Utilizing a Patient-Specific iPSC Platform for the Study of Rare Genetic Kidney and Vascular Diseases
Bibliographic record
Abstract
Background: The increasing use of induced pluripotent stem cells (iPSCs) to model human genetic diseases in vitro has allowed researchers to better understand the pathology of countless disorders, and subsequently develop better treatments for patients. Currently in our lab we use blood-outgrowth endothelial cells (BOECs) to study endothelial cell (EC) dysfunction in kidney diseases and thrombotic microangiopathies. However, isolating patient BOECs is often challenging, as it requires a significant sample of fresh blood which is not always readily available. As such, we are working to utilize a platform that uses patient skin fibroblasts to create patient-specific iPSCs, that can then be differentiated into ECs which capture the genetic conditions of patients. Methods: Patient skin fibroblast-derived iPSCs and healthy control iPSCs were created using a commercially available Sendai virus CytoTune-iPS 2.0 reprogramming kit (Thermo Fisher). These iPSCs were then differentiated into ECs using a directed differentiation protocol in factor-defined media. Following differentiation, CD31 (PECAM1) positive cells were sorted using fluorescence-activated cell sorting (FACS), to isolate a pure population. Isolated ECs were then characterized using immunofluorescence (IF) staining for CD31 and VE-Cadherin, as well as a tubule formation assay. Results: We were able to successfully create the iPSCs and differentiate them into ECs that express the appropriate markers (CD31 and VE-Cadherin) and are able to successfully form robust capillary-like tubes over a 48 hour period. Conclusions: Our iPSC differentiation protocol provides a verified and valuable tool for the study of genetic kidney disorders involving endothelial cells when BOEC isolation is not possible. Funding: Government Support - Non-U.S.Figure 1.: (A) Patient iPSC ECs sorted for CD31 using FACS express CD31 and VE-Cadherin (20x). (B) Patient iPSC ECs form robust capillary-like tubes over 48 hours (4x).
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".