Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures
Bibliographic record
Abstract
Abstract The human immune system undergoes continuous remodeling from infancy through old age, yet the timing and trajectory of these changes across the lifespan remain poorly defined. To address this, we profiled peripheral blood mononuclear cells from 95 healthy individuals (ages 2 months to 88 years), including infants (n=27), children (n=23), adults (n=18), and older adults (n=27) using scRNA-seq and snATAC-seq. MAIT and γδ T cells showed a “Rise and fall” pattern, which rise in childhood, peak in young adulthood, and decline with age. CD8 + T cells were the most affected by aging with decreasing naïve T cells and increasing GzK + CD8 + T cells and TEMRA cells. Infants had lower myeloid/lymphoid ratio, with a distinct composition marked by increased frequencies of CD16 + monocytes and plasmacytoid dendritic cells and reduced frequencies of CD14 + monocytes and conventional DCs. Their adaptive immune compartment also displayed unique features, including constitutive interferon-stimulated gene expression in T and B cells, and an expanded SOX4 + populations in naïve CD4 + , naïve CD8 + and γδ T cells, comprising ∼30% of the naïve T cell pool. SOX4 + naïve CD4 + T cells displayed a Th2 epigenetic signature. This map provides critical insights into human immune system dynamics across the lifespan, emphasizing unique features of the infant immune system.
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 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 | 0.000 | 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) | 0.002 | 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 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".