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Conservation of a core neurite transcriptome across neuronal types and species

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Abstract The intracellular localization of mRNAs allows neurons to control gene expression in neurite extensions (axons and dendrites) and respond rapidly to local stimuli. This plays an important role in diverse processes including neuronal growth and synaptic plasticity, which in turn serves as a foundation for learning and memory. Recent high‐throughput analyses have revealed that neurites contain hundreds to thousands of mRNAs, but an analysis comparing the transcriptomes derived from these studies has been lacking. Here we analyze 20 datasets pertaining to neuronal mRNA localization across species and neuronal types and identify a conserved set of mRNAs that had robustly localized to neurites in a high number of the studies. The set includes mRNAs encoding for ribosomal proteins and other components of the translation machinery, mitochondrial proteins, cytoskeletal components, and proteins associated with neurite formation. Our combinatorial analysis provides a unique resource for future hypothesis‐driven research. This article is categorized under: RNA Export and Localization > RNA Localization RNA Evolution and Genomics > Computational Analyses of RNA RNA Methods > RNA Analyses in Cells
Comparison of high coverage datasets. (a, b) Principle component analysis (PCA) of RNA expression levels in neurites. Normalized transcripts per million (TPM) values (log10) for transcripts detected in at least three datasets with TPM > 10 (8,809 transcripts) were used for PCA. Plots show the distribution of datasets along the principle components (PC) 1 and 2 (a) or 1 and 3 (b). The way of obtaining neurons is indicated by color and type of neurons by the shape of the dot. (c) Heatmaps for the top 200 genes contributing to the first three PCs. Normalized TPM values (log10) were used for the heatmaps. Transcripts are sorted by hierarchical clustering using Euclidian distance and complete method. The specific 200 transcripts from PC are listed in subsets of extended online Table 1. (d) Gene ontology (GO) analysis of the top 200 transcripts contributing to the PC1 and PC3. Five most significant GO terms are shown. For PC2, the genome browser view of 3′‐mRNA‐seq peak for Ccnd2, mapping outside the annotated gene regions, is shown. Similar pattern is characteristic for 16 of the top 25 genes contributing to PC2 (Figure 1). (e) Heatmap of cell type and synaptic markers. Normalized TPM values (log10) were used for the heatmaps. A colored bar indicates which type of neuronal extensions were analyzed in each dataset (green: axons; blue: neurites, or neuropil)
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Datasets included in this study. (a) Bar graph showing the number of transcripts detected at a high confidence expression level (TPM > 10) in neurites for each dataset. The color of the bars indicates if transcripts are identified in at least three independent datasets (dark gray) or in less than three datasets (light gray). A dotted line separates datasets with high coverage (>5,000 transcripts) from the other datasets. (b) Annotation of the datasets: model system and cell type; citation; the species of a dataset (human, mouse, or rat); the separation method for outgrowths and cell bodies; the method of data acquisition. RNA‐seq studies were re‐analyzed using the PiGx RNA‐seq pipeline (Wurmus et al., ). If studies used different analysis methods (*) or raw data was unavailable (**), transcript detection levels were obtained from supplementary data of the respective studies
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RNA Methods > RNA Analyses in Cells
RNA Evolution and Genomics > Computational Analyses of RNA
RNA Export and Localization > RNA Localization

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