Using CRISPR-Cas9 to Generate Isogenic Controls from DCMA Patient-Derived Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Dilated cardiomyopathy with ataxia syndrome (DCMA) is an autosomal recessive disease frequently characterized by heart failure in early childhood. Although globally rare, DCMA is common in the Hutterites of southern Alberta who represent the largest collection of patients in the world. Alberta Children’s Hospital investigators previously identified a single intronic G>C mutation in the poorly characterized gene DNAJC19 as being responsible for DCMA. In collaboration with Stanford University, we have generated induced pluripotent stem cell (iPSCs) from DCMA patient peripheral blood mononuclear cells. Differentiating iPSCs into beating cardiomyocytes (iPSC-CMs) creates a disease-, patient-, and tissue-specific in vitro model of DCMA. However, our current model system has limitations due to lack of appropriately matched controls. This thesis aimed to create isogenic controls from our patient-derived iPSCs using the clusters of regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated endonuclease 9 (Cas9) system. We hypothesized that repairing the G>C mutation in DNAJC19 of our patient iPSCs will produce iPSC-CMs with a phenotype comparable to healthy controls and introducing the G>C mutation into DNAJC19 of healthy iPSCs will produce iPSC-CMs with a DCMA phenotype. Although isogenic controls have yet to be derived, this thesis outlines a potential workflow for the genomic editing of DCMA iPSCs. Our approach utilizes the use of an RNP-complex system that is delivered to iPSCs via lipofection using Lipofectamine Stem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".