Evidence map›Paper›PMID 38607016›Full record

ArticleCells2024

An Automated Imaging-Based Screen for Genetic Modulators of ER Organisation in Cultured Human Cells.

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

Open access · goldAbstract read
In one paragraph

Article in Cells, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 96% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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, 4 Dublin, Ireland.
Jeremy C SimpsonCell Screening Laboratory, UCD School of Biology and Environmental Science, University College Dublin, 4 Dublin, Ireland.ORCID 0000-0002-7956-7805
Niamh C O'SullivanUCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, 4 Dublin, Ireland.ORCID 0000-0002-8244-8145
University College Dublin · IE

Funding

Science Foundation Ireland 16/RI/3745Wellcome Trust
6 · The paper itself

Abstract

Hereditary spastic paraplegias (HSPs) are a heterogeneous group of mono-genetic inherited neurological disorders, whose primary manifestation is the disruption of the pyramidal system, observed as a progressive impaired gait and leg spasticity in patients. Despite the large list of genes linked to this group, which exceeds 80 loci, the number of cellular functions which the gene products engage is relatively limited, among which endoplasmic reticulum (ER) morphogenesis appears central. Mutations in genes encoding ER-shaping proteins are the most common cause of HSP, highlighting the importance of correct ER organisation for long motor neuron survival. However, a major bottleneck in the study of ER morphology is the current lack of quantitative methods, with most studies to date reporting, instead, on qualitative changes. Here, we describe and apply a quantitative image-based screen to identify genetic modifiers of ER organisation using a mammalian cell culture system. An analysis reveals significant quantitative changes in tubular ER and dense sheet ER organisation caused by the siRNA-mediated knockdown of HSP-causing genes

Indexed as

Nervous System DiseasesSpastic Paraplegia, HereditaryAnimalsGTP-Binding ProteinsHumansMammalsMembrane ProteinsMembrane Transport ProteinsGTP-Binding ProteinsMembrane ProteinsMembrane Transport Proteinsautomated image analysisendoplasmic reticulumhigh-content imaginghuman cellssiRNA screen

Identifiers

PMID38607016
PMCPMC11011067
OpenAlexW4393187852

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.