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Record W2896818935 · doi:10.1089/scd.2018.0157

Stress Forces First Lineage Differentiation of Mouse Embryonic Stem Cells; Validation of a High-Throughput Screen for Toxicant Stress

2018· article· en· W2896818935 on OpenAlexaff
Quan-Wen Li, Erica Louden, Jordan Zhou, Sascha Drewlo, Jing Dai, Elizabeth E. Puscheck, Kang Chen, Daniel A. Rappolee

Bibliographic record

VenueStem Cells and Development · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Windsor
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthWayne State University
KeywordsBiologyGreen fluorescent proteinEmbryoid bodyPDGFRAEmbryonic stem cellLeukemia inhibitory factorCell biologyStem cellMolecular biologyImmunologyCancer researchInduced pluripotent stem cellStromal cellGenetics

Abstract

fetched live from OpenAlex

Mouse Embryonic Stem Cells (mESCs) are unique in their self-renewal and pluripotency. Hypothetically, mESCs model gestational stress effects or stresses of in vitro fertilization/assisted reproductive technologies or drug/environmental exposures that endanger embryos. Testing mESCs stress responses should diminish and expedite in vivo embryo screening. Transgenic mESCs for green fluorescent protein (GFP) reporters of differentiation use the promoter for platelet-derived growth factor receptor (Pdgfr)a driving GFP expression to monitor hyperosmotic stress-forced mESC proliferation decrease (stunting), and differentiation increase that further stunts mESC population growth. In differentiating mESCs Pdgfra marks the first-lineage extraembryonic primitive endoderm (ExEndo). Hyperosmotic stress forces mESC differentiation gain (Pdgfra-GFP) in monolayer or three-dimensional embryoid bodies. Despite culture with potency-maintaining leukemia inhibitory factor (LIF), stress forces ExEndo as assayed using microplate readers and validated by coexpression of Pdgfra-GFP, Disabled 2 (Dab2), and laminin by immunofluorescence and GFP protein and Dab2 by immunoblot. In agreement with previous reports, Rex1 and Oct4 loss was inversely proportional to increased Pdgfra-GFP mESC after treatment with high hyperosmotic sorbitol despite LIF. The increase in subpopulations of Pdgfra-GFP + cells>background at ∼23% was similar to the previously reported ∼25% increase in Rex1-red fluorescent protein (RFP)-negative subpopulation at matched high sorbitol doses. By microplate reader, there is a ∼7–11-fold increase in GFP at a high nonmorbid and a morbid dose despite LIF, compared with LIF alone. By flow cytometry (FACS), the subpopulation of Pdgfra-GFP + cells>background increases ∼8–16-fold at these doses. Taken together, the microplate, FACS, immunoblot, and immunofluorescence data suggest that retinoic acid or hyperosmotic stress forces dose-dependent differentiation whether LIF is present or not and this is negatively correlated with and possibly compensates for stress-forced diminished ESC population expansion and potency loss.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 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

Citations18
Published2018
Admission routes1
Has abstractyes

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