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Record W4200430607 · doi:10.1084/jem.20211924

Modeling human yolk sac hematopoiesis with pluripotent stem cells

2021· article· en· W4200430607 on OpenAlexafffund
Michael Atkins, Rebecca Scarfò, Kathleen E. McGrath, Donghe Yang, James Palis, Andrea Ditadi, Gordon Keller

Bibliographic record

VenueThe Journal of Experimental Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsStem Cell NetworkUniversity of TorontoUniversity Health Network
FundersNational Heart, Lung, and Blood InstituteNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthFondazione Telethon
KeywordsYolk sacInduced pluripotent stem cellHaematopoiesisStem cellHuman Induced Pluripotent Stem CellsCell biologyBiologyEmbryonic stem cellEmbryoGeneticsGene

Abstract

fetched live from OpenAlex

In the mouse, the first hematopoietic cells are generated in the yolk sac from the primitive, erythro-myeloid progenitor (EMP) and lymphoid programs that are specified before the emergence of hematopoietic stem cells. While many of the yolk sac-derived populations are transient, specific immune cell progeny seed developing tissues, where they function into adult life. To access the human equivalent of these lineages, we modeled yolk sac hematopoietic development using pluripotent stem cell differentiation. Here, we show that the combination of Activin A, BMP4, and FGF2 induces a population of KDR+CD235a/b+ mesoderm that gives rise to the spectrum of erythroid, myeloid, and T lymphoid lineages characteristic of the mouse yolk sac hematopoietic programs, including the Vδ2+ subset of γ/δ T cells that develops early in the human embryo. Through clonal analyses, we identified a multipotent hematopoietic progenitor with erythroid, myeloid, and T lymphoid potential, suggesting that the yolk sac EMP and lymphoid lineages may develop from a common progenitor.

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)0.001
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 score0.047
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.325
Teacher spread0.284 · 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

Citations81
Published2021
Admission routes2
Has abstractyes

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