Development of human iPSC-derived blood brain barrier CHIPs to assess antibody-triggered receptor mediated transcytosis
Bibliographic record
Abstract
Background: The blood-brain barrier (BBB) is the most important biological barrier between the blood circulation and the central nervous system (CNS), it functions as a physical barrier and plays a major role as a transport and metabolic barrier. In vitro models of the human BBB are highly desirable for drug development and studies of neurovascular pathology. Human induced pluripotent stem cell (iPSC) derived brain endotheli-al-like cells (iBECs) have demonstrated a substantial advantage over primary and immortalized brain endothelial cells for BBB modeling. Methods: We developed a 3D BBB-on-Chip co-culture model using the SynVivo-BBB channel microfluidic technology to model critical com-ponents of the BBB. We established iBEC microvessel lumens under physiological in vivo shear stress conditions (5 dynes/cm2) in the apical channel of the chips, while human primary astrocytes and pericytes were cultured on the basolateral side separated by a porous 1μm membrane. We deployed this BBB-on-Chip model to study antibody-triggered receptor mediated transcytosis by perfusing the iBEC lumens with a well characterized single domain BBB-carrier FC5-Fc and non-crossing A20.1 control. Leveraging Wes (ProteinSimple), we established protocols for on-CHIP BBB permeability quantification, using anti-Fc and anti-His antibodies, in small sample volumes extracted from the microfluidic channels. Results: Astrocyte, pericyte and endothelial cell co-cultures, coupled with in vivo hemodynamic shear stress, enhanced tight junction formation by increased membrane expression of ZO-1 and decreased sodium fluorescein permeability across the iBEC monolayer. We observed similar FC5-Fc transcytosis under 3D static conditions compared to conventional 2D transwell assays; however, a significant increase in FC5-Fc transcytosis was observed under physiological shear stress conditions. Similar BBB crossing of FC5-Fc was observed in in vivo brain exposure experiments. Limitations: This study is limited due to the small volume in the chips. Highly sensitive analytics coupled with small volume size can be used to study the transport mechanism and kinetics. Conclusions: These findings suggest that 3D BBB-on-CHIP technology can recapitulate the physiological characteristics of the BBB in vivo and offer a more predictive platform for assessing antibody transcytosis across the BBB.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".