Evidence map›Paper›PMID 40358835›Full record

ArticleTissue engineering and regenerative medicine2025

Optimal Production of 3D Neuronal Lineage Population by Morphological Classification.

Ji-Hee Choi, Yun-Gwi Park, Jongil Ju, Soon-Jung Park, Sung-Hwan Moon

Abstract read
In one paragraph

Article in Tissue engineering and regenerative medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ji-Hee ChoiDepartment of Animal Science and Technology, Chung-Ang University, Anseong, 17546, Korea.
Yun-Gwi ParkDepartment of Animal Science and Technology, Chung-Ang University, Anseong, 17546, Korea.
Jongil JuDepartment of Physics, College of Science and Technology, Dankook University, Cheonan, Chungnam, Korea.
Soon-Jung ParkR&D Center, Biosolvix Co. Ltd, Seoul, Korea. pure_park@biosolvix.com.
Sung-Hwan MoonDepartment of Animal Science and Technology, Chung-Ang University, Anseong, 17546, Korea. moonsh@cau.ac.kr.ORCID http://orcid.org/0000-0002-6642-5699

Funding

Bio&Medical Technology Development Program of the National Research Foundation RS-2023-00220207Ministry of SMEs and Startups RS-2023-00303986National Research Foundation of Korea 2022R1A2C1006622
6 · The paper itself

Abstract

backgroundThe increasing prevalence of neurodegenerative diseases and toxic substance exposure highlights the need for neuronal cell models that closely mimic human neurons in vivo. Compared to traditional models, human pluripotent stem cell (hPSC)-derived three-dimensional models mimic human physiological characteristics and complex nervous system interactions. These models enable patient-specific treatments and improve the predictive accuracy of drug toxicity evaluations. However, differentiation efficiency varies based on organoid size, structure, and cell line characteristics, necessitating standardized protocols for consistent outcome.

methodsThe morphological characteristics of hPSC-derived embryonic bodies (EBs) formed by concave microwells were analyzed at the early stage of neuronal differentiation. Criteria were established to identify cells with high differentiation efficiency, enabling the optimization of differentiation methods applicable across various cell lines. Neuronal organoids were generated using a microfluidic-concave chip, and their suitability for drug toxicity testing was assessed.

resultsEBs, formed in 500 µm concave microwells, exhibited the highest efficiency for neuronal cell differentiation. Cavity-like EBs were more suitable for neuronal differentiation and maturation than cystic-like forms. The optimal neuronal lineage differentiation method was established, and the drug toxicity sensitivity of organoids generated from this method was validated.

conclusionsThis study identified EB structures suitable for neuronal lineage differentiation based on morphological classification. Furthermore, this study suggested an optimal method for generating neuronal organoids. This method can be applied to various cell lines, enabling its precise use in patient-specific treatments and drug toxicity tests.

Indexed as

Cell Culture Techniques, Three DimensionalCell LineageNeuronsPluripotent Stem CellsCell DifferentiationCell LineHumansOrganoidsConcave microwellMicrofluidic-concave chipMorphological screeningNeuronal organoidPatient-specific medicine

Identifiers

PMID40358835
PMCPMC12209121

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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.