Systemic Gene Silencing in Primary T Lymphocytes Using Targeted Lipid Nanoparticles
Bibliographic record
Abstract
Modulating T cell function by down-regulating specific genes using RNA interference (RNAi) holds tremendous potential in advancing targeted therapies in many immune-related disorders including cancer, inflammation, autoimmunity, and viral infections. Hematopoietic cells, in general, and primary T lymphocytes, in particular, are notoriously hard to transfect with small interfering RNAs (siRNAs). Herein, we describe a novel strategy to specifically deliver siRNAs to murine CD4(+) T cells using targeted lipid nanoparticles (tLNPs). To increase the efficacy of siRNA delivery, these tLNPs have been formulated with several lipids designed to improve the stability and efficacy of siRNA delivery. The tLNPs were surface-functionalized with anti-CD4 monoclonal antibody to permit delivery of the siRNAs specifically to CD4(+) T lymphocytes. Ex vivo, tLNPs demonstrated specificity by targeting only primary CD4(+) T lymphocytes and no other cell types. Systemic intravenous administration of these particles led to efficient binding and uptake into CD4(+) T lymphocytes in several anatomical sites including the spleen, inguinal lymph nodes, blood, and the bone marrow. Silencing by tLNPs occurs in a subset of circulating and resting CD4(+) T lymphocytes. Interestingly, we show that tLNP internalization and not endosome escape is a fundamental event that takes place as early as 1 h after systemic administration and determines tLNPs' efficacy. Taken together, these results suggest that tLNPs may open new avenues for the manipulation of T cell functionality and may help to establish RNAi as a therapeutic modality in leukocyte-associated diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".