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Record W4365814742 · doi:10.1186/s13287-023-03302-x

A beginner’s guide on the use of brain organoids for neuroscientists: a systematic review

2023· review· en· W4365814742 on OpenAlexaff
Lance Mulder, Josse A. Depla, Adithya Sridhar, Katja C. Wolthers, Dasja Pajkrt, Renata Vieira de Sá

Bibliographic record

VenueStem Cell Research & Therapy · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsInstitute of Infection and Immunity
FundersAmsterdam University Medical Centers
KeywordsOrganoidForebrainNeuroscienceWnt signaling pathwayBiologySMADComputational biologyCell biologySignal transductionCentral nervous system

Abstract

fetched live from OpenAlex

BACKGROUND: The first human brain organoid protocol was presented in the beginning of the previous decade, and since then, the field witnessed the development of many new brain region-specific models, and subsequent protocol adaptations and modifications. The vast amount of data available on brain organoid technology may be overwhelming for scientists new to the field and consequently decrease its accessibility. Here, we aimed at providing a practical guide for new researchers in the field by systematically reviewing human brain organoid publications. METHODS: Articles published between 2010 and 2020 were selected and categorised for brain organoid applications. Those describing neurodevelopmental studies or protocols for novel organoid models were further analysed for culture duration of the brain organoids, protocol comparisons of key aspects of organoid generation, and performed functional characterisation assays. We then summarised the approaches taken for different models and analysed the application of small molecules and growth factors used to achieve organoid regionalisation. Finally, we analysed articles for organoid cell type compositions, the reported time points per cell type, and for immunofluorescence markers used to characterise different cell types. RESULTS: Calcium imaging and patch clamp analysis were the most frequently used neuronal activity assays in brain organoids. Neural activity was shown in all analysed models, yet network activity was age, model, and assay dependent. Induction of dorsal forebrain organoids was primarily achieved through combined (dual) SMAD and Wnt signalling inhibition. Ventral forebrain organoid induction was performed with dual SMAD and Wnt signalling inhibition, together with additional activation of the Shh pathway. Cerebral organoids and dorsal forebrain model presented the most cell types between days 35 and 60. At 84 days, dorsal forebrain organoids contain astrocytes and potentially oligodendrocytes. Immunofluorescence analysis showed cell type-specific application of non-exclusive markers for multiple cell types. CONCLUSIONS: We provide an easily accessible overview of human brain organoid cultures, which may help those working with brain organoids to define their choice of model, culture time, functional assay, differentiation, and characterisation strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0150.009
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.299
GPT teacher head0.442
Teacher spread0.143 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations54
Published2023
Admission routes1
Has abstractyes

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