Evidence map›Paper›PMID 34496765›Full record

ArticleBMC bioinformatics2021

A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells.

M Elena Garcia-Pardo, Jeremy C Simpson, Niamh C O'Sullivan

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 13% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
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

3 authors at 1 institution in 1 country.

M Elena Garcia-PardoUCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
Jeremy C Simpson *Cell Screening Laboratory, UCD School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland.ORCID https://orcid.org/0000-0002-7956-7805
Niamh C O'Sullivan *UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin 4, Ireland. niamh.osullivan@ucd.ie.ORCID http://orcid.org/0000-0002-8244-8145
University College Dublin · IE

Funding

Irish Research Council for Science, Engineering and Technology GOIPG/2018/3011Science Foundation Ireland 16/RI/3745University College Dublin UCD PhD Advance awardWellcome TrustWellcome Trust 202020/Z/16/Z
6 · The paper itself

Abstract

backgroundIn mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as changes to the structure and organisation of the reticular network. Due to its complex morphology, image analysis methods to quantitatively describe this organelle, and importantly any changes to it, are lacking.

resultsIn this work we detail a methodological approach that utilises automated high-content screening microscopy to capture images of cells fluorescently-labelled for various ER markers, followed by their quantitative analysis. We propose that two key metrics, namely the area of dense ER and the area of polygonal regions in between the reticular elements, together provide a basis for measuring the quantities of rough and smooth ER, respectively. We demonstrate that a number of different pharmacological perturbations to the ER can be quantitatively measured and compared in our automated image analysis pipeline. Furthermore, we show that this method can be implemented in both commercial and open-access image analysis software with comparable results.

conclusionsWe propose that this method has the potential to be applied in the context of large-scale genetic and chemical perturbations to assess the organisation of the ER in adherent cell cultures.

Indexed as

Endoplasmic ReticulumImage Processing, Computer-AssistedAnimalsCell LineHumansSoftwareAutomated image analysisEndoplasmic reticulum morphologyER functionHigh-content imaging

Identifiers

PMID34496765
PMCPMC8425006
OpenAlexW3198803554

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.