The genome generates RNA molecules through transcription, which guide protein synthesis and influence chromatin and nuclear architecture. More than 98% of the mammalian genome consists of non-coding regions, which can produce diverse non-coding RNAs characterized by low expression levels and short half-lives. Studying nascent RNA is important for revealing intergenic non-coding transcription and for helping to understand the full landscape of genome regulation. From a single-cell perspective, investigating genome-wide transcription dynamics is even more valuable for elucidating the relationship between genome interpretation and cell type emergence. Given that single-cell nascent RNA sequencing is crucial for understanding how the genome drives cellular diversity, and that existing RNA-related sequencing methods have many limitations, Professor Shen Xiaohua’s team at Tsinghua University, building on SeekGene’s Universal Beads technology, developed a single-cell method called scFLUENT-seq. This method can capture genome-wide transcription information through simple metabolic labeling, and features high sensitivity and broad coverage. It helps quantitatively reveal transcriptional dynamics and regulatory heterogeneity across different chromatin regions. The team applied this technology to both normal and transcription-disrupted conditions, focusing on the effects of RNA polymerase II CTD truncation on transcription. They found that the CTD plays a key role in the production of mRNA and ncRNA, and coordinates co-transcriptional events with RNA processing. CTD truncation can alter transcriptional features, affect transcription in coding and intergenic regions, and thereby influence cell states.
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Published in Cell in September 2025, impact factor 42.5. DOI: https://doi.org/10.1016/j.cell.2025.09.003
Technical roadmap
01 Development of scFLUENT-seq
The scFLUENT-seq single-cell method was developed based on SeekGene’s innovative Universal Beads technology, enabling nascent RNA detection at single-cell resolution through EU labeling. Mixing unlabeled human 293T cells with EU-labeled mouse embryonic stem cells (ESCs) demonstrated that the method has very high specificity for enriching nascent RNA. In addition, scFLUENT-seq data showed that 69% of high-confidence reads in nascent RNA were located in intronic regions, a proportion significantly higher than that observed in old RNA and scGRO-seq data. When single-cell RNA sequencing was performed without metabolic labeling, only 25% of reads mapped to intronic regions, displaying a typical steady-state RNA distribution pattern. Analysis of scFLUENT-seq was consistent with bulk nascent RNA sequencing results and could efficiently capture promoter-associated sense and antisense signals. Genome browser tracks showed that the nascent RNA signals detected by scFLUENT-seq were more similar to CAGE (Cap Analysis of Gene Expression) profiles, with some differences from TT-seq and PRO-seq. Long-duration EU labeling experiments indicated that the nascent RNA capture efficiency was approximately 55%, and DNase control experiments confirmed that scFLUENT-seq was essentially free of genomic DNA contamination.
02 scFLUENT-seq reveals intercellular transcriptional heterogeneity
Using a engineered mouse embryonic stem cell line, Pol II CTD-AID, combined with scFLUENT-seq, we revealed the immediate effects of CTD truncation on transcription, including reduced transcription start site signals and increased intergenic RNA. UMAP clustering showed that after IAA treatment, cells could be divided into two groups, iCTL and iIAA. The iCTL cells clustered with wild-type cells at both the nascent RNA and old RNA levels and showed only a weak response to IAA, whereas the iIAA group exhibited pronounced transcriptional changes and heterogeneity, with some cells in a transitional state. scFLUENT-seq analysis showed that the median genome coverage was 0.58% in iCTL cells and 0.43% in iIAA cells, indicating reduced transcriptional activity in the latter. Considering RNA capture efficiency and stringent read filtering, only 1.5% to 3.1% of the genome was transcribed in iCTL single cells. In mouse spleen cells, nascent RNA coverage was only 0.02% to 0.06%, much lower than in ESCs, and old RNA coverage was also low, reflecting transcriptional differences across cell types. Single-cell and pseudo-bulk analyses showed that although the transcriptional coverage of individual cells was low, the aggregated data were close to the population level, highlighting strong intercellular heterogeneity and motivating a quantitative analysis of transcriptional heterogeneity between cells.
03 Annotation of genome-wide intergenic transcription units
By integrating GENCODE and RefSeq annotations, mRNAs, lncRNAs, ncRNAs, and other annotated RNAs were collectively defined as “genes.” The remaining genomic intervals were then classified according to their relative positions to genes into proximal and distal categories. The proximal group included UpG, DoG, and gLink, while the distal group was further divided by distance into near-distal (<10 kb) and far-distal (>10 kb) independent transcription units (TUs). Analysis showed that genes contributed about 84% of transcriptional coverage at the single-cell level, whereas proximal and distal intergenic transcription units accounted for a larger proportion at the population level, with gLink showing a particularly marked increase. In iIAA cells, protein-coding gene coverage decreased, but gLink coverage increased significantly, confirming the role of CTD in restricting intergenic transcription. Although intergenic transcription spans a larger genomic territory across cells, its contribution to overall transcriptional activity is much smaller than that of gene transcription within individual cells. In addition, transcriptional heterogeneity was quantified by calculating the fraction of heterogeneity, and the results showed that intergenic TUs in iIAA cells had higher heterogeneity scores, with no correlation to cell cycle stage. Overall, these results indicate that intergenic transcription exhibits broad heterogeneity under both normal and transcription-disrupted conditions.
04 High transcriptional heterogeneity in heterochromatin
Transcription in euchromatin (A compartment) and heterochromatin (B compartment) was analyzed by estimating single‑cell transcriptional coverage. The results showed that transcriptional activity in region A was about 2.7‑fold higher than that in region B; however, in pseudo‑bulk analysis, transcriptional coverage in region B increased substantially, indicating that transcription in B is more dispersed. By comparing single‑cell nascent transcriptomes with chromatin accessibility, intergenic transcription was further examined across different chromatin regions. The analysis revealed that nascent RNA is not only active in open chromatin but that distal transcription units (TUs) also exhibit transcriptional activity even in the absence of chromatin‑accessibility signals, which is slightly enhanced in iIAA cells—highlighting the high sensitivity of scFLUENT‑seq. Intergenic DNase‑hypersensitive sites (DHSs) are predominantly enriched in region A, whereas distal TUs are mainly located in region B, and the aggregated length of B‑associated feature sets is longer, consistent with the distribution of transcription units. Pseudo‑bulk analysis showed that the RNA synthesis rate in region B is lower than in region A, but the difference is much smaller at the single‑cell level, indicating high heterogeneity in intergenic TU expression within region B. CTD truncation may relax heterochromatin constraints, leading to a significant increase in intergenic RNA synthesis rate in region B in iIAA cells, revealing the highly dynamic nature of heterochromatic transcription. Overall, heterochromatin exhibits greater transcriptional heterogeneity than euchromatin, and this property is intrinsic to cells, regardless of perturbation.
05 mRNA and intergenic non‑coding RNAs display distinct dynamic features
By analyzing scFLUENT‑seq data, mRNA and ncRNA synthesis and degradation rates were estimated. The results showed that nuclear RNA degradation kinetics differ from those of total‑cell RNA, and in iIAA cells the half‑lives of both species were prolonged by 1.8‑ to 2.4‑fold compared with iCTL cells. At both single‑cell and pseudo‑bulk levels, mRNA synthesis and degradation rates were jointly reduced in iIAA cells, suggesting that global coupling maintains nuclear RNA levels; however, the correlation between synthesis and degradation was low within individual iCTL and iIAA cells. In contrast, intergenic TUs in both euchromatin and heterochromatin showed higher correlation between synthesis and degradation rates, and the coefficient of variation in synthesis and degradation rates for intergenic non‑coding RNAs was larger than for mRNAs, indicating weaker and more stochastic regulation. In iIAA cells, mRNA synthesis and degradation rates were highly correlated (R = 0.68) and shifted toward lower activity. For intergenic TUs in iIAA cells, degradation rates decreased but correlations remained unchanged, whereas the coefficient of variation for synthesis and degradation rates of protein‑coding genes increased significantly, suggesting a loss of CTD‑dependent regulatory mechanisms.
06 Local and global effects of non‑coding RNA transcription
To explore the local interaction between protein‑coding and non‑coding transcription, antisense RNAs within 500 bp upstream of gene transcription start sites (TSSs) were defined as “PROMPTs,” while sense transcription at the TSS was referred to as the gene’s “TSS” signal. Pseudo‑bulk analysis revealed a weak positive correlation between PROMPTs and their paired TSS transcripts (R = 0.18), together with a low co‑expression probability, indicating that the two rarely occur together and may act in a mutually suppressive manner. Moreover, the co‑expression probability of PROMPT–TSS pairs was negatively correlated with TSS expression level (R = –0.33), suggesting that active transcription at the TSS may repress PROMPT expression and that the limited co‑expression observed likely reflects random behavior. In contrast, upstream genes and their paired gLink elements showed strong positive correlation, and co‑expression probability increased with gene expression level, indicating gene‑activity‑dependent transcriptional read‑through. Taken together, these findings suggest that RNA polymerase II (Pol II) engaged in transcription preferentially moves in one direction, and this directionality is further reinforced by CTD‑mediated processes. Proximal gLink activity was highly correlated with gene transcription, whereas distal TUs showed weaker correlations. In iIAA cells, two clusters were identified, with the top cluster exhibiting higher transcriptional diversity and higher coverage of distal TUs, suggesting that these subpopulations possess greater plasticity.
07 Capturing key state transitions using nascent transcriptional diversity
Finally, the study examined how transcription within coding regions and intergenic regions influences cell plasticity during state transitions. After two rounds of IAA treatment and washout, gene expression was found to change rapidly during perturbation, whereas intergenic transcription units underwent gradual and overlapping transitions, indicating that intergenic transcription is more stable. Using CytoTRACE, an algorithm for predicting cell fate plasticity, analysis showed that the coding‑region score dropped sharply after IAA treatment and then rebounded, whereas the intergenic‑region score steadily increased. The trajectory of the coding‑region score closely mirrored that of the combined score integrating both coding and intergenic regions, revealing that transcription from coding regions dominates cell plasticity. Three hours after IAA washout, wild‑type Pol II was re‑expressed, partial transcriptional activity was restored, and cells exhibited transiently maximal transcriptional diversity; however, pseudotemporal analysis based on expression levels did not show a significant cell‑state transition. Overall, coding‑region transcription fluctuates rapidly between cell states, whereas intergenic transcription is more continuous, buffering changes in gene expression and supporting both cellular stability and plastic sanity. This interaction between coding‑region and intergenic transcription reveals that cell plasticity can emerge before major reprogramming of gene expression programs and can form metastable states that facilitate state transitions.
Summary
This study developed a new single‑cell sequencing method, scFLUENT‑seq, which captures nascent and mature transcriptomes with extremely high sensitivity and genome‑wide coverage, revealing the dynamics and heterogeneity of the transcriptome at single‑cell resolution. The results show that functional genomic regions actively used per single cell amount to less than 3.1% of the genome, far below population‑level estimates, and that stochastic transcription in intergenic regions and heterochromatin substantially enhances cell plasticity and diversity. Transcriptional heterogeneity is widespread across the genome, and the coordination of RNA synthesis and degradation is dependent on the Pol II CTD, underscoring its central role in maintaining transcriptional homeostasis.
Learn about SeekGene's scFAST-seq solution: https://www.seekgene.com/Product/scFAST-seq.html
#MammalianGenomics #NonCodingRNA #GenomicRegulation #SingleCellSequencing #scFLUENTseq #UniversalBeadsTech #TranscriptionDynamics #ChromatinInsights #RNARegulation #CellStatePlasticity #TechnicalInnovation #DiseaseResearch #RegenerativeMedicine #DrugDiscovery #SeekGene #TsinghuaUniversity #ShenXiaohuaTeam #MetabolicLabeling #Heterochromatin #Euchromatin