Revealing the Nanoscale Dynamics of the Extracellular Space in the Living Brain
Bibliographic record
Abstract
The brain is a highly dynamic structure with the extracellular space taking up almost a quarter of its volume. Signalling molecules, neurotransmitters and nutrients transit via the extracellular space, which constitutes a key microenvironment for cellular communication and clearance of toxic metabolites. Nevertheless, the extracellular space has not been characterized in detail in intact living samples because of the lack of appropriate tools allowing its study. Recent technological advances enhancing the luminescence properties and biocompatibility of carbon nanotubes opened the door to super-resolution imaging in the near-infrared in vivo. The luminescence efficiencies of single carbon nanotubes excited via various excitation strategies were compared and optimized for tissue imaging (e.g., targeting various excitonic transitions and through upconversion). The effects of tissue scattering, absorption, autofluorescence, and temperature increase induced by excitation light were systematically examined [1]. Using carbon nanotube tracking, we revealed the hidden structure and viscoelastic properties of the extracellular space of brain slices [2]. Local morphological and viscosity maps of the extracellular space of brain acute slices were reconstructed. A diversity of extracellular space dimensions down to ~40 nm and local viscosity maps were obtained. The rheological properties of the extracellular space are affected by chemical alterations of the extracellular matrix of the brains of live animals. Interestingly, these alterations are local and highly inhomogeneous in space. Probing the viscoelastic properties of the extracellular space is paramount to understand the spatiotemporal dynamics that regulate the cellular mechanisms ultimately influencing fundamental aspects of cell biology. These technological advances constitute the first milestone to generate super-resolution microscopy applications in the near-infrared to investigate live biological samples in situ. References: (1) Danné N* & Godin AG* et al. (2018) Comparison of the resonant excitations and upconversion luminescence of individual carbon nanotubes for biological tissue studies. ACS Photonics 5 (2) 359-364. *ND and AGG contributed equally to this work. (2) Godin AG et al. (2017) Single-nanotube tracking reveals the nanoscale organization of the extracellular space in the live brain. Nature Nanotechnology 12 (3) 238-24.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".