ArticleGenome research2014
From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing.
Article in Genome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 284 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
284 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- FMRFamide-like peptides expand the behavioral repertoire of a densely connected nervous system.Proceedings of the National Academy of Sciences of the United States of America · 2017Pooled it
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Human vault RNAs exhibit diverse expression patterns and inter-locus compensation.bioRxiv : the preprint server for biology · 2026Article
- Structural basis for RISC assembly of human Argonaute2.Molecular cell · 2026Article
- Transcriptional noise sets fundamental limits to decoding circadian clock phase from single-cell RNA snapshots.iScience · 2026Article
- When RNA goes off script: ensuring transcript fidelity in transgene expression.The EMBO journal · 2026Review
- RNA Modifications in T cell Immunity: Mechanisms, Disease Relevance, and Therapeutic Potential.Theranostics · 2026Review
- Epitranscriptomic regulation by m6A in immunity and autoimmune disorders: emerging mechanisms and clinical perspectives.Frontiers in immunology · 2026Review
- Recent advancements in tissue dissociation techniques for cell manufacturing single-cell analysis and downstream processing.Stem cells translational medicine · 2025Review
- Bioinformatics frameworks for single-cell long-read sequencing: unlocking isoform-level resolution.Briefings in bioinformatics · 2025Review
- Human and mouse platelet transcriptomes and proteomes for phenotyping 3474 genes with hemostatic and platelet traits.Blood vessels, thrombosis & hemostasis · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Landscape and regulation of mRNA translation in the early C. elegans embryo.Cell reports · 2025Article
- Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Integrating short-read and long-read single-cell RNA sequencing for comprehensive transcriptome profiling in mouse retina.Genome research · 2025Article
- Landscape and regulation of mRNA translation in the earlybioRxiv : the preprint server for biology · 2025Article
- Endothelial transcriptomic, epigenomic and proteomic data challenge the proposed role for TSAd in vascular permeability.Angiogenesis · 2025Article
- All the sites we cannot see: Sources and mitigation of false negatives in RNA modification studies.Nature reviews. Molecular cell biology · 2025Review
224 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
7 authors.
Funding
Abstract
Single-cell RNA-seq mammalian transcriptome studies are at an early stage in uncovering cell-to-cell variation in gene expression, transcript processing and editing, and regulatory module activity. Despite great progress recently, substantial challenges remain, including discriminating biological variation from technical noise. Here we apply the SMART-seq single-cell RNA-seq protocol to study the reference lymphoblastoid cell line GM12878. By using spike-in quantification standards, we estimate the absolute number of RNA molecules per cell for each gene and find significant variation in total mRNA content: between 50,000 and 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/split design and find that there are significant differences in expression between individual cells, over and above technical variation. Specific gene coexpression modules were preferentially expressed in subsets of individual cells, including one enriched for mRNA processing and splicing factors. We assess cell-to-cell variation in alternative splicing and allelic bias and report evidence of significant differences in splice site usage that exceed splice variation in the pool/split comparison. Finally, we show that transcriptomes from small pools of 30-100 cells approach the information content and reproducibility of contemporary RNA-seq from large amounts of input material. Together, our results define an experimental and computational path forward for analyzing gene expression in rare cell types and cell states.
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Registered trials
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.