Quantifying Biomass Changes of Single CD8+ T Cells during Antigen Specific Cytotoxicity
Bibliographic record
Abstract
Existing approaches that quantify cytotoxic T cell responses rely on bulk or surrogate measurements which impede the direct identification of single activated T cells of interest. Single cell microscopy or flow cytometry methodologies typically rely on fluorescent labeling, which limits applicability to primary cells such as human derived T lymphocytes. Here, we introduce a quantitative method to track single T lymphocyte mediated cytotoxic events within a mixed population of cells using live cell interferometry (LCI), a label-free microscopy technique that maintains cell viability. LCI quantifies the mass distribution within individual cells by measuring the phase shift caused by the interaction of light with intracellular biomass. Using LCI, we imaged cytotoxic T cells killing cognate target cells. In addition to a characteristic target cell mass decrease of 20-60% over 1-4 h following attack by a T cell, there was a significant 4-fold increase in T cell mass accumulation rate at the start of the cytotoxic event and a 2-3 fold increase in T cell mass relative to the mass of unresponsive T cells. Direct, label-free measurement of CD8+ T and target cell mass changes provides a kinetic, quantitative assessment of T cell activation and a relatively rapid approach to identify specific, activated patient-derived T cells for applications in cancer immunotherapy.
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How this classification was reachedexpand
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) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".