Evidence map›Paper›PMID 30011038›Full record

ArticleNucleic acids research2018

Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.

Isar Nassiri, Matthew N McCall

Open access · goldAbstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
4.9field-weighted citation impact, top 5% of its field
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

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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 at 1 institution in 2 countries.

Isar NassiriDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Matthew N McCallDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
University of Rochester Medical Center · US

Funding

Statistical Methods for Estimation of Gene Regulatory NetworksR00HG006853 · NHGRI · UNIVERSITY OF ROCHESTER · PI MCCALL, MATTHEW NICHOLSON · 2014 to 2016
$726k
NHGRI NIH HHS R00 HG006853
6 · The paper itself

Abstract

Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds. Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action. Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds. These data provide an opportunity to study the interdependence between transcription and cell morphology. Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology. To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology. Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology. We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line.

Indexed as

Cell Line, TumorCell Physiological PhenomenaCell ShapeGene Expression ProfilingGene Expression RegulationGene LibraryGene Regulatory NetworksGenetic Association StudiesHumansImage Processing, Computer-AssistedPhenotypeTranscriptome

Identifiers

PMID30011038
PMCPMC6212779
OpenAlexW2883282742

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.