ReviewCurrent opinion in biotechnology2016
Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method.
Review in Current opinion in biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Systems Biology of the Cancer Cell.Annual review of biomedical engineering · 2025Review
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.PLoS computational biology · 2024Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.bioRxiv : the preprint server for biology · 2024Article
- Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine.Expert review of proteomics · 2021Review
- Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity.Cancer cell · 2021Article
- Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer.Cancer research · 2021Article
- An expanded universe of cancer targets.Cell · 2021Review
- Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy.Cancer communications (London, England) · 2020Review
- Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis.Cell systems · 2019Article
- Capturing single-cell heterogeneity via data fusion improves image-based profiling.Nature communications · 2019Article
- In situ 10-cell RNA sequencing in tissue and tumor biopsy samples.Scientific reports · 2019Article
- A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.Genome medicine · 2017Review
- Non-autonomous cell proliferation in the mammary gland and cancer.Current opinion in cell biology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Recent advances have created new opportunities to dissect cellular heterogeneity at the omics level. The enthusiasm for deep single-cell profiling has obscured a discussion of different types of heterogeneity and the most-appropriate techniques for studying each type. Here, I distinguish heterogeneity in regulation from heterogeneity in lineage. Snapshots of lineage heterogeneity provide a cell atlas that catalogs cellular diversity within complex tissues. Profiles of regulatory heterogeneity seek to interrogate one lineage deeply to capture an ensemble of single-cell states. Single-cell atlases require molecular signatures from many cells at a throughput afforded by mass cytometry-based, microfluidic-based, and microencapsulation-based methods. Single-cell states are more dependent on time, microenvironment, and low-abundance transcripts, emphasizing in situ methods that stress depth of profiling and quantitative accuracy.
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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.