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Multicenter Study
. 2024 Aug 1;19(1):59.
doi: 10.1186/s13024-024-00748-2.

Single-cell peripheral immunoprofiling of lewy body and Parkinson's disease in a multi-site cohort

Thanaphong Phongpreecha  1   2   3 , Kavita Mathi  4 , Brenna Cholerton  1 , Eddie J Fox  1 , Natalia Sigal  4 , Camilo Espinosa  2   3   5 , Momsen Reincke  2   3   5 , Philip Chung  2 , Ling-Jen Hwang  3 , Chandresh R Gajera  1 , Eloise Berson  1   2   3 , Amalia Perna  1 , Feng Xie  2   3   5 , Chi-Hung Shu  2   3   5 , Debapriya Hazra  2   3   5 , Divya Channappa  6 , Jeffrey E Dunn  6 , Lucas B Kipp  6 , Kathleen L Poston  6 , Kathleen S Montine  1 , Holden T Maecker  4 , Nima Aghaeepour  2   3   5 , Thomas J Montine  7
Affiliations
Multicenter Study

Single-cell peripheral immunoprofiling of lewy body and Parkinson's disease in a multi-site cohort

Thanaphong Phongpreecha et al. Mol Neurodegener. .

Abstract

Background: Multiple lines of evidence support peripheral organs in the initiation or progression of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinson's Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). However, the potential contribution of the peripheral immune response to LBD remains unclear. This study aims to characterize peripheral immune responses unique to participants with LBD at single-cell resolution to highlight potential biomarkers and increase mechanistic understanding of LBD pathogenesis in humans.

Methods: In a case-control study, peripheral mononuclear cell (PBMC) samples from research participants were randomly sampled from multiple sites across the United States. The diagnosis groups comprise healthy controls (HC, n = 159), LBD (n = 110), Alzheimer's disease dementia (ADD, n = 97), other neurodegenerative disease controls (NDC, n = 19), and immune disease controls (IDC, n = 14). PBMCs were activated with three stimulants (LPS, IL-6, and IFNa) or remained at basal state, stained by 13 surface markers and 7 intracellular signal markers, and analyzed by flow cytometry, which generated 1,184 immune features after gating.

Results: The model classified LBD from HC with an AUROC of 0.87 ± 0.06 and AUPRC of 0.80 ± 0.06. Without retraining, the same model was able to distinguish LBD from ADD, NDC, and IDC. Model predictions were driven by pPLCγ2, p38, and pSTAT5 signals from specific cell populations under specific activation. The immune responses characteristic for LBD were not associated with other common medical conditions related to the risk of LBD or dementia, such as sleep disorders, hypertension, or diabetes.

Conclusions and relevance: Quantification of PBMC immune response from multisite research participants yielded a unique pattern for LBD compared to HC, multiple related neurodegenerative diseases, and autoimmune diseases thereby highlighting potential biomarkers and mechanisms of disease.

Keywords: Alzheimer’s Disease; Biomarkers; Dementia; Inflammation; Parkinson’s Disease.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Overall Experiment and Resulting Immune Landscape. A Diagram of the experiment. PBMCs were collected from diagnosis groups at Stanford ADRC, Stanford BIG, and NCRAD, which in itself aggregated samples from multiple sites. This was followed by stimulating the PBMCs with one of three different canonical immune activators or vehicle control, immunolabelling for surface and intracellular markers, and measuring the cell-specific signals using flow cytometry. Single-cell signals were manually gated to different cell types, resulting in 1,184 immune features for each PBMC sample that were then used by machine learning for the identification of biomarkers. B A correlation network (edges represent Pearson’s R > 0.7) indicates that the immune landscape was mostly determined by the intracellular signals, i.e. the same intracellular signals tend to be correlated to each other despite different cell types and stimulating conditions. C The t-SNE plots suggest that there was not a strong effect by the site of sample collection (left), and that samples from different diagnosis groups were well distributed overall (right)
Fig. 2
Fig. 2
Models developed from multi-site data suggest peripheral biomarkers for LBD. A The model performance suggested good separation for HC vs. LBD, but not for HC vs. ADD. Note that a random guess baseline would yield an AUROC of 0.50 and an AUPRC equivalent to the prevalence of the positive class in the sample group, which are shown as patterned gray bars. B Performance using cross-site splitting instead of random cross-validation suggests the generalizability of the biomarkers. C Transferring the HC vs. LBD model (without retraining) to classify LBD from disease controls, including ADD, NDC, and IDC, yielded similarly high performance. D The predicted values from the HC vs. LBD model for all diagnosis groups show that the model is LBD-specific. E Model residual (errors from each prediction) did not significantly (M.W.U. P < 0.05) vary with sex, age, Levodopa dosage, APOE e4 status, or PD vs. PDD. This indicates that the model performed equally well across these variables. In contrast, the model’s residual varied for the DLB vs. PD/PDD group, suggesting that the performance of the DLB group differed from the PD/PDD group. F The required number of top immune features needed to achieve similar performance as all 1,184 features. G Correlation network highlighting the top features and the immune features with which they are correlated
Fig. 3
Fig. 3
Strong signals for HC vs. LBD were cell-type specific. A The heatmap of selected intracellular signals (or frequency) from all cell types shows the cell types with the strongest correlations to LBD. B Examples of the top univariate immune features
Fig. 4
Fig. 4
All subgroups within LBD can be separated from HC, but not among themselves. A Model performance of three separate models each developed for classifying HC from each of the subgroups within LBD, including DLB, PDD, and PD. B The performance of the same models (without retraining) classifying among each of the subgroups and all of them vs. AD. C The Venn diagrams of significant immune features for each group (M.W.U. P < 0.01) indicated small overlapping features among them. D The correlation network shows which immune features were unique to or overlapping between DLB, PDD, and PD
Fig. 5
Fig. 5
The identified LBD biomarkers did not have overlapping biological pathways with common non-neurodegenerative comorbidities. A A chord diagram displaying LBD, ADD, or HC co-occurrence with other comorbidities. Note that TBI was also included but due to a low number of cases (n = 6), it is now shown in the plot. B Model performances (AUROC) for all comorbidities using model transfer from HC vs. LBD showing that it can also classify REMDIS. C The Venn diagrams of significantly overlapped immune features among groups (M.W.U. P < 0.01)
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