Transcriptome analysis reveals similarities between human blood CD3− CD56bright cells and mouse CD127+ innate lymphoid cells
Bibliographic record
Abstract
Abstract For many years, human peripheral blood natural killer (NK) cells have been divided into functionally distinct CD3 − CD56 bright CD16 − and CD3 − CD56 dim CD16 + subsets. Recently, several groups of innate lymphoid cells (ILC), distinct from NK cells in development and function, have been defined in mouse. A signature of genes present in mouse ILC except NK cells, defined by Immunological Genome Project studies, is significantly over-represented in human CD56 bright cells, by gene set enrichment analysis. Conversely, the signature genes of mouse NK cells are enriched in human CD56 dim cells. Correlations are based upon large differences in expression of a few key genes. CD56 bright cells show preferential expression of ILC-associated IL7R (CD127), TNFSF10 (TRAIL), KIT (CD117), IL2RA (CD25), CD27, CXCR3, DPP4 (CD26), GPR183 , and MHC class II transcripts and proteins. This could indicate an ontological relationship between human CD56 bright cells and mouse CD127 + ILC, or conserved networks of transcriptional regulation. In line with the latter hypothesis, among transcription factors known to impact ILC or NK cell development, GATA3 , TCF7 (TCF-1), AHR , SOX4, RUNX2 , and ZEB1 transcript levels are higher in CD56 bright cells, while IKZF3 (AIOLOS), TBX21 (T-bet), NFIL3 (E4BP4), ZEB2 , PRDM1 (BLIMP1), and RORA mRNA levels are higher in CD56 dim cells.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".