Highly efficient serum-free manipulation of miRNA in human NK cells without loss of viability or phenotypic alterations is accomplished with TransIT-TKO
Bibliographic record
Abstract
Natural killer (NK) cells are innate lymphocytes with functions that include target cell killing, inflammation and regulation. NK cells integrate incoming activating and inhibitory signals through an array of germline-encoded receptors to gauge the health of neighbouring cells. The reactive potential of NK cells is influenced by microRNA (miRNA), small non-coding sequences that interfere with mRNA expression. miRNAs are highly conserved between species, and a single miRNA can have hundreds to thousands of targets and influence entire cellular programs. Two miRNA species, miR-155-5p and miR-146a-5p are known to be important in controlling NK cell function, but research to best understand the impacts of miRNA species within NK cells has been bottlenecked by a lack of techniques for altering miRNA concentrations efficiently and without off-target effects. Here, we describe a non-viral and straightforward approach for increasing or decreasing expression of miRNA in primary human NK cells. We achieve >90% transfection efficiency without off-target impacts on NK cell viability, education, phenotype or function. This opens the opportunity to study and manipulate NK cell miRNA profiles and their impacts on NK cellular programs which may influence outcomes of cancer, inflammation and autoimmunity.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".