Development of novel platforms for the clinical applications of induced pluripotent stem cells
Bibliographic record
Abstract
This thesis is aimed to develop applications of iPSCs for patients suffering from rare disorders. A personalised-platform was designed to match approved experimental drug/s to a patient with Leigh-like syndrome. It consisted of iPSCs and its derivatives (fibroblasts, neural stem cells, cardiomyocytes and forebrain neurons). Cell-type specific disease pathophysiologies of ATP, extracellular lactate, reactive oxygen species, mitochondrial-membrane-potential, growth and differentiation were assessed in comparison to control. All the cell-types were found to display significant errors in most of the parameters with multi-lineage differentiation, reactive oxygen species production and mitochondrial membrane potential observed to be severely affected. Proteomic analysis established the rescue of mitochondrial membrane potential and normalization of reactive oxygen species production as reproducible indicators of the drug candidates’ efficacy and toxicity. Systematic evaluation of the mitochondrial compromise proved suitability of Elamipretide, as the optimal drug for the studied variant. Next, we demonstrated isogenic sources for cell therapies for patients suffering with Mitochondrial disorders (MELAS and Kearns Sayre Syndrome) who displayed heteroplasmy of the mitochondrial DNA (mtDNA). Nucleated cells from blood displayed a high ratio of normal mtDNA to mutant mtDNA and thence were used to establish iPSCs and differentiated into multi-lineages (neural and cardiac) known to be affected in these disorders. The iPSCs and their derivatives didn’t show any mtDNA mutation over long term culture (>2 years). The patient iPSC-derived fibroblasts, neural stem cells and cardiomyocytes did not display any disease physiologies or progression in the parameters tested here - ATP, cellular growth, extracellular lactate, differentiation, electrophysiology, reactive oxygen species and mitochondrial membrane potential. The final chapter focussed on the immunological landscape of iPSCs and their derivatives, based on the dynamics of the major histocompatibility complexes (MHC-I and MHC-II). The surface expression of MHC-I was found to decrease and that of MHC-II was found to significantly increase after cardiac differentiation. Higher MHC-I led to immune evasion and higher MHC-II led to immune recognition. 26S proteasome was established to be a key regulator of both the MHC-surface-expression. Increasing the 26S proteasome activity in the iPSC-cardiomyocytes helped maintain higher MHC-I and lower MHC-II levels at the cell surface. This helped in conferring immunoprivilege to the iPSC-derived cardiomyocytes. Thus, in this body of work, I have presented novel clinical application of the iPSC – by developing a platform of iPSC-derived functional cell types to support precision medicine in patients with rare inborn disorders. I have also demonstrated that patients affected with mitochondrial disorders can benefit from iPSC generation from tissue with low levels of heteroplasmy, leading to isogenic cell therapy. In the end, I have attempted to address the major hurdle of immunogenicity in the clinical application of iPSC and their derivatives.
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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 | 0.001 | 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 | 0.001 |
| 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".