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Record W2806459091 · doi:10.1016/j.isci.2018.06.001

Live-Cell Imaging of Chromatin Condensation Dynamics by CRISPR

2018· article· en· W2806459091 on OpenAlexfundno aff
Yuan Xue, Murat Açar

Bibliographic record

VenueiScience · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute on AgingNational Institutes of HealthUniversity of TorontoYale University
KeywordsChromatinBiologyDNARibosomal DNACell biologyProphaseMitosisHelicaseGeneticsRNAGenePhylogenetics

Abstract

fetched live from OpenAlex

The spatiotemporal organization of chromatin plays central roles in cellular function. The ribosomal DNA (rDNA) chromatin undergoes dynamic structural changes during mitosis and stress. Here, we developed a CRISPR-based imaging system and tracked the condensation dynamics of rDNA chromatin in live yeast cells under glucose starvation. We found that acute glucose starvation triggers rapid condensation of rDNA. Time-lapse microscopy revealed two stages for rDNA condensation: a "primary stage," when relaxed rDNA chromatin forms higher order loops or rings, and a "secondary stage," wehen the rDNA rings further condense into compact clusters. Twisting of rDNA rings accompanies the secondary stage. The condensin complex, but not the cohesin complex, is required for efficient rDNA condensation in response to glucose starvation. Furthermore, we found that the DNA helicase Sgs1 is essential for the survival of cells expressing rDNA-bound dCas9, suggesting a role for helicases in facilitating DNA replication at dCas9-binding sites.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

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