ArticleProceedings of the National Academy of Sciences of the United States of America2021
Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed, 99 citations in OpenAlex.
- Single cell analysis of developing Merkel cells reveals the emergence of non-coding RNA biotypes as a hallmark of terminal differentiation.Cell death and differentiation · 2026Article
- VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.Nucleic acids research · 2026Article
- Spatial transcriptomics maps host-gut microbiome biogeography at high resolution.Nature microbiology · 2026Article
- sc-rDSeq: a robust and cost-effective full-length total RNA sequencing method for single cells reveals multilayered heterogeneity in drug-resistant lung cancer cells.Nucleic acids research · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- Scalable single-cell total RNA sequencing unifies coding and noncoding transcriptomics.Nature biotechnology · 2026Article
- Tabula Sapiens reveals the non-coding RNA landscape across 22 human organs and tissues.bioRxiv : the preprint server for biology · 2026Article
- A practical guide to targeted single-cell RNA sequencing technologies.Communications biology · 2026Review
- bayesReact: expression-coupled regulatory motif analysis detects microRNA activity across cancers, tissues, and at the single-cell level.Nucleic acids research · 2026Article
- Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells.Genome research · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Review
- Comparative transcriptomic analysis of embryonic stem cells across mammalian species.iScience · 2026Article
- ASTRO: Automated Spatial-Transcriptome whole RNA Output.Bioinformatics (Oxford, England) · 2026Article
- Enhanced MiRISC expression noise reduction by self-feedback regulation of mRNA degradation.Computational and structural biotechnology journal · 2026Article
- Innovative Single-Cell Sequencing Techniques for B cell Analysis and Their Implications for Rational HIV-1 Vaccine Design.Current HIV research · 2026Review
- sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method.Biology methods & protocols · 2026Review
- Cell type inference in cell-free nucleic acid liquid biopsy.Nature biotechnology · 2025Review
- Wide-spectrum profiling of plasma cell-free RNA and the potential for health-monitoring.RNA biology · 2025Article
- Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. However, current single-cell RNA-sequencing (RNA-seq) methods are unable to simultaneously monitor all forms of RNA transcripts at the single-cell level, and thus deliver only a partial snapshot of the cellular RNAome. Here we describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and noncoding RNA from a single cell. Smart-seq-total does not require splitting the RNA content of a cell and allows the incorporation of unique molecular identifiers into short and long RNA molecules for absolute quantification. It outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, thus enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T, and MCF7 cells, as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. By analyzing the coexpression patterns of both noncoding RNA and mRNA from the same cell, we were able to discover new roles of noncoding RNA throughout essential processes, such as cell cycle and lineage commitment during embryonic development. Moreover, we show that independent classes of short-noncoding RNA can be used to determine cell-type identity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.