This is the second article in our “Innovations in Transcriptomics” series! In our first article, we explored how CAST (Cross-Sample Alignment of Spatial Omics) is transforming the landscape of spatial transcriptomics, providing powerful tools to integrate and analyze complex datasets at single-cell resolution. CAST is opening up new avenues to explore the intricate architecture of tissues, revealing disease mechanisms, and offering insights into cellular interactions. However, as we dive deeper into understanding gene expression, it’s clear that the dynamics of transcription itself—especially the temporal and spatial regulation of gene activity—remain key to unlocking many biological mysteries.
This is where a cutting-edge technique for profiling nascent RNA at single-cell resolution comes into play. In this article, we’ll dive into how new techniques are transforming our understanding of gene regulation by capturing the nuances of transcriptional dynamics.
Introduction
Transcription, the process of creating RNA from DNA, occurs in bursts of activity interspersed with silent periods. These bursts are essential for regulating gene expression, yet the mechanisms that trigger and control them remain unclear. Traditional sequencing methods obscure this variability by averaging data across many cells, leaving a significant gap in understanding how transcription functions during processes like the cell cycle, development, and disease progression.
Enhancers, distant DNA regions that regulate genes, add another layer of complexity. These elements are cell-type and state-specific, making them crucial for driving precise gene activity. Genome-wide studies reveal that most disease-linked genetic variants lie in these non-coding regions, emphasizing their importance. However, directly linking enhancers to their target genes has been difficult, as existing methods provide only indirect evidence of their interactions.
scGRO-seq, a single-cell Global Run-On sequencing method introduced by Mahat et al. in Nature, addresses these limitations. By capturing nascent RNA with high precision, scGRO-seq uncovers details about transcriptional bursts, enhancer activity, and gene regulation during different cell cycle phases. This method fills critical gaps in our understanding of transcriptional regulation and paves the way for developing therapies targeting enhancer-driven diseases.
Designing scGRO-seq: A New Era in Single-Cell Nascent RNA Profiling
Capturing nascent RNA at the single-cell level poses unique challenges, as it lacks a poly(A) tail for barcoding and is masked by abundant total RNA. Traditional methods like GRO-seq and PRO-seq average transcription across populations, making them unsuitable for resolving single-cell dynamics. To address this, intact-nuclei AGTuC (inAGTuC) was developed, using click chemistry to selectively label nascent RNA in intact nuclei during a nuclear run-on reaction. This approach preserves nuclear integrity, allowing for precise enrichment of nascent RNA.
Building on inAGTuC, scGRO-seq was designed to profile nascent RNA at single-cell resolution. Individual nuclei were sorted into 96-well plates, where nascent RNA was barcoded using CuAAC and pooled for sequencing. scGRO-seq achieved high efficiency and strong concordance with bulk methods like PRO-seq while offering the advantage of single-cell resolution.
scGRO-seq effectively captured transcription from genes and enhancers, providing detailed insights into transcriptional activity. Although less efficient at detecting paused polymerase transcripts, it demonstrated superior accuracy compared to scRNA-seq in identifying nascent RNA. This innovative method enables the study of transcriptional regulation with unprecedented precision and scalability.
From Burst Size to Frequency: Decoding Transcription with scGRO-seq
Transcriptional bursts, short episodes of RNA polymerase activity, are central to gene regulation. Existing techniques, such as live-cell imaging and intron seqFISH, are constrained by predefined targets or assumptions about transcription dynamics. scGRO-seq addresses these limitations by providing genome-wide, single-nucleotide resolution, enabling direct identification of bursts without prior assumptions.
Using scGRO-seq, closely spaced RNA polymerases (multiplets) were detected more often than expected by random chance, confirming burst behavior. Measured directly, burst size typically ranged from 1 to 4 polymerases, with an average size of 1.23 and a 2-hour interval between bursts. These findings align with previous studies and validate scGRO-seq’s accuracy.
The method also uncovered regulatory influences on bursts. Genes with TATA promoters exhibited larger bursts but lower frequencies, while transcription factors played distinct roles: MYC prolonged activity to increase burst size, and AFF4 enhanced initiation frequency. These insights highlight scGRO-seq’s ability to elucidate detailed transcriptional mechanisms and dynamics.
Temporal Insights into Cell Cycle and Gene Interactions Using scGRO-seq
Determining cell cycle phases is critical for understanding gene regulation and cellular function. Traditional scRNA-seq methods, which rely on mature, polyadenylated RNA, fail to capture replication-dependent histone genes transcribed exclusively during the S phase, introducing delays in transcription detection. scGRO-seq overcomes this limitation by detecting active transcription of histone genes, enabling accurate classification of S-phase cells. Additionally, genes specific to G1/S and G2/M phases were used to classify cells into transcriptionally distinct clusters. Analysis revealed a 40% reduction in transcription during the S phase and recovery during the G2/M phase, demonstrating dynamic regulation throughout the cell cycle.
scGRO-seq also identified co-transcription of functionally related genes within a 4-minute detection window. Approximately 0.7% of gene pairs were co-transcribed, with enriched modules linked to processes like cell cycle regulation, RNA splicing, and DNA repair. This coordination likely stems from shared regulatory factors or chromosomal clustering. Promoter analysis highlighted transcription factor motifs, such as FOXO3, associated with cell cycle progression, showcasing scGRO-seq’s ability to reveal temporal gene transcription patterns.
Conclusion
scGRO-seq represents a transformative leap in single-cell transcriptional analysis, providing precise insights into the timing and coordination of gene expression. By capturing nascent RNA, it enables direct observation of transcriptional dynamics such as bursts, cell cycle-dependent activity, and enhancer-gene interactions, overcoming the limitations of traditional methods.
scGRO-seq offers a powerful tool for identifying transcriptional dysregulation in diseases such as cancer, neurodevelopmental disorders, and metabolic conditions. By revealing the precise timing and coordination of gene activity, it enables the discovery of new therapeutic targets and diagnostic markers. As technology advances, scGRO-seq has the potential to transform our understanding of gene regulation and its role in disease.
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From pipeline development and software engineering to deploying your existing bioinformatic tools, BI can help you on every step of your research journey. As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Click here to schedule a free introductory call with a member of our bioinformatics service provider (BSP) team.
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- SCORPION: Enhancing Population-Level Gene Regulatory Network Analysis with Single-Cell Data
- Revolutionizing Cancer Treatment with AI: How PERCEPTION Uses Single-Cell Sequencing Data to Predict Patient Outcomes
