Subcellular RNA Localization Research Articles

RNALocate v3.0: Advancing the Repository of RNA Subcellular Localization with Dynamic Analysis and Prediction.

Subcellular localization of RNA is a crucial mechanism for regulating diverse biological processes within cells. Dynamic RNA subcellular localizations are essential for maintaining cellular homeostasis; however, their distribution and changes during development and differentiation remain largely unexplored. To elucidate the dynamic patterns of RNA distribution within cells, we have upgraded RNALocate to version 3.0, a repository for RNA-subcellular localization (http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/). RNALocate v3.0 incorporates and analyzes RNA subcellular localization sequencing data from over 850 samples, with a specific focus on the dynamic changes in subcellular localizations under various conditions. The species coverage has also been expanded to encompass mammals, non-mammals, plants and microbes. Additionally, we provide an integrated prediction algorithm for the subcellular localization of seven RNA types across eleven subcellular compartments, utilizing convolutional neural networks(CNNs) and transformer models. Overall, RNALocate v3.0 contains a total of 1 844 013 RNA-localization entries covering 26 RNA types, 242 species and 177 subcellular localizations. It serves as a comprehensive and readily accessible data resource for RNA-subcellular localization, facilitating the elucidation of cellular function and disease pathogenesis.

Subcellular Level Spatial Transcriptomics with PHOTON.

The subcellular localization of RNA is closely linked to its function. Many RNA species are partitioned into organelles and other subcellular compartments for storage, processing, translation, or degradation. Thus, capturing the subcellular spatial distribution of RNA would directly contribute to the understanding of RNA functions and regulation. Here, we present PHOTON (Photoselection of Transcriptome over Nanoscale), a method which combines high resolution imaging with high throughput sequencing to achieve spatial transcriptome profiling at subcellular resolution. We demonstrate PHOTON as a versatile tool to accurately capture the transcriptome of target cell types in situ at the tissue level such as granulosa cells in the ovary, as well as RNA content within subcellular compartments such as the nucleolus and the stress granule. Using PHOTON, we also reveal the functional role of m6A modification on mRNA partitioning into stress granules. These results collectively demonstrate that PHOTON is a flexible and generalizable platform for understanding subcellular molecular dynamics through the transcriptomic lens.

Mapping subcellular RNA localization with proximity labeling.

The subcellular localization of RNA is critical to a variety of physiological and pathological processes. Dissecting the spatiotemporal regulation of the transcriptome is key to understanding cell function and fate. However, it remains challenging to effectively enrich and catalogue RNAs from various subcellular structures using traditional approaches. In recent years, proximity labeling has emerged as an alternative strategy for efficient isolation and purification of RNA from these intricate subcellular compartments. This review focuses on examining RNA-related proximity labeling tools and exploring their application in elucidating the spatiotemporal regulation of RNA at the subcellular level.

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

PreSubLncR: Predicting Subcellular Localization of Long Non-Coding RNA Based on Multi-Scale Attention Convolutional Network and Bidirectional Long Short-Term Memory Network

The subcellular localization of long non-coding RNA (lncRNA) provides important insights and opportunities for an in-depth understanding of cell biology, revealing disease mechanisms, drug development, and innovation in the biomedical field. Although several computational methods have been proposed to identify the subcellular localization of lncRNA, it is difficult to accurately predict the subcellular localization of lncRNA effectively with these methods. In this study, a new deep-learning predictor called PreSubLncR has been proposed for accurately predicting the subcellular localization of lncRNA. This predictor firstly used the word embedding model word2vec to encode the RNA sequences, and then combined multi-scale one-dimensional convolutional neural networks with attention and bidirectional long short-term memory networks to capture the different characteristics of various RNA sequences. This study used multiple RNA subcellular localization datasets for experimental validation, and the results showed that our method has higher accuracy and robustness compared with other state-of-the-art methods. It is expected to provide more in-depth insights into cell function research.

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

Intracellular RNA Labeling Technologies for the Analysis of RNA Biology†

Comprehensive SummaryAs a cornerstone of the central dogma of molecular biology, RNA plays vital roles in living organisms. Over the past few decades, many RNA labeling technologies have been developed to elucidate the biological function of RNA. These technologies have significantly advanced our understanding of RNA secondary structure, localization, and turnover. Additionally, taking advantage of these innovative RNA labeling approaches, plenty of tool kits have been devised for the regulation of RNA‐related biological process, such as gene expression and gene editing. In this review, we primarily focus on an array of intracellular RNA labeling methods, encompassing chemical probes‐based labeling, metabolic labeling, and proximity‐dependent labeling. We also provide a brief overview of their applications in the research of RNA biology. Finally, the perspectives of RNA labeling are also discussed.Key Scientists4SU was first used in1978 as a safer way to directly examine RNA synthesis. However, the enrichment of 4SU‐labeled RNA seriously relied on mercurated cellulose columns, which are toxic. In 2005, Boothroyd et al. introduced a novel covalent‐modification method of 4SU based on thiol exchange reaction. Then, Salic et al. applied 5EU, which is clickable, for RNA metabolic labeling in animals. Later, Spitale et al. designed 5EC and 2’AzU, which enabled cell specific RNA metabolic labeling under the help of specific enzyme. In 2013, directly chemical RNA labeling in living cell for RNA structure mapping was realized by Chang and Kool et al. using SHAPE reagent, which was further modified with an azide handle in 2015. Weissman, Bevilacqua and Assmann et al. introduced DMS probe that targets adenine and cytosine of RNA for RNA structure mapping in 2014. Then, Chang et al. exploited psoralen for RNA duplex mapping in 2016. Later, He, Zhou and Zhang et al. described another clickable probe N3‐kethoxal targeting guanine for RNA secondary structure mapping. Besides, Chang and Ting et al. described a proximity‐dependent RNA labeling method based on APEX in 2019, revealing subcellular RNA localization. Meanwhile, Zou and Wang et al. achieved proximity‐dependent RNA labeling using miniSOG oxidase, which provided an alternative method to investigate the spatial transcriptome. Spitale et al. utilizing photosensitizer dibromofluorescein (DBF) to realize proximity‐dependent RNA labeling and sequencing.

Differential subcellular localization of ASPM RNA and protein.

RNAs encoding some centrosomal components are trafficked to the organelle during mitosis. Some RNAs, including ASPM , localize to the centrosome co-translationally. However, the relative position of these RNAs and their protein after trafficking to centrosomes remained unclear. We find that mislocalization of ASPM RNA from the centrosome does not affect the localization of ASPM protein. Further, ASPM RNA and ASPM protein reside in two physically close yet distinct subcellular spaces, with ASPM RNA on the astral side of the centrosome and ASPM protein on the spindle side. This suggests subtly distinct locations of ASPM RNA translation and ASPM protein function.

Single-Cell Single-Molecule RNA-FISH Combined with Immunofluorescence and High-Speed and High-Resolution Scanning Analysis to Visualize the Reactivation of Latent HIV-1.

Latent HIV-1 reservoirs are a major obstacle to the eradication of HIV-1. Several cure strategies have been proposed to eliminate latent reservoirs. One of the key strategies involves the reactivation of latent HIV-1 from cells using latency-reversing agents. However, currently it is unclear whether any of the latency-reversing agents are able to completely reactivate HIV-1 provirus transcription in all latent cells. An understanding of the reactivation of HIV-1 provirus at single-cell single-molecule level is necessary to fully comprehend the reactivation of HIV-1 in the reservoirs. Furthermore, since reactivable viruses in the pool of latent reservoirs are rare, combining single-cell imaging techniques with the ability to visualize a large number of reactivated single cells that express both viral RNA and proteins in a pool of uninfected and non-reactivated cells will provide unprecedented information about cell-to-cell variability in reactivation. Here, we describe the single-cell single-molecule RNA-FISH (smRNA-FISH) method to visualize HIV-1 gag RNA combined with the immunofluorescence (IF) method to detect Gag protein to characterize the reactivated cells. This method allows the visualization of subcellular localization of RNA and proteins before and after reactivation and facilitates absolute quantitation of the number of transcripts per cell using FISH-quant. In addition, we describe a high-speed and high-resolution scanning (HSHRS) fluorescence microscopy imaging method to visualize rare and reactivated cells in a pool of non-reactivated cells with high efficiency.

Detection of a 7SL RNA-derived small non-coding RNA using Molecular Beacons invitro and in cells.

Small non-coding RNAs (sncRNA) are involved in many steps of the gene expression cascade and regulate processing and expression of mRNAs by the formation ofribonucleoprotein complexes (RNP) such as the RNA-induced silencing complex (RISC). By analyzing small RNA Seq data sets, we identified a sncRNA annotated as piR-hsa-1254, which is likely derived from the 3'-end of 7SL RNA2 (RN7SL2), herein referred to as snc7SL RNA. The 7SL RNA is an abundant long non-coding RNA polymerase III transcript and serves as structural component of the cytoplasmic signal recognition particle (SRP). To evaluate a potential functional role of snc7SL RNA, we aimed to define its cellular localization by live cell imaging. Therefore, a Molecular Beacon (MB)-based method was established to compare the subcellular localization of snc7SL RNA with its precursor 7SL RNA. We designed and characterized several MBs invitro and tested those by live cell fluorescence microscopy. Using a multiplex approach, we show that 7SL RNA localizes mainly to the endoplasmic reticulum (ER), as expected for the SRP, whereas snc7SL RNA predominately localizes to the nucleus. This finding suggests a fundamentally different function of 7SL RNA and its derivate snc7SL RNA.

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

Localization of Kif1c mRNA to cell protrusions dictates binding partner specificity of the encoded protein.

Subcellular localization of messenger RNA (mRNA) is a widespread phenomenon that can impact the regulation and function of the encoded protein. In nonneuronal cells, specific mRNAs localize to cell protrusions, and proper mRNA localization is required for cell migration. However, the mechanisms by which mRNA localization regulates protein function in this setting remain unclear. Here, we examined the functional consequences of localization of the mRNA encoding KIF1C. KIF1C is a kinesin motor protein required for cell migration and mRNA trafficking, including trafficking of its own mRNA. We show that Kif1c mRNA localization does not regulate KIF1C's protein abundance, distribution, or ability to traffic other mRNAs. Conversely, Kif1c mRNA localization to protrusions is required for directed cell migration. We used mass spectrometry to identify binding partners of endogenous KIF1C, which revealed dramatic dysregulation of the number and identity of KIF1C interactors in response to Kif1c mRNA mislocalization. These results therefore uncovered a mechanistic connection between mRNA localization to cell protrusions and the specificity of protein-protein interactions. We anticipate that this mechanism is not limited to Kif1c and is likely to be a general principle that impacts the functions of proteins encoded by protrusion-enriched mRNAs in nonneuronal cells.

Analysis of Gene Expression Patterns and RNA Localization by Fluorescence in Situ Hybridization in Whole Mount Drosophila Testes.

Researchers have used RNA in situ hybridization to detect the presence of RNA in cells and tissues for approximately 50years. The recent development of a method capable of visualizing a single RNA molecule by utilizing tiled fluorescently labeled oligonucleotide probes that together produce a diffraction-limited spot has greatly increased the resolution of this technique, allowing for the precise determination of subcellular RNA localization and relative abundance. Here, we present our method for single molecule RNA fluorescence in situ hybridization (smFISH) in whole mount Drosophila testes and discuss how we have utilized this method to better understand the expression pattern of the highly unusual Y-linked genes.

RNA supply drives physiological granule assembly in neurons

Membraneless cytoplasmic condensates of mRNAs and proteins, known as RNA granules, play pivotal roles in the regulation of mRNA fate. Their maintenance fine-tunes time and location of protein expression, affecting many cellular processes, which require complex protein distribution. Here, we report that RNA granules—monitored by DEAD-Box helicase 6 (DDX6)—disassemble during neuronal maturation both in cell culture and in vivo. This process requires neuronal function, as synaptic inhibition results in reversible granule assembly. Importantly, granule assembly is dependent on the RNA-binding protein Staufen2, known for its role in RNA localization. Altering the levels of free cytoplasmic mRNA reveals that RNA availability facilitates DDX6 granule formation. Specifically depleting RNA from DDX6 granules confirms RNA as an important driver of granule formation. Moreover, RNA is required for DDX6 granule assembly upon synaptic inhibition. Together, this data demonstrates how RNA supply favors RNA granule assembly, which not only impacts subcellular RNA localization but also translation-dependent synaptic plasticity, learning, and memory.

Detecting RNA dynamics in live mammalian cells with fluorescence lifetime‐based sensors

RNA function is closely linked with subcellular localization and interaction with other biological molecules such as other RNAs and proteins. Acute cellular perturbations and disease mutations may alter RNA dynamics and hence function. Therefore, visualizing RNAs in live cells is critically important for a mechanistic understanding. However, robust fluorescence labels for specific RNA molecules to achieve quantitative detection in the complex cellular environment for diverse RNA species are under‐developed. Typically, fluorescent RNA tags are engineered where a genetically encoded short RNA binds a small molecule that conveys fluorescence properties. These efforts aim to generate large fluorescence intensity changes of the fluorophore upon binding the RNA. Here, we use fluorescence lifetime as an orthogonal approach for a fluorescence RNA sensor. We used the Riboglow platform, an RNA tagging platform that relies on binding of a small fluorescent molecule to a short RNA sequence. First, we compared probe fluorescence signal in live cells in the presence and absence of the RNA tag. We found that fluorescence lifetime provides substantially improved contrast compared with fluorescence intensity. Second, we assessed sensitivity of monitoring subcellular RNA localization dynamics live via fluorescence lifetime. Third, we are exploring modularity of the system by varying the RNA‐ and probe component to enable multi‐parameter RNA detection. Together, we find that fluorescence lifetime offers advantages to track RNAs and quantify localization versus fluorescence intensity. We demonstrated the versatility of this system in diverse cellular examples. Our system is the first platform that exploits fluorescence lifetime for live RNA tracking in cells. We plan to expand this imaging modality further to quantitatively assess RNA dynamics.

Transcriptome-scale methods for uncovering subcellular RNA localization mechanisms.

Across a variety of systems, thousands of RNAs are localized to specific subcellular locations. However, for the vast majority of these RNAs, the mechanisms that underlie their transport are unknown. Historically, these mechanisms were uncovered for a single transcript at a time by laboriously testing the ability of RNA fragments to direct transcript localization. Recently developed methods profile the content of subcellular transcriptomes using high-throughput sequencing, allowing the analysis of the localization of thousands of transcripts at once. By identifying commonalities shared among multiple localized transcripts, these methods have the potential to rapidly expand our understanding of RNA localization mechanisms.

FISH-quant v2: a scalable and modular tool for smFISH image analysis.

Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling quantitative studies of gene expression and its regulatory mechanisms. Today, these methods are applicable at a large scale, which in turn come with a need for adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular tool accessible for both experts and non-experts. Our user-friendly package allows the user to segment nuclei and cells, detect isolated RNAs, decompose dense RNA clusters, quantify RNA localization patterns and visualize these results both at the single-cell level and variations within the cell population. This tool was validated and applied on large-scale smFISH image data sets, revealing diverse subcellular RNA localization patterns and a surprisingly high degree of cell-to-cell heterogeneity.

The Role of Alternative Polyadenylation in the Regulation of Subcellular RNA Localization.

Alternative polyadenylation (APA) is a widespread and conserved regulatory mechanism that generates diverse 3′ ends on mRNA. APA patterns are often tissue specific and play an important role in cellular processes such as cell proliferation, differentiation, and response to stress. Many APA sites are found in 3′ UTRs, generating mRNA isoforms with different 3′ UTR contents. These alternate 3′ UTR isoforms can change how the transcript is regulated, affecting its stability and translation. Since the subcellular localization of a transcript is often regulated by 3′ UTR sequences, this implies that APA can also change transcript location. However, this connection between APA and RNA localization has only recently been explored. In this review, we discuss the role of APA in mRNA localization across distinct subcellular compartments. We also discuss current challenges and future advancements that will aid our understanding of how APA affects RNA localization and molecular mechanisms that drive these processes.

Visualization and Quantification of Subcellular RNA Localization Using Single-Molecule RNA Fluorescence In Situ Hybridization.

Advancements in imaging technologies, especially approaches that allow the imaging of single RNA molecules, have opened new avenues to understand RNA regulation, from synthesis to decay with high spatial and temporal resolution. Here, we describe a protocol for single-molecule fluorescent in situ hybridization (smFISH) using three different approaches for synthesizing the fluorescent probes. The three approaches described are commercially available probes, single-molecule inexpensive FISH (smiFISH), and in-house enzymatically labeled probes. These approaches offer technical and economic flexibility to meet the specific needs of an experiment. In addition, we provide a protocol to perform automated smFISH spot detection using the software FISH-quant.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

SummaryAdvances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.