How can gene editing of human pluripotent stem cells help understand the effect of genetics on respiratory diseases?
Bibliographic record
Abstract
Advances in functional genomics are leading the expansion of our knowledge on the role of genetics in respiratory conditions. However, we still need classical genetics to ultimately confirm the role of specific genes in disease. Current approaches to identify gene's roles are either not representative of the disease phenotype or highly time consuming, thus, not feasible in many cases. We aimed to develop an efficient method to genetically manipulate human pluripotent stem cells (hPSCs), which followed by tissue specific differentiation, would speed up the process of generating mutation customised disease relevant tissue specific platforms to complete our understanding of the role of genetics in lung diseases. Using cystic fibrosis (CF) as an example of monogenic disease and exploring hPSC culture conditions and manipulation, we introduced the most common CF mutation, ΔF508, into the CFTR gene, using TALENs into hPSCs. We also corrected the W1282X mutation using CRISPR-Cas9 in human-induced PSCs. This simple method achieved heterozygous and homozygous gene-edited hPSCs with ≤10% efficiency in 3–6 weeks, instead of months. The developed method offers the solution to generate patient derived or mutation customised models derived from hPSCs in a timely manner to assess the function of genes identified by functional genomic approaches. Better integrating gene editing technologies of hPSCs as ultimate step to validate disease associated genes will increase our understanding of genetic determinants of disease development early and later in life, will accelerate the identification of new drug targets and will help increasing translational science applications.
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 | 0.003 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".