A streamlined CRISPR workflow to introduce mutations and generate isogenic iPSCs for modeling amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Amyotrophic lateral sclerosis (ALS) represents a complex neurodegenerative disorder with significant genetic heterogeneity. To date, both the genetic etiology and the underlying molecular mechanisms driving this disease remain poorly understood, although in recent years a number of studies have highlighted a number of genetic mutations causative for ALS. With these mutations pointing to potential pathways that may be affected within individuals with ALS, having the ability to generate human neurons and other disease relevant cells containing these mutations becomes even more critical if new therapies are to emerge. Recent developments with the advent of induced pluripotent stem cells (iPSCs) and clustered regularly interspaced short palindromic repeats (CRISPR) gene editing fields gave us the tools to introduce or correct a specific mutation at any site within the genome of an iPSC, and thus model the specific contribution of risk mutations. In this study we describe a rapid and efficient way to either introduce a mutation into a control line, or to correct a mutation, generating an isogenic control line from patient-derived iPSCs with a given mutation. The mutations introduced were the G93A mutation into SOD1 or H517Q into FUS , and the mutation corrected was a patient iPSC line with I114T in SOD1 . A combination of small molecules and growth factors were used to guide a stepwise differentiation of the edited cells into motor neurons in order to demonstrate that disease-relevant cells could be generated for downstream applications. Through a combination of iPSCs and CRISPR editing, the cells generated here will provide fundamental insights into the molecular mechanisms underlying neuron degeneration in ALS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".