Systemic Gene Silencing in Primary T Lymphocytes Using Targeted Lipid Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it