RNA proximity sequencing reveals the spatial organization of the transcriptome in the nucleus

Protocol for profiling virus-to-host RNA-RNA interactions in infected cells by RIC-seq

Virus-to-host RNA-RNA interactions directly regulate host mRNA stability and viral replication. However, globally profiling virus-to-host in situ RNA-RNA interactions remains challenging. Here, we present an RNA in situ conformation sequencing (RIC-seq)-based protocol for mapping high-confidence virus-to-host in situ RNA-RNA interactions in infected cells. We detail steps for formaldehyde crosslinking, pCp-biotin labeling, in situ proximity ligation, chimeric RNA enrichment, strand-specific library construction, and data analysis. This protocol allows unbiased identification of virus-to-host RNA-RNA interactions for various RNA viruses and is potentially applicable to DNA virus-derived transcripts. For complete details on the use and execution of this protocol, please refer to Zhao et al.1.

The selective sponging of miRNAs by OIP5-AS1 regulates metabolic reprogramming of pyruvate in adenoma-carcinoma transition of human colorectal cancer

RNA interactomes and their diversified functionalities have recently benefited from critical methodological advances leading to a paradigm shift from a conventional conception on the regulatory roles of RNA in pathogenesis. However, the dynamic RNA interactomes in adenoma-carcinoma sequence of human CRC remain unexplored. The coexistence of adenoma, cancer, and normal tissues in colorectal cancer (CRC) patients provides an appropriate model to address this issue. Here, we adopted an RNA in situ conformation sequencing technology for mapping RNA-RNA interactions in CRC patients. We observed large-scale paired RNA counts and identified some unique RNA complexes including multiple partners RNAs, single partner RNAs, non-overlapping single partner RNAs. We focused on the antisense RNA OIP5-AS1 and found that OIP5-AS1 could sponge different miRNA to regulate the production of metabolites including pyruvate, alanine and lactic acid. Our findings provide novel perspectives in CRC pathogenesis and suggest metabolic reprogramming of pyruvate for the early diagnosis and treatment of CRC.

SARS-CoV-2 RNA stabilizes host mRNAs to elicit immunopathogenesis

SARS-CoV-2 RNA interacts with host factors to suppress interferon responses and simultaneously induces cytokine release to drive the development of severe coronavirus disease 2019 (COVID-19). However, how SARS-CoV-2 hijacks host RNAs to elicit such imbalanced immune responses remains elusive. Here, we analyzed SARS-CoV-2 RNA in situ structures and interactions in infected cells and patient lung samples using RIC-seq. We discovered that SARS-CoV-2 RNA forms 2,095 potential duplexes with the 3' UTRs of 205 host mRNAs to increase their stability by recruiting RNA-binding protein YBX3 in A549 cells. Disrupting the SARS-CoV-2-to-host RNA duplex or knocking down YBX3 decreased host mRNA stability and reduced viral replication. Among SARS-CoV-2-stabilized host targets, NFKBIZ was crucial for promoting cytokine production and reducing interferon responses, probably contributing to cytokine storm induction. Our study uncovers the crucial roles of RNA-RNA interactions in the immunopathogenesis of RNA viruses such as SARS-CoV-2 and provides valuable host targets for drug development.

lhCLIP reveals the in vivo RNA-RNA interactions recognized by hnRNPK

RNA-RNA interactions play a crucial role in regulating gene expression and various biological processes, but identifying these interactions on a transcriptomic scale remains a challenge. To address this, we have developed a new biochemical technique called pCp-biotin labelled RNA hybrid and ultraviolet crosslinking and immunoprecipitation (lhCLIP) that enables the transcriptome-wide identification of intra- and intermolecular RNA-RNA interactions mediated by a specific RNA-binding protein (RBP). Using lhCLIP, we have uncovered a diverse landscape of intermolecular RNA interactions recognized by hnRNPK in human cells, involving all major classes of noncoding RNAs (ncRNAs) and mRNA. Notably, hnRNPK selectively binds with snRNA U4, U11, and U12, and shapes the secondary structure of these snRNAs, which may impact RNA splicing. Our study demonstrates the potential of lhCLIP as a user-friendly and widely applicable method for discovering RNA-RNA interactions mediated by a particular protein of interest and provides a valuable tool for further investigating the role of RBPs in gene expression and biological processes.

Complementary Alu sequences mediate enhancer-promoter selectivity

Enhancers determine spatiotemporal gene expression programs by engaging with long-range promoters^1-4. However, it remains unknown how enhancers find their cognate promoters. We recently developed a RNA in situ conformation sequencing technology to identify enhancer-promoter connectivity using pairwise interacting enhancer RNAs and promoter-derived noncoding RNAs^5,6. Here we apply this technology to generate high-confidence enhancer-promoter RNA interaction maps in six additional cell lines. Using these maps, we discover that 37.9% of the enhancer-promoter RNA interaction sites are overlapped with Alu sequences. These pairwise interacting Alu and non-Alu RNA sequences tend to be complementary and potentially form duplexes. Knockout of Alu elements compromises enhancer-promoter looping, whereas Alu insertion or CRISPR-dCasRx-mediated Alu tethering to unregulated promoter RNAs can create new loops to homologous enhancers. Mapping 535,404 noncoding risk variants back to the enhancer-promoter RNA interaction maps enabled us to construct variant-to-function maps for interpreting their molecular functions, including 15,318 deletions or insertions in 11,677 Alu elements that affect 6,497 protein-coding genes. We further demonstrate that polymorphic Alu insertion at the PTK2 enhancer can promote tumorigenesis. Our study uncovers a principle for determining enhancer-promoter pairing specificity and provides a framework to link noncoding risk variants to their molecular functions.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Non-coding RNAs in immunoregulation and autoimmunity: Technological advances and critical limitations

Immune cell function is critically dependent on precise control over transcriptional output from the genome. In this respect, integration of environmental signals that regulate gene expression, specifically by transcription factors, enhancer DNA elements, genome topography and non-coding RNAs (ncRNAs), are key components. The first three have been extensively investigated. Even though non-coding RNAs represent the vast majority of cellular RNA species, this class of RNA remains historically understudied. This is partly because of a lag in technological and bioinformatic innovations specifically capable of identifying and accurately measuring their expression. Nevertheless, recent progress in this domain has enabled a profusion of publications identifying novel sub-types of ncRNAs and studies directly addressing the function of ncRNAs in human health and disease. Many ncRNAs, including circular and enhancer RNAs, have now been demonstrated to play key functions in the regulation of immune cells and to show associations with immune-mediated diseases. Some ncRNAs may function as biomarkers of disease, aiding in diagnostics and in estimating response to treatment, while others may play a direct role in the pathogenesis of disease. Importantly, some are relatively stable and are amenable to therapeutic targeting, for example through gene therapy. Here, we provide an overview of ncRNAs and review technological advances that enable their study and hold substantial promise for the future. We provide context-specific examples by examining the associations of ncRNAs with four prototypical human autoimmune diseases, specifically rheumatoid arthritis, psoriasis, inflammatory bowel disease and multiple sclerosis. We anticipate that the utility and mechanistic roles of these ncRNAs in autoimmunity will be further elucidated in the near future.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships

BACKGROUND

Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample.

RESULTS

Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking.

CONCLUSIONS

We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth "super-experiment" is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/ .

The nucleolus is the site for inflammatory RNA decay during infection

Inflammatory cytokines are key signaling molecules that can promote an immune response, thus their RNA turnover must be tightly controlled during infection. Most studies investigate the RNA decay pathways in the cytosol or nucleoplasm but never focused on the nucleolus. Although this organelle has well-studied roles in ribosome biogenesis and cellular stress sensing, the mechanism of RNA decay within the nucleolus is not completely understood. Here, we report that the nucleolus is an essential site of inflammatory pre-mRNA instability during infection. RNA-sequencing analysis reveals that not only do inflammatory genes have higher intronic read densities compared with non-inflammatory genes, but their pre-mRNAs are highly enriched in nucleoli during infection. Notably, nucleolin (NCL) acts as a guide factor for recruiting cytosine or uracil (C/U)-rich sequence-containing inflammatory pre-mRNAs and the Rrp6-exosome complex to the nucleolus through a physical interaction, thereby enabling targeted RNA delivery to Rrp6-exosomes and subsequent degradation. Consequently, Ncl depletion causes aberrant hyperinflammation, resulting in a severe lethality in response to LPS. Importantly, the dynamics of NCL post-translational modifications determine its functional activity in phases of LPS. This process represents a nucleolus-dependent pathway for maintaining inflammatory gene expression integrity and immunological homeostasis during infection.

The nucleolus is the traditional site for ribosomal RNA biogenesis. Here, the authors find that the nucleolus is a site of inflammatory pre-mRNA turnover and elucidated how immune homeostasis can be maintained by controlling inflammatory gene expression.

Detecting TAD-like domains from RNA-associated interactions

Topologically associated domains (TADs) are crucial chromatin structural units. Evidence has illustrated that RNA–chromatin and RNA–RNA spatial interactions, so-called RNA-associated interactions (RAIs), may be associated with TAD-like domains (TLDs). To decode hierarchical TLDs from RAIs, we proposed SuperTLD, a domain detection algorithm incorporating imputation. We applied SuperTLD on four RAI data sets and compared TLDs with the TADs identified from the corresponding Hi-C datasets. The TLDs and TADs share a moderate similarity of hierarchies ≥ 0.5312 and the finest structures ≥ 0.8295. Comparison between boundaries and domains further demonstrated the novelty of TLDs. Enrichment analysis of epigenetic characteristics illustrated that the novel TLDs exhibit an enriched CTCF by 0.6245 fold change and H3 histone marks enriched within domains. GO analysis on the TLD novel boundaries exhibited enriched diverse terms, revealing TLDs’ formation mechanism related closely to gene regulation.

Methods to Analyze the Non-Coding RNA Interactome—Recent Advances and Challenges

Most of the human genome is transcribed to generate a multitude of non-coding RNAs. However, while these transcripts have generated an immense amount of scientific interest, their biological function remains a subject of an intense debate. Understanding mechanisms of action of non-coding RNAs is a key to addressing the issue of biological relevance of these transcripts. Based on some well-understood non-coding RNAs that function inside the cell by interacting with other molecules, it is generally believed many other non-coding transcripts could also function in a similar fashion. Therefore, development of methods that can map RNA interactome is the key to understanding functionality of the extensive cellular non-coding transcriptome. Here, we review the vast progress that has been made in the past decade in technologies that can map RNA interactions with different sites in DNA, proteins or other RNA molecules; the general approaches used to validate the existence of novel interactions; and the challenges posed by interpreting the data obtained using the interactome mapping methods.

MiRNA fine tuning for crop improvement: using advance computational models and biotechnological tools

MiRNAs modulate target genes expression at post-transcriptional levels, by reducing spatial abundance of mRNAs. MiRNAs regulats plant metabolism, and emerged as regulators of plant stress responses. Which make miRNAs promising candidates for fine tuning to affectively alter crop stress tolerance and other important traits. With recent advancements in the computational biology and biotechnology miRNAs structure and target prediction is possible resulting in pin point editing; miRNA modulation can be done by up or down regulating miRNAs using recently available biotechnological tools (CRISPR Cas9, TALENS and RNAi). In this review we have focused on miRNA biogenesis, miRNA roles in plant development, plant stress responses and roles in signaling pathways. Additionally we have discussed latest computational prediction models for miRNA to target gene interaction and biotechnological systems used recently for miRNA modulation. We have also highlighted setbacks and limitations in the way of miRNA modulation; providing entirely a new direction for improvement in plant genomics primarily focusing miRNAs.

A working model for condensate RNA-binding proteins as matchmakers for protein complex assembly

Most cellular processes are carried out by protein complexes, but it is still largely unknown how the subunits of lowly expressed complexes find each other in the crowded cellular environment. Here, we will describe a working model where RNA-binding proteins in cytoplasmic condensates act as matchmakers between their bound proteins (called protein targets) and newly translated proteins of their RNA targets to promote their assembly into complexes. Different RNA-binding proteins act as scaffolds for various cytoplasmic condensates with several of them supporting translation. mRNAs and proteins are recruited into the cytoplasmic condensates through binding to specific domains in the RNA-binding proteins. Scaffold RNA-binding proteins have a high valency. In our model, they use homotypic interactions to assemble condensates and they use heterotypic interactions to recruit protein targets into the condensates. We propose that unoccupied binding sites in the scaffold RNA-binding proteins transiently retain recruited and newly translated proteins in the condensates, thus promoting their assembly into complexes. Taken together, we propose that lowly expressed subunits of protein complexes combine information in their mRNAs and proteins to colocalize in the cytoplasm. The efficiency of protein complex assembly is increased by transient entrapment accomplished by multivalent RNA-binding proteins within cytoplasmic condensates.

Three-dimensional genome organization via triplex-forming RNAs

An increasing number of long noncoding RNAs (lncRNAs) have been proposed to act as nuclear organization factors during interphase. Direct RNA-DNA interactions can be achieved by the formation of triplex helix structures where a single-stranded RNA molecule hybridizes by complementarity into the major groove of double-stranded DNA. However, whether and how these direct RNA-DNA associations influence genome structure in interphase chromosomes remain poorly understood. Here we theorize that RNA organizes the genome in space via a triplex-forming mechanism. To test this theory, we apply a computational modeling approach of chromosomes that combines restraint-based modeling with polymer physics. Our models suggest that colocalization of triplex hotspots targeted by lncRNAs could contribute to large-scale chromosome compartmentalization cooperating, rather than competing, with architectural transcription factors such as CTCF.

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.

RNA structure probing uncovers RNA structure-dependent biological functions

RNA molecules fold into complex structures that enable their diverse functions in cells. Recent revolutionary innovations in transcriptome-wide RNA structural probing of living cells have ushered in a new era in understanding RNA functions. Here, we summarize the latest technological advances for probing RNA secondary structures and discuss striking discoveries that have linked RNA regulation and biological processes through interrogation of RNA structures. In particular, we highlight how different long noncoding RNAs form into distinct secondary structures that determine their modes of interactions with protein partners to realize their unique functions. These dynamic structures mediate RNA regulatory functions through altering interactions with proteins and other RNAs. We also outline current methodological hurdles and speculate about future directions for development of the next generation of RNA structure-probing technologies of higher sensitivity and resolution, which could then be applied in increasingly physiologically relevant studies.

Global in situ profiling of RNA-RNA spatial interactions with RIC-seq

Emerging evidence has demonstrated that RNA-RNA interactions are vital in controlling diverse biological processes, including transcription, RNA splicing and protein translation. RNA in situ conformation sequencing (RIC-seq) is a technique for capturing protein-mediated RNA-RNA proximal interactions globally in living cells at single-base resolution. Cells are first treated with formaldehyde to fix all the protein-mediated RNA-RNA interactions in situ. After cell permeabilization and micrococcal nuclease digestion, the proximally interacting RNAs are 3' end-labeled with pCp-biotin and subsequently ligated using T4 RNA ligase. The chimeric RNAs are then enriched and converted into libraries for paired-end sequencing. After deep sequencing, computational analysis yields interaction strength scores for every base on proximally interacting RNAs in the starting populations. The whole experimental procedure is designed to be completed within 6 d, followed by an additional 8 d for computational analysis. RIC-seq technology can unbiasedly detect intra- and intermolecular RNA-RNA interactions, thereby rendering it useful for reconstructing RNA higher-order structures and identifying direct noncoding RNA targets.

Polymer modelling unveils the roles of heterochromatin and nucleolar organizing regions in shaping 3D genome organization in Arabidopsis thaliana

The 3D genome is characterized by a complex organization made of genomic and epigenomic layers with profound implications on gene regulation and cell function. However, the understanding of the fundamental mechanisms driving the crosstalk between nuclear architecture and (epi)genomic information is still lacking. The plant Arabidopsis thaliana is a powerful model organism to address these questions owing to its compact genome for which we have a rich collection of microscopy, chromosome conformation capture (Hi-C) and ChIP-seq experiments. Using polymer modelling, we investigate the roles of nucleolus formation and epigenomics-driven interactions in shaping the 3D genome of A. thaliana. By validation of several predictions with published data, we demonstrate that self-attracting nucleolar organizing regions and repulsive constitutive heterochromatin are major mechanisms to regulate the organization of chromosomes. Simulations also suggest that interphase chromosomes maintain a partial structural memory of the V-shapes, typical of (sub)metacentric chromosomes in anaphase. Additionally, self-attraction between facultative heterochromatin regions facilitates the formation of Polycomb bodies hosting H3K27me3-enriched gene-clusters. Since nucleolus and heterochromatin are highly-conserved in eukaryotic cells, our findings pave the way for a comprehensive characterization of the generic principles that are likely to shape and regulate the 3D genome in many species.

RNA matchmaking in chromatin regulation

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

RNA, Genome Output and Input

RNA, the transcriptional output of genomes, not only templates protein synthesis or directly engages in catalytic functions, but can feed back to the genome and serve as regulatory input for gene expression. Transcripts affecting the RNA abundance of other genes act by mechanisms similar to and in concert with protein factors that control transcription. Through recruitment or blocking of activating and silencing complexes to specific genomic loci, RNA and protein factors can favor transcription or lower the local gene expression potential. Most regulatory proteins enter nuclei from all directions to start the search for increased affinity to specific DNA sequences or to other proteins nearby genuine gene targets. In contrast, RNAs emerge from spatial point sources within nuclei, their encoding genes. A transcriptional burst can result in the local appearance of multiple nascent RNA copies at once, in turn increasing local nucleic acid density and RNA motif abundance before diffusion into the nuclear neighborhood. The confined initial localization of regulatory RNAs causing accumulation of protein co-factors raises the intriguing possibility that target specificity of non-coding, and probably coding, RNAs is achieved through gene/RNA positioning and spatial proximity to regulated genomic regions. Here we review examples of positional cis conservation of regulatory RNAs with respect to target genes, spatial proximity of enhancer RNAs to promoters through DNA looping and RNA-mediated formation of membrane-less structures to control chromatin structure and expression. We speculate that linear and spatial proximity between regulatory RNA-encoding genes and gene targets could possibly ease the evolutionary pressure on maintaining regulatory RNA sequence conservation.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

Proximity RNA-seq: A Sequencing Method to Identify Co-localization of RNA

RNA localization is an important regulatory layer of gene expression and cell functioning. The protocol guides through the Proximity RNA-seq method, in which RNA molecules are sequenced in their spatial, cellular context to derive RNA co-localization and transcriptome organization. Transcripts in individual subcellular particles from chemically crosslinked cells are tagged with the same, unique DNA barcode in water-in-oil emulsion droplets. First, single DNA barcodes are PCR amplified and immobilized on single, small magnetic beads in droplets. Subsequently, 3' ends of bead-bound barcode copies are tailed with random pentadecamers. Then beads are encapsulated again into droplets together with crosslinked subcellular particles containing RNA. Reverse transcription using random pentadecamers as primers is performed in droplets, which optimally contain one bead and one particle, in order to tag RNAs co-localized to the same particle. Sequencing such cDNA molecules identifies the RNA molecule and the barcode. Subsequent analysis of transcripts that share the same barcode, i.e., co-barcoding, reveals RNA co-localization and interactions. The technique is not restricted to pairs of RNAs but can as well detect groups of transcripts and estimates local RNA density or connectivity for individual transcripts. We provide here a detailed protocol to perform and analyze Proximity RNA-seq on cell nuclei to study spatial, nuclear RNA organization.

RIC-seq for global in situ profiling of RNA-RNA spatial interactions

Highly structured RNA molecules usually interact with each other, and associate with various RNA-binding proteins, to regulate critical biological processes. However, RNA structures and interactions in intact cells remain largely unknown. Here, by coupling proximity ligation mediated by RNA-binding proteins with deep sequencing, we report an RNA in situ conformation sequencing (RIC-seq) technology for the global profiling of intra- and intermolecular RNA-RNA interactions. This technique not only recapitulates known RNA secondary structures and tertiary interactions, but also facilitates the generation of three-dimensional (3D) interaction maps of RNA in human cells. Using these maps, we identify noncoding RNA targets globally, and discern RNA topological domains and trans-interacting hubs. We reveal that the functional connectivity of enhancers and promoters can be assigned using their pairwise-interacting RNAs. Furthermore, we show that CCAT1-5L-a super-enhancer hub RNA-interacts with the RNA-binding protein hnRNPK, as well as RNA derived from the MYC promoter and enhancer, to boost MYC transcription by modulating chromatin looping. Our study demonstrates the power and applicability of RIC-seq in discovering the 3D structures, interactions and regulatory roles of RNA.

RNA Conformation Capture by Proximity Ligation

RNA proximity ligation is a set of molecular biology techniques used to analyze the conformations and spatial proximity of RNA molecules within cells. A typical experiment starts with cross-linking of a biological sample using UV light or psoralen, followed by partial fragmentation of RNA, RNA-RNA ligation, library preparation, and high-throughput sequencing. In the past decade, proximity ligation has been used to study structures of individual RNAs, networks of interactions between small RNAs and their targets, and whole RNA-RNA interactomes, in models ranging from bacteria to animal tissues and whole animals. Here, we provide an overview of the field, highlight the main findings, review the recent experimental and computational developments, and provide troubleshooting advice for new users. In the final section, we draw parallels between DNA and RNA proximity ligation and speculate on possible future research directions.

RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome

We have previously developed and described a method for measuring RNA co-locations within cells, called Proximity RNA-seq, which promises insights into RNA expression, processing, storage and translation. Here, we describe transcriptome-wide proximity RNA-seq datasets obtained from human neuroblastoma SH-SY5Y cell nuclei. To aid future users of this method, we also describe and release our analysis pipeline, CloseCall, which maps cDNA to a custom transcript annotation and allocates cDNA-linked barcodes to barcode groups. CloseCall then performs Monte Carlo simulations on the data to identify pairs of transcripts, which are co-barcoded more frequently than expected by chance. Furthermore, derived co-barcoding frequencies for individual transcripts, dubbed valency, serve as proxies for RNA density or connectivity for that given transcript. We outline how this pipeline was applied to these sequencing datasets and openly share the processed data outputs and access to a virtual machine that runs CloseCall. The resulting data specify the spatial organization of RNAs and builds hypotheses for potential regulatory relationships between RNAs.

Measurement(s)	RNA • Proximity
Technology Type(s)	RNA sequencing
Factor Type(s)	biological replicate
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11627397