Evidence map›Paper›PMID 35181343›Full record

ArticleJournal of neuroscience methods2022

DRGquant: A new modular AI-based pipeline for 3D analysis of the DRG.

Matthew A Hunt, Harald Lund, Lauriane Delay, Gilson Goncalves Dos Santos, Albert Pham, Zerina Kurtovic, Aditya Telang, Adam Lee, Akhil Parvathaneni, Emily Kussick and 2 more

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

Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

5 citing papers in PubMed, 20 citations in OpenAlex.

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  5. Review
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

12 authors at 2 institutions in 2 countries.

Matthew A HuntDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Physiology, Karolinska Institutet, Stockholm, Sweden. Electronic address: mhunt2488@gmail.com.
Harald LundDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Physiology, Karolinska Institutet, Stockholm, Sweden. Electronic address: harald.lund@ki.se.
Lauriane DelayDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: ldelay@health.ucsd.edu.
Gilson Goncalves Dos SantosDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: ggoncalvesdossantos@health.ucsd.edu.
Albert PhamDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: albert.p2012@gmail.com.
Zerina KurtovicDepartment of Physiology, Karolinska Institutet, Stockholm, Sweden. Electronic address: zerina.kurtovic@ki.se.
Aditya TelangDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: telang.adithya@gmail.com.
Adam LeeDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: a3lee@ucsd.edu.
Akhil ParvathaneniDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: aaparvat@ucsd.edu.
Emily KussickDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: ekussick21@students.claremontmckenna.edu.
Maripat CorrDepartment of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA. Electronic address: mpcorr@health.ucsd.edu.
Tony L YakshDepartments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Departments of Anesthesiology and Pharmacology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. Electronic address: tyaksh@health.ucsd.edu.
University of California, San Diego · USKarolinska Institutet · SE

Funding

Rheumatic Diseases Research Training GrantT32AR064194 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GARY S FIRESTEIN, Monica Guma · 2013 to 2026
$3.8M
AIBP and regulation of neuropathic painR01NS102432 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MILLER, YURY, YAKSH, TONY L. · 2017 to 2021
$3.0M
Sex, Stress and Immunity in the Acute to Chronic Pain TransitionR01NS099338 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI YAKSH, TONY L. · 2017 to 2021
$1.8M
NIAMS NIH HHS T32 AR064194NINDS NIH HHS R01 NS099338NINDS NIH HHS R01 NS102432
6 · The paper itself

Abstract

backgroundThe dorsal root ganglion (DRG) is structurally complex and pivotal to systems processing nociception. Whole mount analysis allows examination of intricate microarchitectural and cellular relationships of the DRG in three-dimensional (3D) space. NEW

methodWe present DRGquant a set of tools and techniques optimized as a pipeline for automated image analysis and reconstruction of cells/structures within the DRG. We have developed an open source software pipeline that utilizes machine learning to identify substructures within the DRG and reliably classify and quantify them.

resultsOur methods were sufficiently sensitive to isolate, analyze, and classify individual DRG substructures including macrophages. The activation of macrophages was visualized and quantified in the DRG following intrathecal injection of lipopolysaccharide, and in a model of chemotherapy induced peripheral neuropathy. The percent volume of infiltrating macrophages was similar to a commercial source in quantification. Circulating fluorescent dextran was visualized within DRG macrophages using whole mount preparations, which enabled 3D reconstruction of the DRG and DRGquant demonstrated subcellular spatial resolution within individual macrophages. COMPARISON WITH EXISTING METHOD(S): Here we describe a reliable and efficient methodologic pipeline to prepare cleared and whole mount DRG tissue. DRGquant allows automated image analysis without tedious manual gating to reduce bias. The quantitation of DRG macrophages was superior to commercial solutions.

conclusionsUsing machine learning to separate signal from noise and identify individual cells, DRGquant enabled us to isolate individual structures or areas of interest within the DRG for a more granular and fine-tuned analysis. Using these 3D techniques, we are better able to appreciate the biology of the DRG under experimental inflammatory conditions.

Indexed as

Ganglia, SpinalMacrophagesImage Processing, Computer-AssistedLipopolysaccharidesMachine LearningLipopolysaccharidesArtificial intelligenceDorsal root ganglionMacrophageThree dimensional (3D) analysisWhole mount

Identifiers

PMID35181343
PMCPMC10644910
OpenAlexW4213191754

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.