Mapping the peripheral immune landscape of Parkinson’s disease patients with single-cell sequencing
Bibliographic record
Abstract
Abstract Parkinson’s Disease (PD) is most recognized for its impact on the central nervous system. However, recent breakthroughs underscore the crucial role of interactions between central and peripheral systems in PD’s pathogenesis. The spotlight is now shifting as we explore beyond the central nervous system, 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 PD, 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 PD. 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 PD signatures and potential peripheral blood biomarkers. Following transcriptional profiling of 78 876 cells from 10 healthy controls and 14 PD donors, we observed five major classes of immune cell types; myeloid (monocytes, dendritic cells) and lymphoid (T, B, natural killer) cells from which we identified 38 cellular subtypes following bioinformatic re-clustering. Comparing immune cell subtypes and phenotypes between PD patients and healthy controls revealed notable features of PD: 1) a significant shift of classical CD14 + monocytes towards an activated CD14 + /CD83 + state, 2) changes in lymphocyte subtype abundance, including a significant decrease in CD4 + naive and mucosal-associated invariant T-cells subtypes, along with an increase in CD56 + natural killer cells, 3) the identification by T-cell receptor sequencing of several PD specific T-cell clones shared between multiple patients, suggesting the implication of common epitopes in PD pathogenesis, 4) a notable increase in the expression of activation signature genes, including the AP-1 stress-response transcription factor complex, across all PD cell types. This signal was not present in atypical parkinsonism patients with multiple systems atrophy or progressive supranuclear palsy. Overall, we present a comprehensive atlas of peripheral blood mononuclear cells from control and PD patients which should serve as a tool to improve our understanding of the role the immune cell landscape plays in PD pathogenesis.
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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".