Lentiviral Vector Preparation for Efficient Gene and MicroRNA Modulation of Peritoneal Cavity Tissue-Resident Macrophages <em>In Vivo</em> in Mice
Bibliographic record
Abstract
Peritoneal tissue-resident macrophages have broad functions in the maintenance of homeostasis and are involved in pathologies within local and neighboring tissues. Their functions are dictated by microenvironmental cues; thus, it is essential to investigate their behavior in an in vivo physiological niche. Currently, specific peritoneal macrophage-targeting methodologies employ whole-mouse transgenic models. Here, a protocol for effective in vivo modulation of mRNA and small RNA species (e.g., microRNA) expression in peritoneal macrophages using lentivirus particles is described. Lentivirus preparations were made in HEK293T cells and purified on a single sucrose layer. In vivo validation of lentivirus effectivity following intraperitoneal injection revealed predominant infection of macrophages restricted to local tissue. Targeting of peritoneal macrophages was successful during homeostasis and thioglycolate-induced peritonitis. The limitations of the protocol, including low-level inflammation induced by intraperitoneal delivery of lentivirus and time restrictions for potential experiments, are discussed. Overall, this study presents a quick and accessible protocol for the rapid assessment of gene function in peritoneal macrophages in vivo.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".