Deep learning-aided inter-species-comparison reveals shared and distinct molecular patterns in cynomolgus monkey and humans following non-specific T cell activation
Bibliographic record
Abstract
The early phase of drug development relies on the examination of the efficacy and safety of therapeutic agents in animal models. Due to their close genetic and physiological relation to humans, cynomolgus monkeys ( Macaca fascicularis ) are a promising animal model in preclinical studies investigating the immune system. However, the shared and divergent characteristics of the immune response at the molecular level are not yet fully understood, which makes transferring findings from these studies to human conditions challenging. Here, we demonstrate a cross-species analysis pipeline using single-cell transcriptomics (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs), investigating the transcriptomic response in cynomolgus monkeys and healthy humans following anti- CD3 /anti- CD28 T cell activation. For this, PBMCs were collected at baseline, stimulated in vitro , and measured at 0 hours, at 6 hours and at 24 hours post-stimulation, with two biological replicates per species. The analysis integrates Variational Autoencoder (VAE)-based deep learning, cell-cell communication, differential gene expression, and pathway enrichment for an in-depth data exploration. We observed shared molecular patterns across species in the transition from innate to adaptive immune response, such as the increase of CD4 + T cell proportion and the reduction of CD14 + CD16 - and CD14 low CD16 + monocytes. Specific transcriptional clusters related to metabolic reprogramming emerged in CD8 + T cells and related to inflammatory and antiviral programs in NK cells at 24 hours post-stimulation in both species, with stronger regulation of pathways related to cell cycle progression, DNA replication, and GPCR signaling in the emerging CD8 + T cell cluster in monkeys than in humans. Cross-species overlap in activated pathways increased from 6 to 24 hours post-stimulation, with pathway co-enrichment and shared foreground genes becoming more similar across species at 24 hours, including Regulation Of Natural Killer Cell Chemotaxis and Interleukin-27-Mediated Signaling Pathway . Across time, we observed a consistent decline in the expression of receptors and ligands involved in cell-cell communication in most cell types, however, the initial levels were higher in humans and the decline more pronounced. Our proposed computational framework enables systematic cross-species time series analyses, advancing translational research and contributing to improved development of immunomodulating therapies.
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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 | 0.001 |
| 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) | 0.001 | 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".