XB-ART-42378J Neurosci November 17, 2010; 30 (46): 15464-78.
Show Gene links Show Anatomy links
Subcellular profiling reveals distinct and developmentally regulated repertoire of growth cone mRNAs.
Cue-directed axon guidance depends partly on local translation in growth cones. Many mRNA transcripts are known to reside in developing axons, yet little is known about their subcellular distribution or, specifically, which transcripts are in growth cones. Here laser capture microdissection (LCM) was used to isolate the growth cones of retinal ganglion cell (RGC) axons of two vertebrate species, mouse and Xenopus, coupled with unbiased genomewide microarray profiling. An unexpectedly large pool of mRNAs defined predominant pathways in protein synthesis, oxidative phosphorylation, cancer, neurological disease, and signaling. Comparative profiling of "young" (pathfinding) versus "old" (target-arriving) Xenopus growth cones revealed that the number and complexity of transcripts increases dramatically with age. Many presynaptic protein mRNAs are present exclusively in old growth cones, suggesting that functionally related sets of mRNAs are targeted to growth cones in a developmentally regulated way. Remarkably, a subset of mRNAs was significantly enriched in the growth cone compared with the axon compartment, indicating that mechanisms exist to localize mRNAs selectively to the growth cone. Furthermore, some receptor transcripts (e.g., EphB4), present exclusively in old growth cones, were equally abundant in young and old cell bodies, indicating that RNA trafficking from the soma is developmentally regulated. Our findings show that the mRNA repertoire in growth cones is regulated dynamically with age and suggest that mRNA localization is tailored to match the functional demands of the growing axon tip as it transforms into the presynaptic terminal.
PubMed ID: 21084603
PMC ID: PMC3683943
Article link: J Neurosci
Species referenced: Xenopus laevis
Genes referenced: actr2 arpc1a coro1a coro1c efna3 efnb2 eif5a ephb4 fgf1 fgfr2 fscn1 fth1.1 h3-5 kif1a pfn2 ppia ppp1r9a prph rhoa rps13 rpsa septin5 sf3a2 slc25a5 slc7a5 sntb2 tmsb4x tuba1c tuba1cl.1 ubc vps4b
GEO Series: GSE25166: NCBI
Article Images: [+] show captions
|Figure 1. LCM of retinal growth cones. A , Schematic representation of an RGC showing the growth cone selected for LCM at 10× magnification. Before ( B ) and after ( C ) LCM micrographs of FM1-43 labeled Xenopus stage 32 retinal growth cones cultured on PET membrane slides. Scale bar, 150 μm.|
|Figure 2. Microarray and IPA of growth cone transcripts. A , Xenopus stage 32 growth cone mRNAs identified by microarray analysis were manually classified into different functional categories based on NCBI Gene tool. In total, 958 transcripts were identified, of which 444 have a known function. The pie chart represents the classification of these 444 mRNAs. B , IPA reveals the top 20 most enriched biological functions represented by the 444 growth cone mRNAs. p < 0.05. C , Table of significant biological pathways involving growth cone mRNAs generated by IPA.|
|Figure 3. Q-PCR validation demonstrates inter-platform reproducibility. Scatter plot representation of log GC-RMA intensity versus average CT value of all 38 transcripts tested for validation of the microarray data by Q-PCR ( A ). Inverse correlation between average CT value and GC-RMA intensity is shown in greater detail for 20 (of the 38) transcripts that had relatively high microarray signal intensities. These 20 transcripts also showed a tight correlation between input and PCR amplification (for details, see Materials and Methods) ( B ). CT values represent the number of cycles at which threshold was passed (thus inversely correlate to expression level), and error bars represent ±SEM.|
|Figure 4. FISH validation of mRNAs in growth cones. A , Quantitative FISH (see Materials and Methods) was performed on stage 32 Xenopus growth cones to obtain the mean pixel (fluorescence) intensity/unit area of antisense signal. Corresponding sense probes were used as controls for signal specificity, and all antisense mean pixel intensity/unit area were normalized to the sense control. The mRNAs chosen sample broadly across the different functional categories identified by the microarray analysis. Transcripts validated include ephrin A3 ( C ) (secreted and ECM category); eIF5A ( D ), Ribosomal protein S13 ( E ), Cyclophilin A ( F ), and Splicing factor 3A ( G ) (protein synthesis and translation category); Ubiquitin C ( H ) (protein degradation and apoptosis category); Histone H3 ( I ) (nuclear category); Solute carrier member 25 ( J ) and Ferritin ( K ) (metabolic/glycolytic category); Thymosin β4 peptide ( L ) and α-tubulin ( M ) (cytoskeletal/motor category); and 67 kDa Ribosomal protein/Laminin receptor 1 ( N ) and Vacuolar sorting protein 4A ( O ) (transmembrane/cell surface receptor and membrane trafficking categories, respectively). GAPDH mRNA was absent in growth cones and served as a negative control ( B ). Scale bar, 5 μm. Insets in boxed areas at higher magnification show differences in granule size and density (scale bar in E inset, 0.5 μm). [Xenbase curators note: accession number given for panel O matches sntb2 gene in NCBI/Genbank- curated as this gene here)|
|Figure 5. Growth cone mRNA repertoire is developmentally regulated: transcript number and complexity increases in 24 h. A , Schematic showing LCM collection of young stage 24 and old stage 32 growth cones. B , mRNAs identified in stage 24 growth cones were manually classified into different functional categories using NCBI Gene tool. In total, 286 mRNAs were identified of which 171 transcripts have a known function. The pie chart represents the functional classification of these 171 mRNAs. C , Comparison of functional classes of mRNAs between old (Fig. 2 A) and young ( B ) growth cones shows that most categories of mRNAs are upregulated with age (upward arrow), except for transcripts involved in protein synthesis and translation, demonstrating the increasing complexity of the growth cone mRNA pool with development. D , Comparative profiling shows that young growth cones have far fewer mRNAs than old growth cones (286 vs 958) and that the degree of overlap is ∼80%. E , IPA identified pathways that are specifically upregulated in stage 32 old versus stage 24 young growth cones. F , EphB4 mRNA localization in stage 32 growth cones was confirmed by fluorescence in situ hybridization. G , The specificity of the fluorescence signal was calculated by the mean pixel (fluorescence) intensity/unit area of antisense signal compared with its corresponding sense signal (n = 3 for each group). Statistical analysis performed using the Kruskal–Wallis test. Scale bar, 5 μm. H , Q-PCR results for EphB4 mRNA in LCM-isolated RGC cell bodies of old (stage 40) and young (stage 32) retina indicating similar expression levels at both developmental stages (n = 4 for each stage).|
|Figure 6. Comparative subcellular profiling reveals mRNAs enriched in growth cone. A , Schematic showing the two axonal subcompartments selected for LCM (axon shaft and growth cone). B , Assignment of the known annotated growth cone enriched mRNAs according to biological functional categories represented by pie chart. The cytoskeletal category is the major functional category of growth cone enriched mRNAs. C , List of known annotated genes that were at least 1.5-fold (GC/axon signal intensity ratio) enriched in Xenopus growth cones with respect to axons.|
|Figure 7. Mouse and Xenopus RGC growth cone mRNA profiles show conserved functional pathways. A , A Venn diagram showing 54% overlap in the transcript profiles of E16 mouse versus stage 32 Xenopus RGC growth cone mRNA. B , IPA reveals the most enriched biological pathways, such as oxidative phosphorylation (p < 0.05) and the number of mRNAs identified for each pathway common in both species. C , IPA identifies protein synthesis as the most enriched functional category among others common in both species (p < 0.05).|
References [+] :
Andreassi, An NGF-responsive element targets myo-inositol monophosphatase-1 mRNA to sympathetic neuron axons. 2010, Pubmed