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Record W4220963421 · doi:10.1126/science.abl4430

Live cell tracking of macrophage efferocytosis during <i>Drosophila</i> embryo development in vivo

2022· article· en· W4220963421 on OpenAlexaff
Michael H. Raymond, Andrew J. Davidson, Yi Shen, D. Tudor, Christopher D. Lucas, Sho Morioka, Justin S. A. Perry, Julia Krapivkina, David Perrais, Linus J. Schumacher, Robert E. Campbell, Will Wood, Kodi S. Ravichandran

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsUniversity of Alberta
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteAcademy of Medical SciencesWellcome Trust
KeywordsEfferocytosisApoptosisPhagocytosisCell biologyBiologyMacrophageIn vivoProgrammed cell deathIn vitroGenetics

Abstract

fetched live from OpenAlex

Apoptosis of cells and their subsequent removal through efferocytosis occurs in nearly all tissues during development, homeostasis, and disease. However, it has been difficult to track cell death and subsequent corpse removal in vivo. We developed a genetically encoded fluorescent reporter, CharON (Caspase and pH Activated Reporter, Fluorescence ON), that could track emerging apoptotic cells and their efferocytic clearance by phagocytes. Using Drosophila expressing CharON, we uncovered multiple qualitative and quantitative features of coordinated clearance of apoptotic corpses during embryonic development. When confronted with high rates of emerging apoptotic corpses, the macrophages displayed heterogeneity in engulfment behaviors, leading to some efferocytic macrophages carrying high corpse burden. Overburdened macrophages were compromised in clearing wound debris. These findings reveal known and unexpected features of apoptosis and macrophage efferocytosis in vivo.

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 score0.108
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations74
Published2022
Admission routes1
Has abstractyes

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