Multiomic Profiling of Human Clonal Hematopoiesis Reveals Genotype and Cell-Specific Inflammatory Pathway Activation
Bibliographic record
Abstract
Abstract Clonal hematopoiesis (CH) is an age-associated phenomenon that increases risk for hematologic malignancy and cardiovascular disease. CH is thought to enhance disease risk through inflammation in the peripheral blood 1 . Here, we profile peripheral blood gene expression in 66,968 single cells from a cohort of 17 CH patients and 7 controls. Using a novel mitochondrial DNA barcoding approach, we were able to identify and separately compare mutant TET2 and DNMT3A cells to non-mutant counterparts. We discovered the vast majority of mutated cells were in the myeloid compartment. Additionally, patients harboring DNMT3A and TET2 CH mutations possessed a pro-inflammatory profile in CD14+ monocytes through previously unrecognized pathways such as galectin and macrophage Inhibitory Factor (MIF). We also found that T cells from CH patients, though mostly un-mutated, had decreased expression of GTPase of the immunity associated protein (GIMAP) genes, which are critical to T cell development, suggesting that CH may impair T cell function. Key points - CD14+ monocytes from clonal hematopoiesis patients stimulate inflammation through increased cytokine expression. - T cells from clonal hematopoiesis are deficient in GIMAP expression, suggesting CH may impair T cell differentiation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".