Evidence map›Paper›PMID 20434785›Full record

ReviewTrends in biotechnology2010

Single cell analysis: the new frontier in 'omics'.

Daojing Wang, Steven Bodovitz

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trends in biotechnology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers.

0numbers the graph read from it
0cells of the map it votes in
254citing 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

254 citing papers in PubMed.

  1. Development of infrared desolvation device for single cell element analysis using droplet sample injection inductively coupled plasma spectrometry.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
    Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Single-cell cloning and its approaches.Frontiers in cell and developmental biology · 2025
    Review
  14. Article
  15. Article
  16. Review
  17. Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024
    Review
  18. Review
  19. Article
  20. Article

194 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Daojing WangLawrence Berkeley National Laboratory, Berkeley, California, USA. djwang@lbl.gov
Steven Bodovitz

Funding

NanoArrays in Mass Spectrometry for Proteomic and Metabolomic ApplicationsR21GM077870 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI WANG, DAOJING · 2006 to 2008
$614k
NIGMS NIH HHS R21 GM077870NIGMS NIH HHS R21GM077870
6 · The paper itself

Abstract

Cellular heterogeneity that arises from stochastic expression of genes, proteins and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capability of 'omics' technology. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in omics, and single cell omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity.

Indexed as

Cell Physiological PhenomenaGene Expression ProfilingGenomicsMetabolomicsProteomicsSystems Biology

Identifiers

PMID20434785
PMCPMC2876223

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.