Characterizing the Shear Stress-mediated Identity of hiPSC-derived Endothelial Cells
Bibliographic record
Abstract
Human induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) are a promising endothelial cell (EC) source for vascular therapy. They are especially alluring for applications where niche EC types are desired as their lack of tissue-specific maturation may lend plasticity that allows for specification towards distinct endothelial subtypes. Although shear is a crucial component of functional endothelium, few studies have examined the response of iPSC-ECs to shear stress or its impact on iPSC-EC identity. To assess the suitability of iPSC-ECs for tissue engineering applications, this thesis investigated the effects of physiologically-relevant fluid shear conditioning on a commercially available iPSC-EC source; the work herein focused on endothelial properties relevant to the generation of a vascular bypass graft, namely arterial-venous identity, shear sensing, barrier function, and inflammation. The iPSC-ECs were evaluated against similarly conditioned mature endothelial benchmarks and overall presented as more arterial than the mature arterial and venous EC controls in vitro; however, both steady and pulsatile shear conditioning prompted a shift towards venous identity relative to static culture. Pathway analysis from RNA-sequencing of the sheared iPSC-ECs revealed dysregulation of several genes involved in arterial specification processes, although the mechanism(s) driving venous identity with shear remain unclear. Using mature ECs as benchmarks, potential deficiencies in gene expression patterns of endothelial shear sensing elements were identified; validation studies assessing protein expression revealed impaired phosphorylation of junctional mechanosensors. Expression patterns of tight junctions, gap junctions, and adherens junctions as measures of endothelial barrier function, as well as genes related to inflammation were also found to be differentially expressed in the iPSC-ECs compared to mature EC benchmarks. Ultimately, although the iPSC-ECs showed some similarity to the mature EC controls, they did not completely align with any one endothelial benchmark for the metrics assessed in this work. These findings enable us to better define iPSC-EC identity, situate the cells among mature EC cultures, and inform potential applications within tissue engineering involving exposure to fluid flow.
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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 | 0.000 |
| 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".