Genome-wide analysis of RNA and protein localization and local translation in mESC-derived neurons

Expression of transgenic biotin ligases in inducible neuronal murine cell lines by integration into the mHipp11 gene locus

Biotin proximity labeling is a powerful method for identifying proteins associated with a specific organelle, a bait protein, or RNA. It requires the expression of a modified biotin ligase by transient transfection or from a stably integrated expression construct. Because such stable integration of transgenes into stem cells can lead to silencing during differentiation, targeting a biotin ligase to a genomic safe harbor site would be beneficial. Here, we report on the successful targeting and expression of two biotin ligase constructs to the mouse Hipp11 locus during neuronal differentiation. While randomly integrated MicroID and TurboID are expressed and active in mouse embryonic stem cells (mESCs), expression ceases upon differentiation into mESC-derived neurons, which is independent of the promoter used. In contrast, targeting of the same expression cassette to the mHipp11 locus results in expression, correct localization, and biotinylation activity not only in mESCs but also in neurons 8-10 days after differentiation. This demonstrates that the mouse Hipp11 locus is a promising genomic integration site for transgenic biotin ligases in mESCs and mESC-derived neurons.

Concurrent Profiling of Localized Transcriptome and RNA Dynamics in Neurons by Spatial SLAMseq

The asymmetric distribution of RNA within a cell plays a pivotal biological role, ensuring the distinctive shapes and functionality of subcellular compartments. In neurons, these mechanisms are fundamental to cellular growth, synaptic plasticity, and information processing. To understand these mechanisms, diverse methods have been developed to analyze localized transcripts. Here, we outline our optimized method for measurement of mRNA half-lives in subcellular neuronal compartments-neurites, and cytoplasmic and nuclear fractions of cell bodies. We call this method spatial SLAMseq, as it combines SLAMseq with subcellular compartment separation techniques. Spatial SLAMseq facilitates the concurrent measurement of mRNA dynamics and steady-state RNA levels within neuronal subcellular compartments.

The long noncoding RNA nHOTAIRM1 is necessary for differentiation and activity of iPSC-derived spinal motor neurons

The mammalian nervous system is made up of an extraordinary array of diverse cells that form intricate functional connections. The programs underlying cell lineage specification, identity and function of the neuronal subtypes are managed by regulatory proteins and RNAs, which coordinate the succession of steps in a stereotyped temporal order. In the central nervous system (CNS), motor neurons (MNs) are responsible for controlling essential functions such as movement, breathing, and swallowing by integrating signal transmission from the cortex, brainstem, and spinal cord (SC) towards peripheral muscles. A prime role in guiding the progression of progenitor cells towards the MN fate has been largely attributed to protein factors. More recently, the relevance of a class of regulatory RNAs abundantly expressed in the CNS - the long noncoding RNAs (lncRNAs) - has emerged overwhelmingly. LncRNA-driven gene expression control is key to regulating any step of MN differentiation and function, and its derangement profoundly impacts neuronal pathophysiology. Here, we uncover a novel function for the neuronal isoform of HOTAIRM1 (nHOTAIRM1), a lncRNA specifically expressed in the SC. Using a model system that recapitulates spinal MN (spMN) differentiation, we show that nHOTAIRM1 intervenes in the binary cell fate decision between MNs and interneurons, acting as a pro-MN factor. Furthermore, human iPSC-derived spMNs without nHOTAIRM1 display altered neurite outgrowth, with a significant reduction of both branch and junction numbers. Finally, the expression of genes essential for synaptic connectivity and neurotransmission is also profoundly impaired when nHOTAIRM1 is absent in spMNs. Mechanistically, nHOTAIRM1 establishes both direct and indirect interactions with a number of target genes in the cytoplasm, being a novel post-transcriptional regulator of MN biology. Overall, our results indicate that the lncRNA nHOTAIRM1 is essential for the specification of MN identity and the acquisition of proper morphology and synaptic activity of post-mitotic MNs.

mRNA stability and m6A are major determinants of subcellular mRNA localization in neurons

For cells to perform their biological functions, they need to adopt specific shapes and form functionally distinct subcellular compartments. This is achieved in part via an asymmetric distribution of mRNAs within cells. Currently, the main model of mRNA localization involves specific sequences called "zipcodes" that direct mRNAs to their proper locations. However, while thousands of mRNAs localize within cells, only a few zipcodes have been identified, suggesting that additional mechanisms contribute to localization. Here, we assess the role of mRNA stability in localization by combining the isolation of the soma and neurites of mouse primary cortical and mESC-derived neurons, SLAM-seq, m6A-RIP-seq, the perturbation of mRNA destabilization mechanisms, and the analysis of multiple mRNA localization datasets. We show that depletion of mRNA destabilization elements, such as m6A, AU-rich elements, and suboptimal codons, functions as a mechanism that mediates the localization of mRNAs associated with housekeeping functions to neurites in several types of neurons.

Massively parallel identification of mRNA localization elements in primary cortical neurons

Cells adopt highly polarized shapes and form distinct subcellular compartments in many cases due to the localization of many mRNAs to specific areas, where they are translated into proteins with local functions. This mRNA localization is mediated by specific cis-regulatory elements in mRNAs, commonly called 'zipcodes'. Although there are hundreds of localized mRNAs, only a few zipcodes have been characterized. Here we describe a novel neuronal zipcode identification protocol (N-zip) that can identify zipcodes across hundreds of 3' untranslated regions. This approach combines a method of separating the principal subcellular compartments of neurons-cell bodies and neurites-with a massively parallel reporter assay. N-zip identifies the let-7 binding site and (AU)_n motif as de novo zipcodes in mouse primary cortical neurons. Our analysis also provides, to our knowledge, the first demonstration of an miRNA affecting mRNA localization and suggests a strategy for detecting many more zipcodes.

Digital color-coded molecular barcoding reveals dysregulation of common FUS and FMRP targets in soma and neurites of ALS mutant motoneurons

Mutations in RNA binding proteins (RBPs) have been linked to the motor neuron disease amyotrophic lateral sclerosis (ALS). Extensive auto-regulation, cross-regulation, cooperation and competition mechanisms among RBPs are in place to ensure proper expression levels of common targets, often including other RBPs and their own transcripts. Moreover, several RBPs play a crucial role in the nervous system by localizing target RNAs in specific neuronal compartments. These include the RBPs FUS, FMRP, and HuD. ALS mutations in a given RBP are predicted to produce a broad impact on such delicate equilibrium. Here we studied the effects of the severe FUS-P525L mutation on common FUS and FMRP targets. Expression profiling by digital color-coded molecular barcoding in cell bodies and neurites of human iPSC-derived motor neurons revealed altered levels of transcripts involved in the cytoskeleton, neural projection and synapses. One of the common targets is HuD, which is upregulated because of the loss of FMRP binding to its 3'UTR due to mutant FUS competition. Notably, many genes are commonly altered upon FUS mutation or HuD overexpression, suggesting that a substantial part of the effects of mutant FUS on the motor neuron transcriptome could be due to HuD gain-of-function. Among altered transcripts, we also identified other common FUS and FMRP targets, namely MAP1B, PTEN, and AP2B1, that are upregulated upon loss of FMRP binding on their 3'UTR in FUS-P525L motor neurons. This work demonstrates that the impairment of FMRP function by mutant FUS might alter the expression of several genes, including new possible biomarkers and therapeutic targets for ALS.

A massively parallel reporter assay reveals focused and broadly encoded RNA localization signals in neurons

Asymmetric subcellular mRNA localization allows spatial regulation of gene expression and functional compartmentalization. In neurons, localization of specific mRNAs to neurites is essential for cellular functioning. However, it is largely unknown how transcript sorting works in a sequence-specific manner. Here, we combined subcellular transcriptomics and massively parallel reporter assays and tested ∼50 000 sequences for their ability to localize to neurites. Mapping the localization potential of >300 genes revealed two ways neurite targeting can be achieved: focused localization motifs and broadly encoded localization potential. We characterized the interplay between RNA stability and localization and identified motifs able to bias localization towards neurite or soma as well as the trans-acting factors required for their action. Based on our data, we devised machine learning models that were able to predict the localization behavior of novel reporter sequences. Testing this predictor on native mRNA sequencing data showed good agreement between predicted and observed localization potential, suggesting that the rules uncovered by our MPRA also apply to the localization of native full-length transcripts.

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism

As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locations remains a challenging task due to the difficulty of finding representative features and robust classifiers. Many feature fusion methods have been widely applied to tackle the above issues. However, they still suffer from accuracy loss due to feature redundancy. Furthermore, multiple protein subcellular locations prediction is more complicated since it is fundamentally a multi-label classification problem. The traditional binary classifiers or even multi-class classifiers cannot achieve satisfactory results. This paper proposes a novel method for protein subcellular location prediction with both single and multiple sites based on deep convolutional neural networks. Specifically, we first obtain the integrated features by simultaneously considering the pseudo amino acid, amino acid index distribution, and physicochemical property. We then adopt deep convolutional neural networks to extract high-dimensional features from the fused feature, removing the redundant preliminary features and gaining better representations of the raw sequences. Moreover, we use the self-attention mechanism and a customized loss function to ensure that the model is more inclined to positive data. In addition, we use random k-label sets to reduce the number of prediction labels. Meanwhile, we employ a hybrid strategy of over-sampling and under-sampling to tackle the data imbalance problem. We compare our model with three representative classification alternatives. The experiment results show that our model achieves the best performance in terms of accuracy, demonstrating the efficacy of the proposed model.

Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization

In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find. Second, the classifier structure is difficult to determine as it is greatly affected by the distribution of classified data. To solve these problems, a self-evoluting framework using DCNNs for multilocus protein subcellular localization is proposed. It has three characteristics that the previous algorithms do not. The first is that it combines the ant colony algorithm with the DCNN to form a self-evoluting algorithm for multilocus protein subcellular localization. The second is that it randomly groups subcellular sites using a limited random k-labelsets multi-label classification method. It also solves complex problems in a divide-and-conquer approach and proposes a flexible expansion model. The third is that it realizes the random selection feature extraction method in the positioning process and avoids the defects in individual feature extraction methods. The algorithm in the present paper is tested on the human database, and the overall correct rate is 67.17%, which is higher than that for the stacked self-encoder (SAE), support vector machine (SVM), random forest classifier (RF), or single deep convolutional neural network.Graphical abstract The algorithm mentioned in the present paper mainly includes four parts. They are protein sequence data preprocessing, integrated DCNN model construction, finding optimal DCNN combination by ant colony optimization, and protein subcellular localization for sequences. These parts are sequential relationships and the data obtained in the previous part is the basis for the latter part of the function. In the part of data preprocessing, the limited RAkEL multi-label classification method is used to randomly group subcellular sites. At the same time, the feature fusion of protein sequences is carried out by using multiple feature extraction methods. Each combination including features and sites information corresponds to a DCNN model. In the part of finding optimal DCNN combination by ant colony optimization, the main purpose is to find the best combination of DCNN models through the global optimization ability of the ant colony algorithm. The positioning of sequences is mainly to obtain multilocus subcellular localization by the optimal model combination.

Conservation of a core neurite transcriptome across neuronal types and species

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.