Mapping the peripheral immune landscape of Parkinson’s disease patients with single-cell sequencing
Bibliographic record
Abstract
Parkinson's disease is most recognized for its impact on the CNS. However, recent breakthroughs underscore the crucial role of interactions between central and peripheral systems in Parkinson's disease pathogenesis. The spotlight is now shifting as we explore beyond the CNS, discovering that peripheral changes such as inflammatory dysfunctions may predict the rate of disease progression and severity. Despite more than 200 years of research on Parkinson's disease, reliable diagnostic or progression biomarkers and effective disease-modifying treatments are still lacking. Additionally, the cellular mechanisms that drive changes in immunity are largely unknown. Thus, understanding peripheral immune signatures could lead to earlier diagnosis and more effective treatments for Parkinson's disease. Here, we sought to define the transcriptomic alterations of the complete peripheral immune cell compartment by single-cell RNA and T-cell-receptor sequencing with hopes of uncovering Parkinson's disease signatures and potential peripheral blood biomarkers. Following transcriptional profiling of 78 876 cells from 10 healthy controls and 14 Parkinson's disease donors, we observed all expected major classes of immune cells; the myeloid (monocytes, dendritic cells) and lymphoid (T lymphocytes, B lymphocytes, natural killer) compartments were further analysed through bioinformatics re-clustering to obtain the final 38 cellular subtypes. Comparing immune cell subtypes and phenotypes between patients with Parkinson's disease and healthy control subjects revealed notable features of Parkinson's disease: (i) a significant shift of classical CD14+ monocytes towards an activated CD14+/CD83+ state; (ii) changes in lymphocyte subtype abundance, including a significant decrease in CD4+ naive and mucosal-associated invariant T-cell subtypes, along with an increase in CD56+ natural killer cells; (iii) the identification of several specific T-cell clones shared between multiple patients, suggesting the implication of common epitopes in Parkinson's disease pathogenesis; and (iv) a notable increase in the expression of activation signature genes, including the AP-1 stress-response transcription factor complex, across all Parkinson's disease cell types. This signal was not present in atypical parkinsonism patients with multiple system atrophy or progressive supranuclear palsy. Overall, we present a comprehensive atlas of peripheral blood mononuclear cells from healthy and Parkinson's disease donors which should serve as a tool to improve our understanding of the role the immune cell landscape plays in Parkinson's disease pathogenesis.
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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 | 0.000 | 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) | 0.000 | 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".