Evidence map›Paper›PMID 30390800›Full record

ArticleMethods in enzymology2018

High Content, Phenotypic Assays and Screens for Compounds Modulating Cellular Processes in Primary Neurons.

Miklos Kepiro, Boglarka H Varkuti, Ronald L Davis

Open access · greenAbstract read
In one paragraph

Article in Methods in enzymology, 2018. 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
2.4field-weighted citation impact, top 9% 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, 9 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

3 authors at 1 institution in 1 country.

Miklos KepiroDepartment of Neuroscience, The Scripps Research Institute Florida, Jupiter, FL, United States.
Boglarka H VarkutiDepartment of Neuroscience, The Scripps Research Institute Florida, Jupiter, FL, United States.
Ronald L DavisDepartment of Neuroscience, The Scripps Research Institute Florida, Jupiter, FL, United States. Electronic address: rdavis@scripps.edu.
Scripps Research Institute · US

Funding

Suite of high content assays for mitochondrial dynamics in neuronsR01MH109957 · NIMH · SCRIPPS FLORIDA · PI DAVIS, RONALD L · 2016 to 2019
$2.9M
NIMH NIH HHS R01 MH109957
6 · The paper itself

Abstract

High content, phenotypic screens offer a powerful approach to systems biology at the cellular level. The approach employs cells carrying fluorescently labeled molecules or organelles in 384- or 1536-well microplates, and an automated confocal screening microscope for capturing images from each well. Although some specifics vary according to the assay type, each will apply some degree of image processing and feature extraction followed by a data analysis pipeline to identify the perturbations (small molecules, etc.) of interest. We describe and discuss the advantages and limitations of high content assays and screens using the specific example of assaying mitochondrial dynamics in primary neurons. We provide a detailed description of our culturing methods, imaging and data analysis techniques and provide an open source, ready to use CellProfiler pipeline for high-throughput image segmentation and quantification tool for mitochondrial parameters.

Indexed as

AnimalsCells, CulturedDrug DiscoveryDrug Evaluation, PreclinicalEquipment DesignHigh-Throughput Screening AssaysHumansImage Processing, Computer-AssistedMiceMicroscopy, ConfocalMitochondrial DynamicsNeuronsRatsStaining and LabelingAutomated image analysisCellProfilerHigh content phenotypic screenHit selectionMitochondrial dynamicsMitochondrial morphologyPintoolPrimary neurons

Identifiers

PMID30390800
PMCPMC6532062
OpenAlexW2897457059

What OpenQuestion holds

Textmetadata
LicenceTDM
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.