Evidence map›Paper›PMID 36160045›Full record

ArticleCell reports methods2022

Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models.

Jakob J Metzger, Carlota Pereda, Arjun Adhikari, Tomomi Haremaki, Szilvia Galgoczi, Eric D Siggia, Ali H Brivanlou, Fred Etoc

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
8.7field-weighted citation impact, top 2% 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

23 citing papers in PubMed, 36 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Jakob J MetzgerLaboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA.
Carlota PeredaRUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA.
Arjun AdhikariRUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA.
Tomomi HaremakiLaboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA.
Szilvia GalgocziLaboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA.
Eric D SiggiaCenter for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065, USA.
Ali H BrivanlouLaboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA.
Fred EtocLaboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA.
Rockefeller University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibility and scalability have prevented the use of current organoids for high-throughput screening. Here, we present a method that overcomes these limitations by using deep-learning-driven analysis for phenotypic drug screens based on highly standardized micropattern-based neural organoids. This allows us to distinguish between disease and wild-type phenotypes in complex tissues with extremely high accuracy as well as quantify two predictors of drug success: efficacy and adverse effects. We applied our approach to Huntington's disease (HD) and discovered that bromodomain inhibitors revert complex phenotypes induced by the HD mutation. This work demonstrates the power of combining machine learning with phenotypic drug screening and its successful application to reveal a potentially new druggable target for HD.

Indexed as

Deep LearningHuntington DiseaseDrug Evaluation, PreclinicalHigh-Throughput Screening AssaysHumansOrganoidsReproducibility of Resultsbromodomainsdeep learningHuntington's diseaseorganoids

Identifiers

PMID36160045
PMCPMC9500000
OpenAlexW4296378197

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.