Rice Research: Open Access
Open Access

RNA sequencing; Transcriptomics; Precision medicine; Single-cell RNA sequencing; Spatial transcriptomics; Long-read sequencing; Bioinformatics; Oncology; Neurodevelopmental disorders; Circular RNA

Introduction

RNA sequencing (RNA-seq) stands as a foundational technology in modern biology, providing an exhaustive view of the transcriptome with profound implications for research and clinical applications. This powerful technique has evolved, moving from bulk analysis to high-resolution single-cell and spatially resolved methods, continuously expanding our understanding of complex biological systems. It helps researchers decipher cellular heterogeneity, identify disease mechanisms, discover biomarkers, and develop targeted therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) offers a high-resolution view of cellular heterogeneity. Integrating scRNA-seq with other multi-omics data, like ATAC-seq or proteomics, is becoming crucial. This integration helps researchers identify disease mechanisms, discover biomarkers, and develop personalized treatment strategies, moving precision medicine forward by providing a more comprehensive understanding of complex biological systems [1].

Bulk RNA sequencing, while not single-cell resolution, remains a powerful tool for understanding overall gene expression profiles in tissues. This particular study used it to uncover distinct molecular differences between dorsal and ventral regions of the human developing spinal cord. These insights are key for understanding spinal cord development and potential therapeutic targets for neurological disorders, demonstrating the utility of bulk RNA-seq for broad transcriptional analysis [2].

RNA sequencing is proving invaluable in clinical oncology, extending beyond mere research. It helps identify critical driver alterations in tumors, allows for more precise tumor classification, and ultimately guides treatment decisions in precision oncology. This approach moves us closer to tailoring therapies specifically to an individual's cancer profile, improving patient outcomes by leveraging detailed molecular information [3].

In plant science, RNA sequencing is fundamental for understanding how plants respond to environmental stresses. This work demonstrates its utility in pinpointing the specific gene expression changes in rice plants that enable them to tolerate salinity. Discovering these transcriptional signatures is key for developing more resilient crop varieties, which is vital for food security in challenging agricultural environments [4].

RNA sequencing provides a high-resolution lens into the complex interactions between hosts and pathogens. This study leveraged it to analyze the host-pathogen interplay during Respiratory Syncytial Virus (RSV) infection. By revealing dynamic gene expression changes in both the host and the virus, RNA-seq offers critical insights into disease mechanisms and potential targets for antiviral therapies, which is vital for combating infectious diseases [5].

While RNA sequencing generates a wealth of data, its analysis presents significant bioinformatics challenges, from data normalization and batch effects to differential expression and interpretation. This paper discusses current hurdles and outlines innovative computational solutions designed to overcome them. Addressing these challenges is essential for accurate biological interpretation and for maximizing the utility of RNA-seq in research and clinical applications [6].

Spatial transcriptomics, an exciting advancement in RNA sequencing, adds a crucial dimension to traditional gene expression studies by preserving the spatial context of gene activity within tissues. This method allows researchers to map where genes are expressed, providing unprecedented insights into tissue architecture, cellular interactions, and disease progression. It's truly transforming our understanding of biological systems by showing where things happen, not just what happens [7].

Long-read RNA sequencing, unlike its short-read counterpart, can sequence full-length RNA transcripts. This capability opens doors to discovering novel isoforms, alternative splicing events, and gene fusion transcripts that are often missed by short-read approaches. While it brings significant opportunities for a more complete picture of the transcriptome, researchers still face challenges in data processing and error correction, which are actively being addressed to harness its full potential [8].

RNA sequencing has become an indispensable tool for unraveling the genetic and molecular underpinnings of complex neurodevelopmental disorders. By profiling gene expression patterns in affected brain regions or relevant cell types, researchers can identify aberrant pathways, novel disease-causing genes, and potential therapeutic targets. This technique is critical for gaining a deeper understanding of these challenging conditions and ultimately developing effective interventions [9].

Circular RNAs (circRNAs), once thought to be transcriptional noise, are now recognized as important regulators in various biological processes. RNA sequencing has been pivotal in their discovery, identification, and functional characterization. Researchers are using RNA-seq to pinpoint novel circRNAs, annotate their features, and investigate their roles, revealing a new layer of gene regulation with implications for disease mechanisms and biomarker discovery [10].

Description

RNA sequencing (RNA-seq) has revolutionized how scientists investigate gene expression, providing a detailed molecular snapshot of biological systems. This technology offers diverse applications, from high-resolution single-cell studies to broad transcriptional profiling of entire tissues, proving invaluable across various domains of biological and medical research. For instance, single-cell RNA sequencing (scRNA-seq) combined with other multi-omics data, like ATAC-seq, provides a comprehensive view of cellular heterogeneity. This integrated approach is crucial for pinpointing disease mechanisms, discovering biomarkers, and developing personalized treatment strategies, driving progress in precision medicine [1]. While scRNA-seq offers granular detail, bulk RNA sequencing continues to be a robust method for assessing overall gene expression profiles. It has been effectively utilized to distinguish molecular differences between dorsal and ventral regions of the human developing spinal cord, yielding critical insights into spinal cord development and potential therapeutic targets for neurological disorders [2].

Beyond basic research, the clinical utility of RNA-seq is increasingly recognized, especially in oncology. It facilitates the identification of critical driver alterations in tumors, enabling more precise tumor classification. This capability directly informs treatment decisions in precision oncology, allowing for therapies tailored to an individual patient's cancer profile, ultimately improving outcomes through detailed molecular information [3]. The technique's versatility extends to understanding complex biological interactions, such as those between hosts and pathogens. Studies employing RNA-seq have meticulously analyzed host-pathogen interplay during Respiratory Syncytial Virus (RSV) infection, uncovering dynamic gene expression changes in both the host and the virus. These insights are vital for deciphering disease mechanisms and identifying potential targets for antiviral therapies, which is essential in the ongoing fight against infectious diseases [5].

RNA-seq also plays a fundamental role in fields like plant science, where it illuminates how plants respond to environmental stressors. Research has demonstrated its power in identifying the transcriptional signatures that confer salinity tolerance in rice plants. This discovery is instrumental for developing resilient crop varieties, which is a key factor in ensuring food security in challenging agricultural environments globally [4]. Furthermore, the technology is indispensable for unraveling the genetic and molecular underpinnings of complex neurodevelopmental disorders. By profiling gene expression patterns in affected brain regions or relevant cell types, researchers can identify aberrant pathways, novel disease-causing genes, and potential therapeutic targets, leading to a deeper understanding and more effective interventions for these conditions [9].

Recent advancements in RNA sequencing technologies have expanded its capabilities significantly. Spatial transcriptomics, for example, adds a vital spatial context to gene activity within tissues, moving beyond simply knowing what genes are expressed to understanding where they are expressed. This innovation provides unprecedented insights into tissue architecture, cellular interactions, and disease progression, truly transforming our understanding of biological systems [7]. Similarly, long-read RNA sequencing allows for sequencing full-length RNA transcripts, revealing novel isoforms, alternative splicing events, and gene fusion transcripts that often elude short-read methods. While this offers a more complete picture of the transcriptome, researchers are actively addressing challenges in data processing and error correction to fully exploit its potential [8]. Moreover, RNA-seq has been crucial in the discovery and characterization of circular RNAs (circRNAs), now recognized as important regulators in biological processes, offering a new layer of gene regulation with implications for disease mechanisms and biomarker discovery [10].

However, the wealth of data generated by RNA sequencing presents considerable bioinformatics challenges. Issues such as data normalization, batch effects, and accurate differential expression analysis require sophisticated computational solutions. Addressing these hurdles is paramount for reliable biological interpretation and for maximizing the utility of RNA-seq in both research and clinical settings [6]. The continuous evolution of RNA-seq technologies and analytical methods underscores its enduring importance as a tool for advancing our knowledge across a broad spectrum of biological questions and clinical needs.

Conclusion

RNA sequencing (RNA-seq) has become a cornerstone technology across diverse biological fields, offering unparalleled insights into gene expression. From detailed single-cell analysis that integrates with multi-omics data for precision medicine [1] to broad transcriptional profiling in tissues like the developing human spinal cord [2], RNA-seq continues to advance our understanding. Its clinical utility is clear in oncology, where it helps identify driver alterations and guide personalized treatments [3]. Beyond human health, RNA-seq is vital in plant science, revealing genetic responses to environmental stresses like salinity in rice [4], and in studying host-pathogen interactions during infections such as with Respiratory Syncytial Virus (RSV) [5]. The technology isn't without its complexities; significant bioinformatics challenges in data analysis, including normalization and batch effects, are actively being addressed with innovative computational solutions [6]. Emerging advancements like spatial transcriptomics add a crucial dimension by preserving the spatial context of gene activity, transforming our view of tissue architecture and disease progression [7]. Long-read RNA sequencing further enriches this by discovering full-length transcripts, novel isoforms, and gene fusions often missed by shorter reads, despite ongoing data processing challenges [8]. RNA-seq is also essential for unraveling the molecular basis of neurodevelopmental disorders, identifying aberrant pathways and therapeutic targets [9]. Moreover, it has been pivotal in characterizing circular RNAs (circRNAs), once overlooked, now recognized as important regulators in biological processes, opening new avenues for biomarker discovery and understanding gene regulation [10]. This collective application highlights RNA-seq's transformative impact on research, diagnosis, and therapeutic development.

References

1. Mengying S, Qing-Xiang M, Zihan S (2023) Single-cell RNA sequencing-based multi-omics data integration for precision medicine. Cell Biol Toxicol 39:1793-1808.

   Indexed at, Google Scholar, CrossRef

2. Meng-Wei W, Yi-Long L, Jian-Feng H (2022) Bulk RNA-sequencing of the human developing spinal cord reveals molecular distinction of dorsal and ventral regions. Front Cell Dev Biol 10:873646.

   Indexed at, Google Scholar, CrossRef

3. Kevin LS, Patrick RA, Joshua ER (2024) Clinical utility of RNA sequencing to identify driver alterations, classify tumors, and guide precision oncology. Cancer Gene Ther 31:460-470.

   Indexed at, Google Scholar, CrossRef

4. Gaby CLH, Sara KJ, Javier PG (2021) Using RNA sequencing to identify the transcriptional signature of rice plants tolerant to salinity stress. BMC Plant Biol 21:70.

   Indexed at, Google Scholar, CrossRef

5. Jie D, Yu Z, Xiaojun X (2023) RNA-sequencing analysis identifies host-pathogen interplay in respiratory syncytial virus infection. Pathog Dis 81:ftad026.

   Indexed at, Google Scholar, CrossRef

6. Yu-Han W, Jian L, Kai W (2022) Current Challenges and Solutions in RNA-Sequencing Data Analysis. Front Genet 12:807525.

   Indexed at, Google Scholar, CrossRef

7. Qian L, Hongyun W, Yuanjie Z (2024) Spatial transcriptomics: A new dimension to transcriptomic analysis. Cell Biosci 14:16.

   Indexed at, Google Scholar, CrossRef

8. Fan Z, Dongdong Y, Jiaqi L (2023) Opportunities and challenges of long-read RNA sequencing. Comput Struct Biotechnol J 21:1238-1249.

   Indexed at, Google Scholar, CrossRef

9. Yu-Jen C, Yu-Ting L, I-Ching C (2021) RNA-Seq for Understanding Neurodevelopmental Disorders. Biomedicines 9:1511.

   Indexed at, Google Scholar, CrossRef

10. Yan L, Jingyi X, Xiaoxiao L (2020) Recent Advances in circRNA Identification, Annotation, and Function. J Mol Biol 432:3799-3806.

    Indexed at, Google Scholar, CrossRef