In Situ Staining and Laser Capture Microdissection of Lymph Node Residing SIV Gag-Specific CD8+ T cells—A Tool to Interrogate a Functional Immune Response Ex Vivo
Bibliographic record
Abstract
While a plethora of data describes the essential role of systemic CD8+ T cells in the control of SIV replication little is known about the local in situ CD8+ T cell immune responses against SIV at the intact tissue level, due to technical limitations. In situ staining, using GagCM9 Qdot 655 multimers, were here combined with laser capture microdissection to detect and collect SIV Gag CM9 specific CD8+ T cells in lymph node tissue from SIV infected rhesus macaques. CD8+ T cells from SIV infected and uninfected rhesus macaques were also collected and compared to the SIV GagCM9 specific CD8+ T cells. Illumina bead array and transcriptional analyses were used to assess the transcriptional profiles and the three different CD8+ T cell populations displayed unique transcriptional patterns. This pilot study demonstrates that rapid and specific immunostaining combined with laser capture microdissection in concert with transcriptional profiling may be used to elucidate phenotypic differences between CD8+ T cells in SIV infection. Such technologies may be useful to determine differences in functional activities of HIV/SIV specific T cells.
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.
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".