Generation of Human Blood Vessel Organoids from Pluripotent Stem Cells
Bibliographic record
Abstract
An organoid is defined as an engineered multicellular in vitro tissue that mimics its corresponding in vivo organ such that it can be used to study defined aspects of that organ in a tissue culture dish. The breadth and application of human pluripotent stem cell (hPSC)-derived organoid research have advanced significantly to include the brain, retina, tear duct, heart, lung, intestine, pancreas, kidney, and blood vessels, among several other tissues. The development of methods for the generation of human microvessels, specifically, has opened the way for modeling human blood vessel development and disease in vitro and for the testing and analysis of new drugs or tissue tropism in virus infections, including SARS-CoV-2. Complex and lengthy protocols lacking visual guidance hamper the reproducibility of many stem cell-derived organoids. Additionally, the inherent stochasticity of organoid formation processes and self-organization necessitates the generation of optical protocols to advance the understanding of cell fate acquisition and programming. Here, a visually guided protocol is presented for the generation of 3D human blood vessel organoids (BVOs) engineered from hPSCs. Presenting a continuous basement membrane, vascular endothelial cells, and organized articulation with mural cells, BVOs exhibit the functional, morphological, and molecular features of human microvasculature. BVO formation is initiated through aggregate formation, followed by mesoderm and vascular induction. Vascular maturation and network formation are initiated and supported by embedding aggregates in a 3D collagen and solubilized basement membrane matrix. Human vessel networks form within 2-3 weeks and can be further grown in scalable culture systems. Importantly, BVOs transplanted into immunocompromised mice anastomose with the endogenous mouse circulation and specify into functional arteries, veins, and arterioles. The present visually guided protocol will advance human organoid research, particularly in relation to blood vessels in normal development, tissue vascularization, and disease.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".