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Record W4414272675 · doi:10.1101/2025.09.17.675183

Early micro and nanoscopic responses of microglia to blood-brain barrier modulation by transcranial-focused ultrasound

2025· preprint· en· W4414272675 on OpenAlexafffund
Elisa Gonçalves de Andrade, Jared VanderZwaag, Rikke Hahn Kofoed, Micaël Carrier, Katherine Picard, Keelin Henderson Pekarik, Fernando Gonzàlez Ibáñez, Mohammadparsa Khakpour, Kullervo Hynynen, Isabelle Aubert, Marie‐Ève Tremblay

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsSunnybrook Health Science CentreUniversity of Victoria
FundersFDC FoundationSunnybrook Foundation
KeywordsMicrogliaHippocampal formationMicrobubblesBlood–brain barrierUltrasoundInflammationPermeability (electromagnetism)Immunofluorescence

Abstract

fetched live from OpenAlex

Abstract Modulation of the blood-brain barrier (BBB) using transcranial-focused ultrasound (FUS) has rapidly progressed to clinical trials. In combination with phospholipid microspheres, also known as microbubbles, administered in the bloodstream, ultrasound energy is guided by magnetic resonance imaging (MRI) to target specific brain regions with millimetric precision. At the targeted area, the interaction between FUS and microbubbles increases local BBB permeability for 4 to 6 hours, with an ensuing inflammation that resolves within days to weeks. Microglia, as the resident immune cells of the brain, are triggered by FUS-BBB modulation, although the time course of this response is unclear. Thus, the goal of this study was to characterize the early cellular (i.e., density, distribution, and morphology) and subcellular (i.e., ultrastructure) changes in microglial activities following FUS-BBB modulation. Methods We targeted the hippocampi of adult mice with FUS, in the presence of intravenous microbubbles and guided by MRI, and performed analyses 1 hour and 24 hours after FUS-BBB modulation. Microglia were investigated at the population, cellular and subcellular levels, where hippocampal BBB permeability was identified by the entry of endogenous immunoglobulin (Ig)G in the parenchyma. Respective outcome measures included i) the density and distribution of ionized calcium binding adaptor molecule-positive (Iba)1-positive (+) cells; ii) the morphology of the soma and processes of Iba1+ cells; and iii) the quantification of microglial organelles (e.g., phagosomes) and contacts with blood vessels and synapses using chip mapping scanning electron microscopy. Results No significant changes in baseline density and distribution of microglia were found in IgG-positive hippocampal areas at 1 hour and 24 hours after FUS-BBB modulation. By contrast, FUS-BBB modulation was associated with more elongated microglial cell bodies at both time points. The relative distribution of morphologies at 1 hour shifted toward compact shapes with stubby processes, whereas at 24 hours, shapes were bigger, with fewer processes. At the nanoscale, microglia maintained their interactions with blood vessel elements, except vessels most affected by swollen endfeet, which occurred regardless of treatment. In the parenchyma, 24 hours after FUS-BBB modulation, microglia reduced the frequency of contacts with pre-synaptic elements and extracellular space pockets, while showing features of increased metabolic demand and reduced lysosomal activity. Conclusion At 1 hour and 24 hours after FUS-BBB modulation, traits of microglial surveillance activity were largely maintained, with shifts in the shape of a subset of cells, which adopted a morphology associated with injury shielding. FUS-BBB modulation also appears to temporarily modify the digestive, but not the phagocytic activity, of microglia and to reduce pre-synaptic remodeling early after treatment.

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 head0.005
GPT teacher head0.196
Teacher spread0.190 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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