Preparation and Applications of Organotypic Thymic Slice Cultures
Bibliographic record
Abstract
Thymic selection proceeds in a unique and highly organized thymic microenvironment resulting in the generation of a functional, self-tolerant T cell repertoire. In vitro models to study T lineage commitment and development have provided valuable insights into this process. However, these systems lack the complete three-dimensional thymic milieu necessary for T cell development and, therefore, are incomplete approximations of in vivo thymic selection. Some of the challenges related to modeling T cell development can be overcome by using in situ models that provide an intact thymic microenvironment that fully supports thymic selection of developing T cells. Thymic slice organotypic cultures complement existing in situ techniques. Thymic slices preserve the integrity of the thymic cortical and medullary regions and provide a platform to study development of overlaid thymocytes of a defined developmental stage or of endogenous T cells within a mature thymic microenvironment. Given the ability to generate ~20 slices per mouse, thymic slices present a unique advantage in terms of scalability for high throughput experiments. Further, the relative ease in generating thymic slices and potential to overlay different thymic subsets or other cell populations from diverse genetic backgrounds enhances the versatility of this method. Here we describe a protocol for the preparation of thymic slices, isolation and overlay of thymocytes, and dissociation of thymic slices for flow cytometric analysis. This system can also be adapted to study non-conventional T cell development as well as visualize thymocyte migration, thymocyte-stromal cell interactions, and TCR signals associated with thymic selection by two-photon microscopy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".