Sex-biased human thymic architecture guides T cell development through spatially defined niches
Bibliographic record
Abstract
Within the thymus, regulation of the cellular crosstalk directing T cell development depends on spatial interactions within specialized niches. To create a spatially defined map of tissue niches guiding human postnatal T cell development, we employed the multidimensional imaging platform co-detection by indexing (CODEX) as well as cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) and assay for transposase accessible chromatin sequencing (ATAC-seq). We generated age-matched 4- to 5-month-old human postnatal thymus datasets for male and female donors, identifying significant sex differences in both T cell and thymus biology. We demonstrate a possible role for JAG ligands in directing thymic-like dendritic cell development, identify important functions of a population of extracellular matrix (ECM) − fibroblasts, and characterize the medullary niches surrounding Hassall’s corpuscles. Together, these data represent an age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, providing an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females. • Spatial multiomics show sex differences in early postnatal T cell development • JAG1 skews early thymic progenitor development toward thymic dendritic cells • Thymocytes may self-select to support positive selection of αβT cells • Hassall’s corpuscles organize negative selection niches in the medulla Stankiewicz et al. generate a spatial multiomic resource of human postnatal thymus. Using a niche-centric approach, they show that sex-based differences in thymus biology and T cell development arise before puberty, providing an essential contribution to our understanding of the mechanisms underlying immune function and dysfunction in males and females.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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; both teacher heads agree on what is shown here.
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".