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Record W2924170852 · doi:10.1126/science.aan0925

Cell competition during reprogramming gives rise to dominant clones

2019· article· en· W2924170852 on OpenAlexafffund
Nika Shakiba, Ahmed Fahmy, Gowtham Jayakumaran, Sophie McGibbon, Laurent David, Daniel Trcka, Judith Elbaz, Mira C. Puri, András Nagy, Derek van der Kooy, Sidhartha Goyal, Jeffrey L. Wrana, Peter W. Zandstra

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsReprogrammingNeural crestBiologyEmbryonic stem cellStem cellCell biologyCell typeDevelopmental biologyPopulationCellNeurosciencePerspective (graphical)GeneticsEmbryoSociologyComputer science

Abstract

fetched live from OpenAlex

The ability to generate induced pluripotent stem cells from differentiated cell types has enabled researchers to engineer cell states. Although studies have identified molecular networks that reprogram cells to pluripotency, the cellular dynamics of these processes remain poorly understood. Here, by combining cellular barcoding, mathematical modeling, and lineage tracing approaches, we demonstrate that reprogramming dynamics in heterogeneous populations are driven by dominant "elite" clones. Clones arise a priori from a population of poised mouse embryonic fibroblasts derived from Wnt1-expressing cells that may represent a neural crest-derived population. This work highlights the importance of cellular dynamics in fate programming outcomes and uncovers cell competition as a mechanism by which cells with eliteness emerge to occupy and dominate the reprogramming niche.

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 score0.022
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.262
Teacher spread0.256 · 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

Citations124
Published2019
Admission routes2
Has abstractyes

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