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Record W3156792714 · doi:10.1126/science.abf1230

Barcoded viral tracing of single-cell interactions in central nervous system inflammation

2021· article· en· W3156792714 on OpenAlexafffund
Iain C. Clark, Cristina Gutiérrez‐Vázquez, Michael A. Wheeler, Zhaorong Li, Veit Rothhammer, Mathias Linnerbauer, Liliana M. Sanmarco, Lydia Guo, Manon Blain, Stéphanie Zandee, Chun‐Cheih Chao, Katelyn V. Batterman, Marius Schwabenland, Peter Lotfy, Amalia Tejeda Velarde, Patrick Hewson, Carolina Manganeli Polonio, Michael W. Shultis, Yasmin Salem, Emily Tjon, Pedro H. Fonseca-Castro, Davis Borucki, Kalil Alves de Lima, Agustín Plasencia, Adam R. Abate, Douglas L. Rosene, Kevin J. Hodgetts, Marco Prinz, Jack P. Antel, Alexandre Prat, Francisco J. Quintana

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesNational Institute on AgingNational Cancer Institute, Cairo UniversityFonds de Recherche du Québec - SantéNational Institutes of HealthMinistry of Science and Technology, Government of the People’s Republic of BangladeshNational Multiple Sclerosis SocietyDeutsche ForschungsgemeinschaftMultiple Sclerosis SocietyInternational Progressive MS AllianceASCRS Research FoundationFundação de Amparo à Pesquisa do Estado de São PauloNational Cancer InstituteBrigham and Women's HospitalAmerican Cancer Society
KeywordsCentral nervous systemInflammationTracingNeuroscienceSingle-cell analysisBiologyCellComputational biologyVirologyComputer scienceImmunologyGenetics

Abstract

fetched live from OpenAlex

Cell-cell interactions control the physiology and pathology of the central nervous system (CNS). To study astrocyte cell interactions in vivo, we developed rabies barcode interaction detection followed by sequencing (RABID-seq), which combines barcoded viral tracing and single-cell RNA sequencing (scRNA-seq). Using RABID-seq, we identified axon guidance molecules as candidate mediators of microglia-astrocyte interactions that promote CNS pathology in experimental autoimmune encephalomyelitis (EAE) and, potentially, multiple sclerosis (MS). In vivo cell-specific genetic perturbation EAE studies, in vitro systems, and the analysis of MS scRNA-seq datasets and CNS tissue established that Sema4D and Ephrin-B3 expressed in microglia control astrocyte responses via PlexinB2 and EphB3, respectively. Furthermore, a CNS-penetrant EphB3 inhibitor suppressed astrocyte and microglia proinflammatory responses and ameliorated EAE. In summary, RABID-seq identified microglia-astrocyte interactions and candidate therapeutic targets.

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)0.001
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 score0.023
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.261
Teacher spread0.224 · 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

Citations283
Published2021
Admission routes2
Has abstractyes

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