ArticleNature protocols2018
Exponential scaling of single-cell RNA-seq in the past decade.
Article in Nature protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 523 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
523 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Optimized summary-statistic-based single-cell eQTL meta-analysis.Scientific reports · 2025Pooled it
- PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas.BMC genomics · 2024Pooled it
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Folate deficiency disrupts key metabolic transitions within the developing neural ectoderm.bioRxiv : the preprint server for biology · 2026Article
- LIGER2: Scalable Single-Cell Integration with On-Disk Datasets.bioRxiv : the preprint server for biology · 2026Article
- Nrsn1-Smarcc1 Coupling Regulates Neural Stem Cell Differentiation and Chronic-phase Recovery After Ischemic Stroke.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identification and functional validation of lipid droplet-associated prognostic biomarkers in breast cancer via integrative multi-omics and machine learning approaches.International journal of clinical oncology · 2026Article
- Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.Journal of the National Cancer Institute · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- Plant Spatio-Temporal Integration Network (PSTN): a framework for dynamic single-cell and spatial transcriptomics in fungal-plant interactions.Bioinformatics (Oxford, England) · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Increasing usable reads in RNA-seq protocols.iScience · 2026Article
- RareCapsNet: An explainable capsule network enables robust discovery of rare cell populations from large-scale single-cell transcriptomics.PLoS computational biology · 2026Article
- Article
- The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.Cell genomics · 2026Article
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Benchmarking AI scientists for omics data-driven biological discovery.Bioinformatics (Oxford, England) · 2026Article
- Deep Cellular and Spatial Profiling of the Mouse Spinal Cord Reveals Sex-Specific Neuron Types and the Ascending Projection Neuron Repertoire.bioRxiv : the preprint server for biology · 2026Article
- Single-cell T-cell landscape in atherosclerosis: implications for targeted therapy and beyond.Journal of translational medicine · 2026Review
463 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.
Indexed as
Identifiers
29494575What OpenQuestion holds
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.