 1991), or permit analysis of other 767 tissues where filters rather than density centrifugation may be preferred for generating an SNF 768 (e.g. Spinal cord) (Shinomura et al. 1999). Likewise, study of diseases that cause major changes 769 in density of the cellular processes may require optimization of the gradient for consistent 770 collection of the SNF across conditions. Nonetheless, the current protocol should be directly 771 applicable to cell types in cortex and other regions with similar physical properties in the CNS. 772 773 Our current method of harvesting GFP-tagged ribosomes in the presence of the elongation 774 inhibitor cycloheximide cannot distinguish if these ribosomes were actively translating. While 775 ribosome occupancy is a prerequisite for translation, culture studies have shown that 776 transcripts are transported in neurons while bound to ribosomes in a stalled state (Graber et al. 777 2013). Therefore, it would be of interest in future studies to combine SynapTRAP with 778 stimulation, run-off studies, or indeed even nucleotide resolution analyses of translation to 779 better distinguish to what extent our candidates are stalled and which protein species are being 780 produced. 781 782 Finally the presence of substantial glial RNA in the SNF prior to SynapTRAP (Figure 2) was also 783 intriguing. This indicated that RNA from some glial processes might be co-fractionating with 784 synaptoneurosomes, consistent with prior proteomic studies that identified glial derived 785 proteins in synaptic fractions (Pielot et al. 2012). Indeed, astrocytes have fine processes that 786 interact with synapses providing both trophic and homeostatic support, and microglial 787 processes show great motility in the CNS, apparently surveying and phagocytosing nearby 788 synapses (Perea, Navarrete, and Araque 2009; Schafer, Lehrman, and Stevens 2013). Using a

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789 variation of the SynapTRAP approach we have also now just shown that astrocytes also have 790 potential for local translation as well (Sakers et al. 2017). 791 792 Identification of Mechanisms of Regulating Local Translation 793 794 Downstream analysis of candidates identified by SynapTRAP can be analyzed in many ways 795 using bioinformatics. For example, the increased UTR length and secondary structure of Local 796 Translation Candidates is consistent with the region’s regulatory role. It will also be of great 797 interest in future investigations to determine whether transcript localization shows UTR- 798 regulated differences between cell types, and whether the evolutionary constraint on the 799 protein coding sequence for several Local Translation Candidates also extends to motifs in their 800 3’ UTRs. 801 802 Our studies are also consistent with prior models indicating that FMRP plays a role in regulating 803 local translation in the cortex, and that this might be mediated by interactions with G-quartet 804 motifs. In addition, differential splicing also likely regulates isoform enrichment on neurite 805 ribosomes for a set of transcripts, consistent with a recent study of axonal RNA (Shigeoka et al. 806 2016) and cultured neurites (Taliaferro et al. 2016), though whether the distal ALEs are 807 disproportionately enriched compared with somatic ribosomes is unclear from our data. This is 808 in contrast with prior work indicating a clear preference for distal last exons in process localized 809 transcripts (Taliaferro et al. 2016). However, this contrast could reflect differences between in 810 vivo and in vitro systems, relative phase of maturation of the processes (neurite outgrowth in 811 vitro compared to an age more associated with synaptic refinement in vivo), or differences in 812 cell types. Regardless, these findings suggest that different mechanisms are utilized to regulate 813 localization in different contexts, and both analyses support the importance of 3’ UTRs. 814 815 In addition to the G-quartet, we identified other 3’UTR motifs that may contribute to regulation 816 of local translation. Specifically, we identified an increased number of strong polyadenylation 817 signals in Somatic Translation Candidates. This suggests regulation of poly adenylation or 818 poly(A) tail length might contribute to RNA localization or ribosome binding, and will be an 819 interesting area for future exploration. Though further evidence for these specific models 820 awaits additional computational and functional studies, overall the presence of significant 821 motifs in the 3’UTRs supports the hypothesis that these regions of the transcript are particularly 822 important for regulation of location and translation. 823 824 Applications to Disease 825 826 As ASD-related syndromes such as fragile X syndrome and tuberous sclerosis have been 827 previously hypothesized as a diseases of altered translation at the synapse (Kelleher and Bear 828 2008), it is notable that the list of Local Translation Candidates significantly overlaps with FMRP 829 targets. The overlap of these two list is significant even after correction for length and 830 expression bias of transcripts (Ouwenga and Dougherty 2015), and a binding sequence for 831 FMRP is enriched in the Local Translation Candidates. This is consistent with the hypothesized 832 role for FMRP in regulation of locally translated transcripts during transport to neuronal

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833 processes (Darnell et al. 2011). This also suggests SynapTRAP could be a viable way to identify 834 perturbations in local translation across specific neuronal populations and in disease models in 835 the future. 836 837 Author Contributions 838 RO: Developed and conducted SynapTRAP, data analysis, validation studies, and writing of 839 manuscript 840 AL: Contributed to data analysis and writing of manuscript 841 AM: Conducted EM imaging. 842 DO: Contributed to data analysis 843 AD: Conducted STORM imaging 844 JD: Conceived of study, contributed to data analysis, and writing of manuscript. 845 846 FUNDING 847 This work was supported by the CDI (MD-II-2013-269), and NIH (R21DA038458, R21MH099798, 848 R01NS102272). Key technical support was provided by the Genome Technology Resource 849 Center at Washington University (supported by NIH grants P30 CA91842 and UL1TR000448). RO 850 was supported by T32 GM081739. JDD is a NARSAD investigator. 851 852 853 REFERENCES 854 Ainsley, Joshua A., Laurel Drane, Jonathan Jacobs, Kara A. Kittelberger, and Leon G. Reijmers. 855 2014. “Functionally Diverse Dendritic mRNAs Rapidly Associate with Ribosomes 856 Following a Novel Experience.” Nature Communications 5 (July). 857 doi:10.1038/ncomms5510. 858 Anders, Simon, Paul Theodor Pyl, and Wolfgang Huber. 2015. “HTSeq—a Python Framework to 859 Work with High-Throughput Sequencing Data.” Bioinformatics 31 (2): 166–69. 860 doi:10.1093/bioinformatics/btu638. 861 Andreassi, Catia, and Antonella Riccio. 2009. “To Localize or Not to Localize: mRNA Fate Is in 862 3ʹUTR Ends.” Trends in Cell Biology 19 (9): 465–74. doi:10.1016/j.tcb.2009.06.001. 863 Bailey, T. L., and C. Elkan. 1994. “Fitting a Mixture Model by Expectation Maximization to 864 Discover Motifs in Biopolymers.” Proceedings. International Conference on Intelligent 865 Systems for Molecular Biology 2: 28–36. 866 Bolger, Anthony M., Marc Lohse, and Bjoern Usadel. 2014. “Trimmomatic: A Flexible Trimmer 867 for Illumina Sequence Data.” Bioinformatics 30 (15): 2114–20. 868 doi:10.1093/bioinformatics/btu170. 869 Bright, Nicola J., David Carling, and Claire Thornton. 2008. “Investigating the Regulation of 870 Brain-Specific Kinases 1 and 2 by Phosphorylation.” The Journal of Biological Chemistry 871 283 (22): 14946–54. doi:10.1074/jbc.M710381200. 872 Burgin, K. E., M. N. Waxham, S. Rickling, S. A. Westgate, W. C. Mobley, and P. T. Kelly. 1990. “In 873 Situ Hybridization Histochemistry of Ca2+/Calmodulin-Dependent Protein Kinase in 874 Developing Rat Brain.” The Journal of Neuroscience 10 (6): 1788–98. 875 Cajigas, Iván J, Georgi Tushev, Tristan J Will, Susanne tom Dieck, Nicole Fuerst, and Erin M 876 Schuman. 2012. “The Local Transcriptome in the Synaptic Neuropil Revealed by Deep

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1050 Xu, Xiaoxiao, Alan B. Wells, David R. O’Brien, Arye Nehorai, and Joseph D. Dougherty. 2014. 1051 “Cell Type-Specific Expression Analysis to Identify Putative Cellular Mechanisms for 1052 Neurogenetic Disorders.” The Journal of Neuroscience 34 (4): 1420–31. 1053 doi:10.1523/JNEUROSCI.4488-13.2014. 1054 Yoshida, Hirotaka, and Michel Goedert. 2012. “Phosphorylation of Microtubule-Associated 1055 Protein Tau by AMPK-Related Kinases.” Journal of Neurochemistry 120 (1): 165–76. 1056 doi:10.1111/j.1471-4159.2011.07523.x. 1057 Zeisel, Amit, Ana B. Muñoz-Manchado, Simone Codeluppi, Peter Lönnerberg, Gioele La Manno, 1058 Anna Juréus, Sueli Marques, et al. 2015. “Cell Types in the Mouse Cortex and 1059 Hippocampus Revealed by Single-Cell RNA-Seq.” Science 347 (6226): 1138–42. 1060 doi:10.1126/science.aaa1934. 1061 Zhang, Ye, Kenian Chen, Steven A. Sloan, Mariko L. Bennett, Anja R. Scholze, Sean O’Keeffe, 1062 Hemali P. Phatnani, et al. 2014. “An RNA-Sequencing Transcriptome and Splicing 1063 Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex.” The Journal of 1064 Neuroscience 34 (36): 11929–47. doi:10.1523/JNEUROSCI.1860-14.2014. 1065 Zhong, Jun, Theresa Zhang, and Lisa M. Bloch. 2006. “Dendritic mRNAs Encode Diversified 1066 Functionalities in Hippocampal Pyramidal Neurons.” BMC Neuroscience 7 (1): 17. 1067 doi:10.1186/1471-2202-7-17. 1068 Zhou, Pingzhu, Yijing Zhang, Qing Ma, Fei Gu, Daniel S. Day, Aibin He, Bin Zhou, et al. 2013. 1069 “Interrogating Translational Efficiency and Lineage-Specific Transcriptomes Using 1070 Ribosome Affinity Purification.” Proceedings of the National Academy of Sciences 110 1071 (38): 15395–400. doi:10.1073/pnas.1304124110. 1072 1073 FIGURE LEGENDS 1074 1075 Figure 1: Synaptoneurosomal Fractions 1076 A) Electromicrograph of a synaptoneurosome, including presynaptic vesicles (black arrow) and 1077 postsynaptic density (white arrow), in the synaptoneurosomal fraction (SNF). B) Representative 1078 immunoblots of a total forebrain homogenate (Hom), supernatant that is the input to the 1079 discontinuous column (Load) after cell lysis, the membrane fraction (Mem), and the SNF. C) 1080 Quantification of three replicate immunoblots reveals the expected relative enrichment of PSD- 1081 95 and depletion of OLIG2 and NeuN in the SNF relative to the homogenate and other fractions. 1082 eGFP/Rpl10a is also robustly detected. Each sample is normalized to Ponceau stain and 1083 corresponding homogenate. Error bars +/- s.e.m. Analysis by T-test with 4df of SNF samples 1084 compared to loading controls: GFP t=6.46 p=0.003; PSD95 t=12.67 p=0.0002; OLIG2 t=1.72 1085 p=.16; NeuN t=6.38 p= 0.0031. *p<.05. 1086 1087 Figure 2: Cell-type Enrichment in the SNF 1088 A) CSEA analysis using TRAP reference gene sets of top 150 SNF transcripts (full list Figure 2-1) 1089 indicates significant enrichment of mRNAs expressed in adult cortical neurons (Pnoc, Layer 6b), 1090 but also some mRNAs expressed in glial cell types (Astrocytes, and the Etv1 line which 1091 measured both pyramidal neurons and putative microglia). Polygon size is scaled to number of 1092 genes enriched in each cell type, from least stringent threshold (outermost polygon), to most 1093 stringent threshold (innermost polygon) for identifying cell type specific genes. Color indicates

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1094 significance of overlap per provided scale bar. Grey indicates p>.05. B) Comparison of the 100 1095 most enriched SNF transcripts to the Barres lab P21 RNA-sequencing dataset also reveals that 1096 while >50% of transcript are found in multiple neural cell types (including neuron), a substantial 1097 number of transcripts are likely derived specifically from glial cell populations. C) CSEA using 1098 single-cell reference gene sets on the top 100 SNF transcripts confirms contribution of both 1099 neuronal (Int1,2,6,11,12,14,16, Ca1Pyr1, CA1PyrInt, S1PyrL6) and non-neuronal (e.g. Oligo1, 1100 Astro1,2, Pvm1,2, Mgl1, Vsmc) sources to SNF transcripts. 1101 1102 Figure 3: eGFP/Rpl10a is Found in Neurites Across Multiple bacTRAP Lines 1103 A) Anti-GFP immunohistochemistry (IHC) on Hypocretinergic line Hcrt-eGFP/Rpl10a shows 1104 labeling of neuronal cell bodies and large caliber fibers (presumptive dendrites) in the lateral 1105 hypothalamus. B) Anti-GFP immunofluorescence of Pcp2-eGFP/Rpl10a reveals eGFP/Rpl10a is 1106 found in discrete puncta along the dendrites in the Purkinje cell layer of the cerebellum (scale 1107 bars, 5 uM). C) Serotonergic line Slc6a4-eGFP/Rpl10a shows labeling of neuronal cell bodies and 1108 large caliber fibers (presumptive dendrites) in the Raphe nuclei. D) Immunofluorescence of 1109 Slc6a4-eGFP/Rpl10a reveals punctate labeling in large caliber processes extending from cell 1110 bodies. E) STORM microscopy reveals these GFP positive puncta (green) within the processes 1111 are proximal to glutamatergic synapses, as defined by apposition of bassoon (red) and homer 1112 (blue) (scale bars 200 nM). 1113 1114 Figure 4: SynapTRAP Method for Isolating Localized Translation in Neuronal Projections 1115 A) Workflow of RNA isolation from cortical samples and B) the four RNA samples collected by 1116 the method. Representative RNA Bioanalyzer traces demonstrate the harvest of intact 80s 1117 ribosomes, indicated by both large (28s) and small subunit (18s) ribosomal capture. C) Percent 1118 of reads that map to exons, introns, and intergenic regions. Greater than 86% reads mapped to 1119 exons across all sample types. 1120 1121 Figure 5: Comparative Analysis to Define Local Translation Candidates for Cortical Neurons 1122 A) Distributions of Log2 fold changes for three pairwise comparisons (Figure 5-1). B) 1123 Distributions of Log2 fold changes for same comparisons highlighting those implicated in a prior 1124 study (Dougherty et al. 2012) as neuron or astrocyte derived. C) The candidate lists were 1125 generated by the overlap of differentially detected transcripts in the samples. Local Translation 1126 Candidates (Figure 5-2) were defined as those that were enriched in the SNF from the WCH (in 1127 cell processes) and also enriched in the SynapTRAP sample above the SNF (neuronal). Ten 1128 representative transcripts shown from the Local Translation Candidates. D) Somatic Translation 1129 Candidates (Figure 5-3) were defined as those that were depleted on the column compared to 1130 the WCH (not in processes) and enriched in the Whole Cell TRAP above the WCH (neuronal). 1131 Ten representative transcripts from the Somatic Translation Candidates. E) Venn diagram of 1132 previous studies that overlap with the Local Translation Candidates Fisher’s Exact Test Cajigas 1133 p=0.01345 CI=[1.12, Inf] OR=1.56; Ainsley p=2.692E-CI=[112.59, Inf] OR= 3.5; Lein p=0.000128 1134 CI=[4.30, Inf] OR=11.6. 1135 1136 Figure 6: Validation by qPCR

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1137 qPCR of the whole cell homogenate (WCH), the traditional method of harvesting neurites (SNF), 1138 and the SynapTRAP sample of A) Local Translation Candidates, B) Somatic Translation 1139 Candidates, and C) RNA previously described as localizing to neurites(Camk2a, Shank3) or the 1140 soma (Sox10). *p<.05, ** *p<.01, ***p<.001 Statistical testing was determined by ANOVA (bar): 1141 Camk2a F= 5.5 p= .048; Shank3 F=4.2 p=.076; Sox10 F=4.18 p=0.078; Armc6 F=4.15 p= 0.079; 1142 Cpne6 F=3.48 p=0.11; Gsn F=134.9 p= 3.3E-5; Myhl14 F= 35.8 p=0.00083; Neurl1a F=12.6 1143 p=0.009; Nsmf F=11.8 p=0.01; Tesc F=8.01 p= 0.02; and Unpaired Two Tailed T-test between 1144 WCH and SynapTRAP samples with 4df Camk2a t=4.23 p=0.01; Shank3 t=3.98 p=0.016; Sox10 1145 t=2.29 p=0.08; Armc6 t=4.16 p=0.04; Cpne6 t=3.29 p=0.03; Gsn t=19.35 p=0.0001; Myhl14 1146 t=10.2 p=0.0005; Neurl1a t=5.66 p=0.0048; Nsmf t=5.47 p=0.0054; Tesc t=4.17 p=0.0141. 1147 1148 Figure 7: Validation by ISH 1149 A) In situ Hybridizations of SynapTRAP candidates (red), neuronal process (green) and DAPI 1150 (blue) on 20uM brain slices from Tg(Thy1-EGFP)MJrs/J mice. Arrows indicate an example ISH 1151 signal overlapping with a neurite. B) Quantification of the number of puncta overlapping with 1152 GFP labeled processes divided by the area of the image labeled with GFP for each probe. 1153 *p<.05, **p<.01, ***p<.001, unpaired T-Tests. Error bars +/- s.e.m. Camk2a n=46 76df t=4.06 1154 p=0.0001; Snph n=21 51df t=2.3 p= 0.0256; Nsmf n=46 76df t=2.79 p=.0067; Mllt6 n=25 55df 1155 t=3.52 p=0.0009; Hist3hba n=19 49df t=1.13 p=0.6231; No probe n=32. 1156 1157 Figure 8: Gene Ontologies Analysis of Candidates Lists 1158 Hierarchical network representation of Molecular Function, Cellular Component, and Biological 1159 Process GO analysis for A) Local Translation Candidates and B) Somatic Translation Candidates 1160 with a p-value cutoff at p=.00001. Circles are scaled to size of each GO gene list. Color indicates 1161 p-value per scale bar. GO categories are ordered hierarchically from broadest (top) to most 1162 specific. C) Ten significant categories from GO analysis of the Local Translation Candidates 1163 (Figure 8-1) against the background of genes with transcripts >2CPM in the WCH. D) Ten 1164 significant categories from GO analysis of Somatic Translation Candidates (Figure 8-2) against 1165 the background of genes with transcripts >2CPM in the WCH. *Passes Benjamini-Hochberg 1166 correction at FDR<.05. 1167 1168 Figure 9: Overrepresented Sequence Features in Local Translation Candidates 1169 A) Local Translation Candidates have longer 5’UTRs, 3’UTRs, and coding regions (CDS) than 1170 somatic controls. Welch’s 2 tailed t-test with Benjamini-Hochberg (BH) correction 5’UTR t=2.10, 1171 df=250.57, p=0.037; CDS t=5.35, df=255.87, p=7.26E-07; 3’UTR t=3.74, df=254.46, p=3.66E-04. 1172 Local Translation Candidates are higher in 5’UTR, 3’UTR, and CDS GC content than somatic 1173 controls. Welch’s 2 tailed t-test with BH correction 5’UTR t=3.13, df=278.96, p=2.61E-03; CDS 1174 t=10.51, df=266.92, p=5.94E-21; 3’UTR t=5.30, df=234.98, p=7.26E-07. Local Translation 1175 Candidates have more stable UTR secondary structures than somatic controls. Welch’s 2 tailed 1176 t-test with BH correction 5’UTR t=2.76, df=266.31 p=6.98E-03; 3’UTR t = 4.08, df=250.42, 1177 p=1.23E-04. ***p<.001, **p<.01, *p<.05, BH corrected. B) Local Translation Candidates are 1178 enriched in constrained genes and targets of FMRP; odds ratios and 95% confidence intervals 1179 shown. One-tailed Fisher’s exact test, constrained genes p<8E-8; FMRP targets p<10E-16; C) 1180 Distribution of p-values after Fisher Exact Testing of 1000 random gene lists sampled to match

27

1181 the FMRP targets on length and expression biases. P-value of true target list indicated with red 1182 arrow. D) Top motif discovered in 3’UTRs of Local Translation Candidates (E-value = 8.4E-090, 1183 detected in 131/145 sequences). Sequence matches canonical G-quartet motif. E) Top motif 1184 discovered in 3’UTRs of Somatic Translation Candidates (E-value = 3.9e-189, detected in 1185 310/310 sequences). This matches the canonical poly(A) signal. F) Overlap of Local Translation 1186 Candidates, G-quartet containing sequences, and FMRP targets. 1187 1188 Figure 10: Regulation of Transcript Localization by Alternative Splicing 1189 A) Counts of neurite (Figure 10-1) and somatic (Figure 10-2) localized splice isoforms for each 1190 alternative splicing event type. Events are summarized by whether the localized isoform was 1191 the exclusion/distal (red) or inclusion/proximal (green) isoform. B) Distribution of RNA-seq 1192 reads across transcript Dtna, an example of a transcript containing a neurite-enriched distal last 1193 exon (p<.05, permuted t-test). One representative sample from WCH, SNF, and SynapTRAP 1194 groups are shown. Right panels: percent spliced in (Ψ) with 95% confidence intervals for each 1195 sample shown. C) Distribution of ΔΨ values for pairwise comparisons of the four samples. 1196 1197 Figure 2-1: Table of Transcripts Enriched in SNF 1198 Gene_id:Ensembl gene ID. gene_name: corresponding gene symbol. Chr: chromosome. logFC: 1199 log base 2 fold change for SNF compared to WCH. logCPM, LR, PValue, FDR: EdgeR output for 1200 the comparison. 1201 1202 Figure 5-1: Table of All Processed Data for Each Sample Comparison 1203 Column names as above. Mean CPM for each group provided across all detectable transcripts, 1204 as well as complete EdgeR results from 3 comparisons. bySynapTRAP refers to EdgeR results for 1205 SynapTRAP>SNF comparison. byColumn refers to SNF>WCH comparison. byTRAP refers to 1206 TRAP>WCH comparison. 1207 1208 Figure 5-2: Table of Local Translation Candidates 1209 Candidates meeting criteria described in figure 4C. Column names as above. bySynapTRAP 1210 refers to EdgeR results for SynapTRAP>SNF comparison. byColumn refers to SNF>WCH 1211 comparison. 1212 1213 Figure 5-3: Table of Somatic Translation Candidates 1214 Candidates meeting criteria described in figure 4D. Column names as above. byTRAP refers to 1215 EdgeR results for TRAP>WCH comparison. byColumn refers to WCH>SNF comparison. 1216 1217 Figure 8-1: Table of GO Analysis of Local Translation Candidates 1218 Gene Ontology Analysis of the Local Translation Candidates. 1219 1220 Figure 8-2: Table of GO Analysis of Somatic Translation Candidates 1221 Gene Ontology Analysis of the Somatic Translation Candidates.

28

1222 1223 Figure 10-1: Table of Neurite-Enriched Alternative Splicing Events 1224 Alternative splicing events enriched in neurites, identified as described (Materials and 1225 Methods). ΔΨ (denoted dPSI here), p-value, and Benjamini-Hochberg FDR for SNF>WCH and 1226 SynapTRAP>SNF comparisons. 1227 1228 Figure 10-2: Table of Soma-enriched Alternative Splicing Events 1229 Alternative splicing events enriched in soma, identified as described (Materials and Methods). 1230 ΔΨ (denoted dPSI here), p-value, and Benjamini-Hochberg FDR for SNFWCH 1231 comparisons. 1232 1233 1234

29 A)B)HomLoad Mem SNF C) 3.5 PSD95 * PSD95 3 Neun GFP Neun 2.5 Olig2

eGFP 2 *

Olig2 1.5

1 Total Protein (Intensity/Ponceau)/Homogenate 0.5 *

0 Hom Load Mem SNF D1+ Spiny Layer 5b A) Layer 6 Neurons B) Neurons Neurons Cort+ Neurons Cones Unipolar & Cortex D2+ Spiny Cortex Bergmann Glia Striatum Neurons Cortex Stellate & Rods Basket Neurons Cerebellum Layer 5A & Retina Immune Cells Striatum

Cerebellum Percent of Genes Oligodendrocytes Retina Granule Neurons Cortex

Oligodendrocyte 01020304050 Common Endothelial Oligo Progenitors Astrocytes to Multiple Microglia Cortex Cells Precursor Cerebellum Cell-types Cell Purkinje Neurons Golgi Neurons Oligodendrocytes

CholinergicCortex Astrocytes Cholinergic Neurons Cerebellum Neurons Cerebellum Hypocretinergic Neurons Cerebellum Cholinergic Basal Habenula Neurons Cortex Forebrain Hypothalmus Astrocytes Cholinergic Bergmann Glia & Cholinergic Motor Neurons Oligodendrocytes Neurons Pnoc+ Serotonergic Spinal cord Neurons Neurons

Striatum Brain stem Cortex Brain stem Cerebellum

0.05 0.0375 0.025 0.0125 0 | | | | | Cerebellum p−values

Int3 Int5 Int13 Int15 Int2 Int4 Int6 C) Int8 Int1 S1PyrL23 S1PyrL6 Int9 S1PyrL4 S1PyrL5

CA1PyrInt Int7

CA1Pyr1 Choroid Oligo2 Oligo1 Epend Int14 Int10 Oligo4

Int16

Vend2 Int11 Vend1 Oligo5 Peric Int12

Vsmc Astro1 Oligo3 Mgl2 Oligo6 Mgl1 Astro2 S1PyrDL ClauPyr

Pvm2 CA1Pyr2 Pvm1 S1PyrL5a S1PyrL6b CA2Pyr2 0.05 0.0375 0.025 0.0125 0 | | | | | SubPyr p−values A)

B)

C)

D)

E) A) B) C) 1. Whole Cell Homogenate (WCH) Exon Cortical Dissection 86.7% 100 Intron 50 4.9% Intergenic 0 8.4% 25 200 1000 4000 [nts] Y

Y 2. Whole Cell TRAP Y Y

Y Exon Membranes Y 90.6% Intron 100 4.6% Anti-eGFP Intergenic Affinity Purification 0 4.8%

Fluorescence25 Fluorescence 200 1000 4000 [nts] Myelin 3. Synaptoneurosomal Fraction (SNF) Exon 90.3% 50 Intron SN Fraction 4.1% 0 Intergenic 5.6% Y Fluorescence 25 200 1000 4000 [nts] Y

Y Y Organelles Y 4. SynapTRAP

Y Exon 93% Anti-eGFP 100 Intron 4.6% Affinity Purification 0 Intergenic

Fluorescence 2.4% 25 200 1000 4000 [nts] A) SNF/WCH Distribution TRAP/WCH Distribution SynapTRAP/SNF Distribution  'HQVLW\   0 0.1 0.3 0.5 0 0 0.1 0.3 0.5 B) í í í 0  í í í 0  í í í 0  0.8 JOLDO 0.8 JOLDO 0.8 JOLDO QHXURQDO QHXURQDO QHXURQDO 'HQVLW\          0 0 0 í í í 0  í í í 0  ííí0  /RJFROG&KDQJH /RJFROG&KDQJH /RJFROG&KDQJH

C) E) Enriched by Local &DJLMDV /HLQ Enriched by TRAP Cellular Translation /RFDO SynapTRAP > SNF Fractionation $LQVOH\ Candidates &DQGLGDWHV  1245 SNF > WCH 5 153 3408  18 0 Enrichment by  0 15  Enrichment by TRAP Cellular Fractionation Local Translation Candidates SynapTRAP > SNF SNF > WCH 5 0 387 0 Gene ID Gene Name LogFC Pvalue FDR LogFC Pvalue FDR 17 ENSMUSG00000022602 Arc 1.23 8.63E-04 2.36E-02 0.80 2.81E-02 7.00E-02  ENSMUSG00000035390 Brsk1 1.42 8.42E-07 2.51E-04 0.96 1.21E-03 5.40E-03 ENSMUSG00000024617 Camk2a 1.35 4.80E-03 7.04E-02 1.14 1.71E-02 4.75E-02 ENSMUSG00000026879 Gsn 1.58 2.73E-02 1.82E-01 2.19 2.80E-03 1.09E-02 ENSMUSG00000038437 Mllt6 1.39 6.66E-05 4.87E-03 1.38 7.91E-05 5.22E-04 ENSMUSG00000030739 Myh14 1.96 2.80E-05 2.65E-03 1.30 6.53E-03 2.18E-02 ENSMUSG00000006435 Neurl1a 1.56 2.94E-06 6.27E-04 0.99 3.08E-03 1.17E-02 ENSMUSG00000006476 Nsmf 1.44 3.06E-06 6.33E-04 1.13 2.19E-04 1.26E-03 ENSMUSG00000022623 Shank3 1.50 1.42E-05 1.63E-03 1.36 7.69E-05 5.09E-04 ENSMUSG00000027457 Snph 1.25 1.12E-05 1.41E-03 1.05 2.12E-04 1.22E-03

D)

Depleted Somatic by Cellular Enriched by TRAP Translation Fractionation TRAP > WCH Candidates WCH > SNF 2662 315 2728

Depletion by Enrichment by TRAP Cellular Fractionation Somatic Translation Candidates TRAP > WCH WCH >SNF

GeneID GeneName LogFC Pvalue FDR LogFC Pvalue FDR ENSMUSG00000057649 Brd9 0.81 2.86E-03 1.64E-02 -0.64 2.94E-02 7.25E-02 ENSMUSG00000022212 Cpne6 1.38 3.09E-10 7.05E-08 -0.69 2.46E-03 9.78E-03 ENSMUSG00000024560 Cxxc1 0.84 5.55E-04 4.78E-03 -0.79 4.36E-03 1.57E-02 ENSMUSG00000056895 Hist3h2ba 1.12 2.13E-03 1.31E-02 -0.92 4.54E-02 1.02E-01 ENSMUSG00000003500 Impdh1 1.04 2.75E-05 4.86E-04 -0.83 5.37E-03 1.85E-02 ENSMUSG00000046432 Ngfrap1 1.25 3.16E-08 2.60E-06 -0.71 2.37E-03 9.48E-03 ENSMUSG00000021645 Smn1 0.98 3.70E-03 1.98E-02 -1.53 1.33E-03 5.83E-03 ENSMUSG00000052293 Taf9 1.36 1.49E-06 5.15E-05 -0.90 5.24E-03 1.82E-02 ENSMUSG00000029359 Tesc 1.51 8.29E-08 5.56E-06 -0.80 1.14E-02 3.43E-02 ENSMUSG00000036989 Trim3 0.70 3.08E-03 1.73E-02 -0.76 3.03E-03 1.16E-02 A) Local Translation Candidates

Gsn Neurl1a Nsmf Myh14

−3 *** ** * *** *** ** ** −7 −6.5 −1.5 ***

−4

−7.0 −2.0 −8

−5 −7.5 −2.5 −9

−6

Abundance Change Compared to βactin WCH SNF SynapTRAP WCH SNF SynapTRAP WCH SNF SynapTRAP WCH SNF SynapTRAP

B) Somatic Translation Candidates C) Controls

Cpne6 Tesc Camk2a Shank3 Sox10

* * −5.5 1.0 −5 −6.0 * −4

0.5 −6.0 −6.5 −6 * 0.0 * −5 −6.5 −7.0 * −7 −0.5

−7.5 −7.0 Abundance Change Compared to βactin −8 −6

Abundance Change Compared to βactin −1.0 WCH SNF SynapTRAP WCH SNF SynapTRAP WCH SNF SynapTRAP WCH SNF SynapTRAP WCH SNF SynapTRAP A) B) 100 * 90 80 70 60 ** 50 *** 40 30 THY1GFP 20 *** Camk2a ISH 10 Number of Puncta per Area GFP Number of Puncta per 0 Camk2a Snph Nsmf Mllt6 Hist3h2ba No Probe

THY1GFP Snph ISH

THY1GFP Nsmf ISH

THY1GFP Mllt6 ISH

THY1GFP Hist3h2ba ISH

THY1GFP No Probe ISH A) GO Analysis of Local Translation Candidates B) GO Analysis of Somatic Translation Candidates

cellular cellular component organelle component cell cell part cell part projection intracellular neuron projection organelle cell organelle intracellular cell part membrane intracellular macro intracellular non-membrane- part molecular part bounded organelle organelle complex cell ribonucleo intracellular non-membrane-- part projection protein complex intracellular membrane- organelle part bounded organellee membrane plasma signal protein organelle bounded part membrane recognition organelle intracellular cell leading cytoplasm complex edge particle organelle plasma intracellular cytoskeletal cytoplasm signal recognition non-membrane- - cell ruffle membrane part particle, ER targeting bounded intracellular fraction part cytoskeleton organelle membrane-bounded intracellular cytoplasmic intracellular organelle non-membrane- synapse organelle cell junction part cytoskeleton bounded organelle part cytoskeletal part molecular function cytosol nucleus actin cytoskeleton molecular function catalytic binding activity

ion catalytic transferase binding nucleoside binding activity ion binding binding nucleotide activity transferring nucleotide binding phospho- phosphorus- binding purine transferase containing groups cation hydrolase nucleoside activity, alcohol ribonucleotide protein binding ribo- activity cation binding binding group as binding binding nucleotide kinase purine purine acceptorptor hydrolase activity, binding activity nucleotide nucleoside nucleotide protein acting on acid anhydrides adenyl binding binding metal ion binding kinase activity purine nucleotide purine nucleoside binding protein ribonucleotide hydrolase activity, binding purine binding binding protein binding acting on acid anhydrides, ribonucleotidee serine/threonine adenyl nucleotide in phosphorus-containing anhydrides metal ion binding binding nucleic acid adenyl kinase activity adenyl binding binding pyrophosphatase ribonucleotide cytoskeletal ribonucleotide activity binding protein binding binding ATP RNA nucleoside- binding ATP binding actin binding binding triphosphatase activity

cellular localization component macromolecule biological biological organization localization process biological process cellular regulation localizationn protein biological establishment of regulation regulation of localization cellular establishment of localization regulation of organelle biological process process cellular localization in cell biological process metabolic organization cellular macromolecule multicellular metabolic process localization regulation of organismal process process cellular component actin filament- process transport developmental biosynthetic organization cellular protein regulation of cellular based process intracellular localization process process small molecule transport component organization developmental cellular regulation of cytoskeleton metabolic process organelle intracellular establishment multicellular process metabolic nitrogen anatomical structure organization organization protein of protein localization organismal primary process compound morphogenesis cellular transport development macromolecule mmetabolic process nitrogen metabolic metabolic protein regulation of anatomical phosphorus metabolic process compound protein process process targeting cellular process structure metabolic gene metabolic process transport regulation of development process expression primary protein targeting protein cellular biological macromolecule actin cytoskeleton metabolic to membrane metabolic cellular macromolecule biosynthetic quality metabolic organization process process metabolic process process nucleobase, nucleoside, nucleotide and system process phosphate regulation of cell regulation of macro cellular nucleic acid metabolic process development metabolic morphogenesis cellular metabolic molecule macromolecule process protein cellular process biosynthetic biosynthetic regulation of metabolic process macromolecule regulation of primary regulation of nucleic acid cell shape metabolic process metabolic process macromolecule metabolic process Pvalue protein modification regulation Pvalue of cellular metabolic process 1x10-4 >1x10-9 process macromolecule phosphorylation 1x10-4 >1x10-9 metabolic modification regulation of process regulation of transcription gene expression post-translational protein amino acid cellular process protein modification phosphorylation

C) D) GO:0003779 Actin binding* GO:0005737 Cytoplasm* GO:0042995 Cell projection* GO:0005654 Nucleoplasm* GO:0008360 Regulation of cell shape* GO:0016571 Histone methylation GO:0045202 Synapse* GO:0005856 Cytoskeleton* GO:0014069 Postsynaptic density* GO:0006479 Protein methylation* GO:0005856 Cytoskeleton* GO:0005874 Microtubule* GO:0043197 Dendritic spine* GO:0005634 Nucleus GO:0007416 Synapse assembly GO:0032259 Methylation GO:0030424 Axon* GO:0008168 Methyltransferase activity GO:0030425 Dendrite GO:0044822 Poly(A) RNA binding 024681012 012345678 Negative Log (Pvalue) 10 Negative Log10(Pvalue) A) GC Content Sequence Length RNA Structure Stability * *** 0.8 ** 100 ***

12 0.6 80 *** *** *** ***

0.4

Percent 60 8 Log2(length) Stability Index

0.2 40 4 20 0.0 Somatic Local Somatic Local Somatic Local Somatic Local Somatic Local Somatic Local Somatic Local Somatic Local 5’UTRCDS 3’UTR 5’UTRCDS 3’UTR 5’UTR 3’UTR

Distribution of P−values B) C)

Soma 0.15 Genes Local Constrained Constrained 0.10 Gene Set Soma Density FMRP Targets Local 0.05 0 −20 −15 −10 −5 0 246 p−value(Log10) Odds Ratio

D) F) FMRP Targetsg Local Candidates

33 602 94

13 E) 86 13

718

G Quartet A) Neurite Localized Isoforms Somatic Localized Isoforms Alternative 5’ Alternative 5’ Splice Site (A5SS) Splice Site (A5SS) Alternative Alternative First Exon (AFE) First Exon (AFE) Alternative Alternative Last Exon (ALE) Last Exon (ALE) Mutually Exclusive Mutually Exclusive Exon (MXE) Exon (MXE) Isoform Retained Isoform Retained Exclusive/Distal Intron (RI) Intron (RI) Exclusive/Distal Inclusive/Proximal Inclusive/Proximal Skipped Skipped Exon (SE) Exon 0 5 10 15 0 5 10 15 20 Count Count B) Dtna 2.5 1.7 =0.13 (RPKM)

10 0.8 [0.09, 0.18] 0

2.5 =0.39 1.7

(RPKM) [0.35, 0.42]

10 0.8

Log 0

2.5 =0.10 1.7

(RPKM) Log [0.08, 0.13]

10 0.8 0 Log

MISO 23791722 23797816 23811737 23818204

Distal Exon

Proximal Exon

C) SNF and WCH TRAP and WCH SynapTRAP and SNF

0.2 *

dPSI 0.0

−0.2

−0.4 A5SS AFE ALE MXE RI SE A5SS AFE ALE MXE RI SE A5SS AFE ALE MXE RI SE