Capturing T Lymphocytes’ Dynamic Interactions With Human Neural Cells Using Time-Lapse Microscopy
Bibliographic record
Abstract
To fully perform their functions, T lymphocytes migrate within organs’ parenchyma and interact with local cells. Infiltration of T lymphocytes within the central nervous system (CNS) is associated with numerous neurodegenerative disorders. Nevertheless, how these immune cells communicate and respond to neural cells remains unresolved. To investigate the behavior of T lymphocytes that reach the CNS, we have established an in vitro co-culture model and analyzed the spatiotemporal interactions between human activated CD8+ T lymphocytes and primary human astrocytes and neurons using time-lapse microscopy. By combining multiple variables extracted from individual CD8+ T cell tracking, we show that CD8+ T lymphocytes adopt a more motile and exploratory behavior upon interacting with astrocytes than with neurons. Pretreatment of astrocytes or neurons with IL-1β to mimic in vivo inflammation significantly increases CD8+ T lymphocyte motility. Using visual interpretation and analysis of numerical variables extracted from CD8+ T cell tracking, we identified four distinct CD8+ T lymphocyte behaviors: scanning, dancing, poking and round. IL-1β-pretreatment significantly increases the proportion of scanning CD8+ T lymphocytes, which are characterized by active exploration, and reduces the proportion of round CD8+ T lymphocytes, which are less active. Blocking MHC class I on astrocytes significantly diminishes the proportion of poking CD8+ T lymphocytes, which exhibit synapse-like interactions. Lastly, our co-culture time-lapse model is easily adaptable and sufficiently sensitive and powerful to characterize and quantify spatiotemporal interactions between human T lymphocytes and primary human cells in different conditions while preserving viability of fragile cells such as neurons and astrocytes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".