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Single-cell nascent transcription is sparse and heterogeneous, revealing cellular plasticity [single_cell]

ABSTRACT: Single-cell nascent RNA sequencing is essential for understanding how a genome drives cell diversity. We developed scFLUENT-seq, a single-cell method that captures genome-wide transcription with brief metabolic labeling. Our analysis shows that only 3~6% of the genome is transcribed per cell in a 10-minute window, compared to over 80% in bulk, revealing significant variability in how individual cells interpret the genome. Notably, substantial transcription occurs in intergenic regions, particularly in heterochromatin, with high stochasticity. Moreover, promoter-associated antisense and genic sense transcription rarely co-occur in the same cell. Distal intergenic transcription correlates poorly with gene activity but links to increased genome-wide transcriptional diversity, which marks cellular plasticity and may precede cell-state shifts. Furthermore, mRNA synthesis and decay are uncoupled at the single-cell level, unlike intergenic ncRNA, suggesting specialized mechanisms counteracting stochastic noncoding production. In summary, scFLUENT-seq captures the full transcriptional spectrum, revealing the heterogeneity and regulatory complexity underlying cellular plasticity.

ORGANISM(S): Mus musculus

PROVIDER: GSE278777 | GEO | 2024/10/09

REPOSITORIES: GEO

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Project description:A single cell RNA sequencing atlas of the Arabidopsis root distinguishes cells both by developmental fate and time, revealing defining expression features that depict a complex cascade of developmental progressions from stem cell through differentiation supported by mirroring waves of transcription factor expression. Methods: mRNA profiles of 6-day-old wild-type (WT) and shortroot-knockout Arabidopsis thaliana roots were generated by deep sequencing of single cell (all) and bulk RNA libraries (wild type only), in duplicate (bulk & wild-type single cell) and singlicate (shr-3), using Illumina NextSeq. The sequence reads that passed quality filters were analyzed at the transcript level. Single Cell libraries were processed and analysed using Cell Ranger, STAR, Seurat. Bulk libraries were processed using Trimmomatic, STAR, HTSeq and DEseq2. Results: For Single Cell - Using an optimized data analysis workflow, we mapped ~87k sequence reads per wild-type cell to the Arabidopsis genome (TAIR10) and identified a median of 4,276 genes and 14,758 transcripts per cell. In total, transcripts for 16,975 genes were detected (RPM ≥1) from wild-type cells. After correction for read depth, this represents ~90% of genes detected by bulk RNA-seq of protoplasted root tissue. Bulk RNA-seq data identified genes induced by protplasting of the Arabidopsis root which could be discounted from single cell analysis. The global gene expression profiles of pooled scRNA-seq and bulk RNA-seq are highly correlated (r = 0.9) indicating that plant scRNA-seq is highly sensitive. Conclusions: Our high-resolution single cell RNA sequencing atlas of the Arabidopsis root captures precise temporal information for all major cell types, revealing new regulators and defining features foreach. Developmental trajectories derived from pseudotime analysis depict a finely resolved cascade of developmental progressions between stem cell and final differentiation supported by mirroring waves of transcription factor expression.

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Single-cell nascent transcription is sparse and heterogeneous, revealing cellular plasticity [single_cell]

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