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Record W3016961162 · doi:10.26508/lsa.202000707

Robust production of uniform human cerebral organoids from pluripotent stem cells

2020· article· en· W3016961162 on OpenAlexafffund
Adam Sivitilli, Jessica Gosio, Bibaswan Ghoshal, Alesya Evstratova, Daniel Trcka, Parisa Ghiasi, J. Javier Hernández, Jean‐Martin Beaulieu, Jeffrey L. Wrana, Liliana Attisano

Bibliographic record

VenueLife Science Alliance · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundKrembil FoundationCanada Research Chairs
KeywordsOrganoidInduced pluripotent stem cellBiologyNeuroscienceCell typeHuman brainStem cellCell biologyHuman Induced Pluripotent Stem CellsCellComputational biologyGeneEmbryonic stem cellGenetics

Abstract

fetched live from OpenAlex

Human cerebral organoid (hCO) models offer the opportunity to understand fundamental processes underlying human-specific cortical development and pathophysiology in an experimentally tractable system. Although diverse methods to generate brain organoids have been developed, a major challenge has been the production of organoids with reproducible cell type heterogeneity and macroscopic morphology. Here, we have directly addressed this problem by establishing a robust production pipeline to generate morphologically consistent hCOs and achieve a success rate of >80%. These hCOs include both a radial glial stem cell compartment and electrophysiologically competent mature neurons. Moreover, we show using immunofluorescence microscopy and single-cell profiling that individual organoids display reproducible cell type compositions that are conserved upon extended culture. We expect that application of this method will provide new insights into brain development and disease processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.261
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations78
Published2020
Admission routes2
Has abstractyes

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