Clonal expansion under the microscope: studying lymphocyte activation and differentiation using live‐cell imaging
Bibliographic record
Abstract
Clonal expansion of lymphocytes is a hallmark of vertebrate adaptive immunity. A small number of precursor cells that recognize a specific antigen proliferate into expanded clones, differentiate and acquire various effector and memory phenotypes, which promote effective immune responses. Recent studies establish a large degree of heterogeneity in the level of expansion and in cell state between and within expanding clones. Studying these processes in vivo, while providing insightful information on the level of heterogeneity, is challenging due to the complex microenvironment and the inability to continuously track individual cells over extended periods of time. Live cell imaging of ex vivo cultures within micro fabricated arrays provides an attractive methodology for studying clonal expansion. These experiments facilitate continuous acquisition of a large number of parameters on cell number, proliferation, death and differentiation state, with single-cell resolution on thousands of expanding clones that grow within controlled environments. Such data can reveal stochastic and instructive mechanisms that contribute to observed heterogeneity and elucidate the sequential order of differentiation events. Intercellular interactions can also be studied within these arrays by following responses of a controlled number of interacting cells, all trapped within the same microwell. Here we describe implementations of live-cell imaging within microwell arrays for studies of lymphocyte clonal expansion, portray insights already gained from these experiments and outline directions for future research. These tools, together with in vivo experiments tracking single-cell responses, will expand our understanding of adaptive immunity and the ways by which it can be manipulated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".