Evidence map›Paper›PMID 37578220›Full record

ArticleJournal of visualized experiments : JoVE2023

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium.

Zander Esh, Sharanya Suresh, Davide Ortolan, Mitra Farnoodian, Devika Bose, Jiwon Ryu, Andrei Volkov, Kapil Bharti, Ruchi Sharma

Open access · bronzeAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 1 institution in 1 country.

Zander Esh *Ocular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Sharanya Suresh *Ocular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Davide OrtolanOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Mitra FarnoodianOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Devika BoseOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Jiwon RyuOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Andrei VolkovOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH).
Kapil BhartiOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH); kapil.bharti@nih.gov.
Ruchi SharmaOcular and Stem Cell Translational Research Section, National Eye Institute, National Institutes of Health (NIH); fnu.ruchi2@nih.gov.
National Institutes of Health · US

Funding

Developing patient-specific iPS cell derived RPE disease modelsZIAEY000532 · NEI · NATIONAL EYE INSTITUTE · PI BHARTI, KAPIL · 2013 to 2025
$22.0M
Developing functional and authenticated RPE tissue from iPS cellsZIAEY000533 · NEI · NATIONAL EYE INSTITUTE · PI BHARTI, KAPIL · 2013 to 2025
$14.4M
Developing 3D patient-specific iPS cell derived RPE/choroid modelsZIAEY000559 · NEI · NATIONAL EYE INSTITUTE · PI BHARTI, KAPIL · 2017 to 2025
$13.7M
Intramural NIH HHS ZIA EY000532Intramural NIH HHS ZIA EY000533Intramural NIH HHS ZIA EY000559
6 · The paper itself

Abstract

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to photoreceptors and choroidal capillaries, performs phagocytosis of photoreceptor outer segments (POS), and secretes cytokines in a polarized manner for maintaining the homeostasis of the outer retina. Dysfunctional RPE, caused by mutations, aging, and environmental factors, results in the degeneration of other retinal layers and causes vision loss. A hallmark phenotypic feature of degenerating RPE is intra and sub-cellular lipid-rich deposits. These deposits are a common phenotype across different retinal degenerative diseases. To reproduce the lipid deposit phenotype of monogenic retinal degenerations in vitro, induced pluripotent stem cell-derived RPE (iRPE) was generated from patients' fibroblasts. Cell lines generated from patients with Stargardt and Late-onset retinal degeneration (L-ORD) disease were fed with POS for 7 days to replicate RPE physiological function, which caused POS phagocytosis-induced pathology in these diseases. To generate a model for age-related macular degeneration (AMD), a polygenic disease associated with alternate complement activation, iRPE was challenged with alternate complement anaphylatoxins. The intra and sub-cellular lipid deposits were characterized using Nile Red, boron-dipyrromethene (BODIPY), and apolipoprotein E (APOE). To quantify the density of lipid deposits, a machine learning-based software, LipidUNet, was developed. The software was trained on maximum-intensity projection images of iRPE on culture surfaces. In the future, it will be trained to analyze three-dimensional (3D) images and quantify the volume of lipid droplets. The LipidUNet software will be a valuable resource for discovering drugs that decrease lipid accumulation in disease models.

Indexed as

Induced Pluripotent Stem CellsRetinal DegenerationHumansLipidsRetinaRetinal Pigment EpitheliumLipids

Identifiers

PMID37578220
PMCPMC12288051
OpenAlexW4385333278

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.