Assessing the Innate Sensing of HIV-1 Infected CD4<sup>+</sup> T Cells by Plasmacytoid Dendritic Cells Using an <em>Ex vivo</em> Co-culture System.
Bibliographic record
Abstract
HIV-1 innate sensing requires direct contact of infected CD4+ T cells with plasmacytoid dendritic cells (pDCs). In order to study this process, the protocols described here use freshly isolated human peripheral blood mononuclear cells (PBMCs) or plasmacytoid dendritic cells (pDCs) to sense infections in either T cell line (MT4) or heterologous primary CD4+ T cells. In order to ensure proper sensing, it is essential that PBMC are isolated immediately after blood collection and that optimal percentage of infected T cells are used. Furthermore, multi-parametric flow cytometric staining can be used to confirm that PBMC samples contain the different cell lineages at physiological ratios. A number of controls can also be included to evaluate viability and functionality of pDCs. These include, the presence of specific surface markers, assessing cellular responses to known agonist of Toll-Like Receptors (TLR) pathways, and confirming a lack of spontaneous type-I interferon (IFN) production. In this system, freshly isolated PBMCs or pDCs are co-cultured with HIV-1 infected cells in 96 well plates for 18-22 hr. Supernatants from these co-cultures are then used to determine the levels of bioactive type-I IFNs by monitoring the activation of the ISGF3 pathway in HEK-Blue IFN-α/β cells. Prior and during co-culture conditions, target cells can be subjected to flow cytometric analysis to determine a number of parameters, including the percentage of infected cells, levels of specific surface markers, and differential killing of infected cells. Although, these protocols were initially developed to follow type-I IFN production, they could potentially be used to study other imuno-modulatory molecules released from pDCs and to gain further insight into the molecular mechanisms governing HIV-1 innate sensing.
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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) | 0.000 | 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 | 0.001 |
| 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".