Mouse embryonic stem cell-derived blood–brain barrier model: applicability to studying antibody triggered receptor mediated transcytosis
Bibliographic record
Abstract
Abstract Blood brain barrier (BBB) models in vitro are an important tool to aid in the pre-clinical evaluation and selection of BBB-crossing therapeutics. Stem cell derived BBB models have recently demonstrated a substantial advantage over primary and immortalized brain endothelial cells (BECs) for BBB modeling. Coupled with recent discoveries highlighting significant species differences in the expression and function of key BBB transporters, the field is in need of robust, species-specific BBB models for improved translational predictability. We have developed a mouse BBB model, composed of mouse embryonic stem cell (mESC-D3)-derived brain endothelial-like cells (mBECs), employing a directed monolayer differentiation strategy. Although the mBECs showed a mixed endothelial-epithelial phenotype, they exhibited high transendothelial electrical resistance, inducible by retinoic acid treatment up to 400 Ω cm2. This tight cell barrier resulted in restricted sodium fluorescein permeability (1.7 × 10–5 cm/min), significantly lower than that of bEnd.3 cells (1.02 × 10–3 cm/min) and comparable to human induced pluripotent stem cell (iPSC)-derived BECs (2.0 × 10–5 cm/min). The mBECs expressed tight junction proteins, polarized and functional P-gp efflux transporter and receptor mediated transcytosis (RMT) receptors; collectively important criteria for studying barrier regulation and drug delivery applications in the CNS. In this study, we compared transport of a panel of antibodies binding species selective or cross-reactive epitopes on BBB RMT receptors in both the mBEC and human iPSC-derived BEC model, to demonstrate discrimination of species-specific BBB transport mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".