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Record W4311362065 · doi:10.1186/s13024-022-00584-2

Single-cell microglial transcriptomics during demyelination defines a microglial state required for lytic carcass clearance

2022· article· en· W4311362065 on OpenAlexafffund
Sameera Zia, Brady P. Hammond, Martin Zirngibl, Anastasia Sizov, Charbel S. Baaklini, Sharmistha P. Panda, Madelene F. S. Ho, Kelly V. Lee, Apurba Mainali, Mena K. Burr, Sioned Williams, Andrew V. Caprariello, Christopher Power, Thomas Simmen, Bradley J. Kerr, Jason R. Plemel

Bibliographic record

VenueMolecular Neurodegeneration · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of CalgaryWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health ResearchHealth CanadaAzrieli FoundationUniversity of AlbertaMultiple Sclerosis Society of CanadaNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsMicrogliaPhagocytosisLytic cycleBiologyTranscriptomeCell biologyProgrammed cell deathNeuroscienceImmunologyApoptosisInflammationBiochemistryGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Microglia regulate the response to injury and disease in the brain and spinal cord. In white matter diseases microglia may cause demyelination. However, how microglia respond and regulate demyelination is not fully understood. METHODS: To understand how microglia respond during demyelination, we fed mice cuprizone-a potent demyelinating agent-and assessed the dynamics of genetically fate-mapped microglia. We then used single-cell RNA sequencing to identify and track the microglial subpopulations that arise during demyelination. To understand how microglia contribute to the clearance of dead oligodendrocytes, we ablated microglia starting at the peak of cuprizone-induced cell death and used the viability dye acridine orange to monitor apoptotic and lytic cell morphologies after microglial ablation. Lastly, we treated serum-free primary microglial cultures to model distinct aspects of cuprizone-induced demyelination and assessed the response. RESULTS: The cuprizone diet generated a robust microglial response by week 4 of the diet. Single-cell RNA sequencing at this time point revealed the presence of several cuprizone-associated microglia (CAM) clusters. These clusters expressed a transcriptomic signature indicative of cytokine regulation and reactive oxygen species production with altered lysosomal and metabolic changes consistent with ongoing phagocytosis. Using acridine orange to monitor apoptotic and lytic cell death after microglial ablation, we found that microglia preferentially phagocytose lytic carcasses. In culture, microglia exposed to lytic carcasses partially recapitulated the CAM state, suggesting that phagocytosis contributes to this distinct microglial state during cuprizone demyelination. CONCLUSIONS: Microglia serve multiple roles during demyelination, yet their transcriptomic state resembles other neurodegenerative conditions. The phagocytosis of cellular debris is likely a universal cause for a common neurodegenerative microglial state.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.229
Teacher spread0.202 · 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

Citations36
Published2022
Admission routes2
Has abstractyes

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