Evidence map›Paper›PMID 36824835›Full record

ArticlebioRxiv : the preprint server for biology2023

Assessing the performance of the Cell Painting assay across different imaging systems.

Callum Tromans-Coia, Nasim Jamali, Hamdah Shafqat Abbasi, Kenneth A Giuliano, Mai Hagimoto, Kevin Jan, Erika Kaneko, Stefan Letzsch, Alexander Schreiner, Jonathan Z Sexton and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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
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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Callum Tromans-CoiaORCID 0000-0002-5518-8915
Hamdah Shafqat AbbasiORCID 0000-0003-0237-0323
Kenneth A Giuliano
Mai Hagimoto
Kevin Jan
Erika Kaneko
Stefan Letzsch
Alexander Schreiner
Jonathan Z SextonORCID 0000-0002-9244-5888
Mahomi Suzuki
O Joseph Trask
Mitsunari Yamaguchi
Fumiki Yanagawa
Michael Yang

Funding

Laser-based high content imaging microscopeS10OD034245 · OD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SEXTON, JONATHAN ZACHARY · 2024 to 2024
$1.4M
NIH HHS S10 OD034245
6 · The paper itself

Abstract

Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.

Identifiers

PMID36824835
PMCPMC9949001

What OpenQuestion holds

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LicenceCC BY
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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.