Reproducible, Ultra High-Throughput Formation of Multicellular Organization from Single Cell Suspension-Derived Human Embryonic Stem Cell Aggregates
Bibliographic record
Abstract
BACKGROUND: Human embryonic stem cells (hESC) should enable novel insights into early human development and provide a renewable source of cells for regenerative medicine. However, because the three-dimensional hESC aggregates [embryoid bodies (hEB)] typically employed to reveal hESC developmental potential are heterogeneous and exhibit disorganized differentiation, progress in hESC technology development has been hindered. METHODOLOGY/PRINCIPAL FINDINGS: Using a centrifugal forced-aggregation strategy in combination with a novel centrifugal-extraction approach as a foundation, we demonstrated that hESC input composition and inductive environment could be manipulated to form large numbers of well-defined aggregates exhibiting multi-lineage differentiation and substantially improved self-organization from single-cell suspensions. These aggregates exhibited coordinated bi-domain structures including contiguous regions of extraembryonic endoderm- and epiblast-like tissue. A silicon wafer-based microfabrication technology was used to generate surfaces that permit the production of hundreds to thousands of hEB per cm(2). CONCLUSIONS/SIGNIFICANCE: The mechanisms of early human embryogenesis are poorly understood. We report an ultra high throughput (UHTP) approach for generating spatially and temporally synchronised hEB. Aggregates generated in this manner exhibited aspects of peri-implantation tissue-level morphogenesis. These results should advance fundamental studies into early human developmental processes, enable high-throughput screening strategies to identify conditions that specify hESC-derived cells and tissues, and accelerate the pre-clinical evaluation of hESC-derived cells.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".