Evidence map›Paper›PMID 20639869›Full record

ArticleNature methods2010

Amplification-free digital gene expression profiling from minute cell quantities.

Fatih Ozsolak, David T Ting, Ben S Wittner, Brian W Brannigan, Suchismita Paul, Nabeel Bardeesy, Sridhar Ramaswamy, Patrice M Milos, Daniel A Haber

Abstract read
In one paragraph

Article in Nature methods, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Systematic detection of mCell reports methods · 2021
    Article
  2. Article
  3. Cell type-specific transcriptome profiling in mammalian brains.Frontiers in bioscience (Landmark edition) · 2016
    Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Dual RNA-seq of pathogen and host.Nature reviews. Microbiology · 2012
    Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes.Proceedings of the National Academy of Sciences of the United States of America · 2012
    Article
  18. SNP Discovery through Next-Generation Sequencing and Its Applications.International journal of plant genomics · 2012
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Fatih OzsolakHelicos BioSciences Corporation, Cambridge, Massachusetts, USA. fatihozsolak@gmail.com
David T Ting
Ben S Wittner
Brian W Brannigan
Suchismita Paul
Nabeel Bardeesy
Sridhar Ramaswamy
Patrice M Milos
Daniel A Haber

Funding

Attomole-Level High-Throughput GenomicsR44HG005279 · NHGRI · SEQLL, LLC · PI MILOS, PATRICE M · 2010 to 2012
$1.6M
Howard Hughes Medical InstituteNHGRI NIH HHS 1R44HG005279-01NHGRI NIH HHS R44 HG005279
6 · The paper itself

Abstract

Generating reliable expression profiles from minute cell quantities is critical for scientific discovery and potential clinical applications. Here we present low-quantity digital gene expression (LQ-DGE), an amplification-free approach involving capture of poly(A)(+) RNAs from cellular lysates onto poly(dT)-coated sequencing surfaces, followed by on-surface reverse transcription and sequencing. We applied LQ-DGE to profile malignant and nonmalignant mouse and human cells, demonstrating its quantitative power and potential applicability to archival specimens.

Indexed as

AnimalsCell LineGene Expression ProfilingHumansMiceMicroarray AnalysisOligonucleotide Array Sequence AnalysisPoly TReverse TranscriptionRNA, MessengerSequence Analysis, DNAPoly TRNA, Messenger

Identifiers

PMID20639869
PMCPMC2975601

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Registered trials

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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.