Efficient and stable CRISPR/Cas9-mediated genome-editing of human type 2 innate lymphoid cells
Bibliographic record
Abstract
Innate lymphoid cells (ILCs) are a family of innate lymphocytes with important roles in immune response coordination and maintenance of tissue homeostasis. The ILC family includes group 1 (ILC1s), group 2 (ILC2s) and group 3 (ILC3s) 'helper' ILCs, as well as cytotoxic Natural Killer (NK) cells. Study of helper ILCs in humans presents several challenges, including their low proportions in peripheral blood or needing access to rare samples to study tissue resident ILC populations. In addition, the lack of established protocols harnessing genetic manipulation platforms has limited the ability to explore molecular mechanism regulating human helper ILC biology. CRISPR/Cas9 is an efficient genome editing tool that enables the knockout of genes of interest, and is commonly used to study molecular regulation of many immune cell types. Here, we developed methods to efficiently knockout genes of interest in human ILC2s. We discuss challenges and lessons learned from our CRISPR/Cas9 gene editing optimizations using a nucleofection transfection approach and test a range of conditions and nucleofection settings to obtain a protocol that achieves effective and stable gene knockout while maintaining optimal cell viability. Using IL-4 as a representative target, we compare different ribonucleoprotein configurations, as well as assess effects of length of time in culture and other parameters that impact CRISPR/Cas9 transfection efficiency. Collectively, we detail a CRISPR/Cas9 protocol for efficient genetic knockout to aid in studying molecular mechanism regulating human ILC2s.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".