Imaging Organization of RNA Processing within the Nucleus

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Real-time single-molecule imaging of transcriptional regulatory networks in living cells

Gene regulatory networks drive the specific transcriptional programmes responsible for the diversification of cell types during the development of multicellular organisms. Although our knowledge of the genes involved in these dynamic networks has expanded rapidly, our understanding of how transcription is spatiotemporally regulated at the molecular level over a wide range of timescales in the small volume of the nucleus remains limited. Over the past few decades, advances in the field of single-molecule fluorescence imaging have enabled real-time behaviours of individual transcriptional components to be measured in living cells and organisms. These efforts are now shedding light on the dynamic mechanisms of transcription, revealing not only the temporal rules but also the spatial coordination of underlying molecular interactions during various biological events.

Multiplexed Immunofluorescence and Single-Molecule RNA Fluorescence In Situ Hybridization in Mouse Skeletal Myofibers

RNA fluorescence in situ hybridization (FISH) is a powerful method to determine the abundance and localization of mRNA molecules in cells. While modern RNA FISH techniques allow quantification at single molecule resolution, most methods are optimized for mammalian cell culture and are not easily applied to in vivo tissue settings. Single-molecule RNA detection in skeletal muscle cells has been particularly challenging due to the thickness and high autofluorescence of adult muscle tissue and a lack of in vitro models for mature muscle cells (myofibers). Here, we present a method for isolation of adult myofibers from mouse skeletal muscle and detection of single mRNA molecules and proteins using multiplexed RNA FISH and immunofluorescence.

Inferring stochastic rates from heterogeneous snapshots of particle positions

Many imaging techniques for biological systems - like fixation of cells coupled with fluorescence microscopy - provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Following the Birth, Life, and Death of mRNAs in Single Cells

Recent advances in single-molecule imaging of mRNAs in fixed and living cells have enabled the lives of mRNAs to be studied with unprecedented spatial and temporal detail. These approaches have moved beyond simply being able to observe specific events and have begun to allow an understanding of how regulation is coupled between steps in the mRNA life cycle. Additionally, these methodologies are now being applied in multicellular systems and animals to provide more nuanced insights into the physiological regulation of RNA metabolism.

Introns and Their Therapeutic Applications in Biomedical Researches

Context

Although for a long time, it was thought that intervening sequences (introns) were junk DNA without any function, their critical roles and the underlying molecular mechanisms in genome regulation have only recently come to light. Introns not only carry information for splicing, but they also play many supportive roles in gene regulation at different levels. They are supposed to function as useful tools in various biological processes, particularly in the diagnosis and treatment of diseases. Introns can contribute to numerous biological processes, including gene silencing, gene imprinting, transcription, mRNA metabolism, mRNA nuclear export, mRNA localization, mRNA surveillance, RNA editing, NMD, translation, protein stability, ribosome biogenesis, cell growth, embryonic development, apoptosis, molecular evolution, genome expansion, and proteome diversity through various mechanisms.

Evidence Acquisition

In order to fulfill the objectives of this study, the following databases were searched: Medline, Scopus, Web of Science, EBSCO, Open Access Journals, and Google Scholar. Only articles published in English were included.

Results & Conclusions

The intervening sequences of eukaryotic genes have critical functions in genome regulation, as well as in molecular evolution. Here, we summarize recent advances in our understanding of how introns influence genome regulation, as well as their effects on molecular evolution. Moreover, therapeutic strategies based on intron sequences are discussed. According to the obtained results, a thorough understanding of intron functional mechanisms could lead to new opportunities in disease diagnosis and therapies, as well as in biotechnology applications.

Dynamics of nuclear architecture during early embryonic development and lessons from liveimaging

Nuclear organization has emerged as a potential key regulator of genome function. During development, the deployment of transcriptional programs must be tightly coordinated with cell division and is often accompanied by major changes in the repertoire of expressed genes. These transcriptional and developmental events are paralleled by changes in the chromatin landscape. Numerous studies have revealed the dynamics of nuclear organization underlying them. In addition, advances in live-imaging-based methodologies enable the study of nuclear organization with high spatial and temporal resolution. In this Review, we summarize the current knowledge of the changes in nuclear architecture in the early embryogenesis of various model systems. Furthermore, to highlight the importance of integrating fixed-cell and live approaches, we discuss how different live-imaging techniques can be applied to examine nuclear processes and their contribution to our understanding of transcription and chromatin dynamics in early development. Finally, we provide future avenues for outstanding questions in this field.

Research progress of live-cell RNA imaging techniques

RNA molecules play diverse roles in many physiological and pathological processes as they interact with various nucleic acids and proteins. The various biological processes of RNA are highly dynamic. Tracking RNA dynamics in living cells is crucial for a better understanding of the spatiotemporal control of gene expression and the regulatory roles of RNA. Genetically encoded RNA-tagging systems include MS2/MCP, PP7/PCP, boxB/λN22 and CRISPR-Cas. The MS2/MCP system is the most widely applied, and it has the advantages of stable binding and high signal-to-noise ratio, while the realization of RNA imaging requires gene editing of the target RNA, which may change the characteristics of the target RNA. Recently developed CRISPR-dCas13 system does not require RNA modification, but the uncertainty in CRISPR RNA (crRNA) efficiency and low signal-to-noise ratio are its limitations. Fluorescent dye-based RNA-tagging systems include molecular beacons and fluorophore-binding aptamers. The molecular beacons have high specificity and high signal-to-noise ratio; Mango and Peppers outperform the other RNA-tagging system in signal-to-noise, but they also need gene editing. Live-cell RNA imaging allows us to visualize critical steps of RNA activities, including transcription, splicing, transport, translation (for message RNA only) and subcellular localization. It will contribute to studying biological processes such as cell differentiation and the transcriptional regulation mechanism when cells adapt to the external environment, and it improves our understanding of the pathogenic mechanism of various diseases caused by abnormal RNA behavior and helps to find potential therapeutic targets. This review provides an overview of current progress of live-cell RNA imaging techniques and highlights their major strengths and limitations.

Bromodomains regulate dynamic targeting of the PBAF chromatin-remodeling complex to chromatin hubs

Chromatin remodelers actively target arrays of acetylated nucleosomes at select enhancers and promoters to facilitate or shut down the repeated recruitment of RNA polymerase II during transcriptional bursting. It is poorly understood how chromatin remodelers such as PBAF dynamically target different chromatin states inside a live cell. Our live-cell single-molecule fluorescence microscopy study reveals chromatin hubs throughout the nucleus where PBAF rapidly cycles on and off the genome. Deletion of PBAF's bromodomains impairs targeting and stable engagement of chromatin in hubs. Dual color imaging reveals that PBAF targets both euchromatic and heterochromatic hubs with distinct genome-binding kinetic profiles that mimic chromatin stability. Removal of PBAF's bromodomains stabilizes H3.3 binding within chromatin, indicating that bromodomains may play a direct role in remodeling of the nucleosome. Our data suggests that PBAF's dynamic bromodomain-mediated engagement of a nucleosome may reflect the chromatin-remodeling potential of differentially bound chromatin states.

Targeted RNA editing: novel tools to study post-transcriptional regulation

RNA binding proteins (RBPs) regulate nearly all post-transcriptional processes within cells. To fully understand RBP function, it is essential to identify their in vivo targets. Standard techniques for profiling RBP targets, such as crosslinking immunoprecipitation (CLIP) and its variants, are limited or suboptimal in some situations, e.g. when compatible antibodies are not available and when dealing with small cell populations such as neuronal subtypes and primary stem cells. This review summarizes and compares several genetic approaches recently designed to identify RBP targets in such circumstances. TRIBE (targets of RNA binding proteins identified by editing), RNA tagging, and STAMP (surveying targets by APOBEC-mediated profiling) are new genetic tools useful for the study of post-transcriptional regulation and RBP identification. We describe the underlying RNA base editing technology, recent applications, and therapeutic implications.