Single-cell transcriptomics reveals that human milk feeding shapes neonatal immune cell interleukin signaling pathways in a nonrandomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Several studies have indicated the benefits of human milk feeding to infants however, mechanisms behind positive health outcomes have not been investigated. OBJECTIVES: The study aimed to characterize circulating immune cell subpopulation gene expression in human milk-fed (HMF) compared with cow milk formula-fed (FF) infants using single-cell transcriptomics. METHODS: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy HMF (n = 6), and FF (n = 3) infants who were 3-3.5 mo old and enrolled in a nonrandomized clinical trial. Single-cell RNA sequencing was used to generate a PBMC atlas and evaluate gene expression in immune cell subsets. Differential expression analysis was performed on each cell type independently after clustering the cells by similar marker gene expression using the scGEAToolbox. Differentially expressed genes were subjected to pathway analyses using an online functional enrichment analysis program. RESULTS: The relative abundance (%) of T and B lymphocytes, natural killer (NK) cells, and plasmacytoid dendritic cells were similar, whereas monocytes were higher in FF infants than in HMF infants (22.6 ± 10.7 compared with 8.3 ± 5.6; P = 0.0314). In addition, innate and adaptive immune cells from FF infants exhibited a higher activation state compared with HMF infants. We identified 16 distinct cell subsets from the major immune cell types: 3 monocyte subsets, 4 NK subsets, 2 B cell subsets, and 7 T cell subsets. Transcriptional profiles of each peripheral innate and adaptive immune cell subtype varied between HMF and FF infants. Pathway enrichment analysis of cell-specific transcriptional changes within subsets of major cell types revealed that the interleukin (IL)-4/IL-13 signaling pathways were upregulated in FF infants relative to HMF infants. CONCLUSIONS: These findings suggest that human milk downregulates peripheral immune cell cytokine transcriptional signatures linked to allergic inflammation and infection relative to formula feeding.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".