Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

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Science Advances  25 Nov 2020:
Vol. 6, no. 48, eabd5575
DOI: 10.1126/sciadv.abd5575
  • Fig. 1 Experimental and analytical workflow from obtaining PBMCs to identifying potential immune cell markers.

    (A) In the discovery cohorts, whole blood was collected from 28 individuals with AD and 17 individuals with PD; AD was compared with the samples from 53 older HCs (HC-I), while PD samples were compared with a subset of those with age- and sex-matched HCs (HC-Isub). A different set of 10 younger HCs (HC-II) was included for examining age effects. In addition, an independent cohort of nine individuals with AD (AD-V) and 15 HCs (HC-V) was used for validation of the developed machine learning models without retraining. (B) PBMCs were either unstimulated or stimulated with IFN-α, IL-6, IL-7, IL-10, IL-21, LPS, or PMA/ionomycin. PBMCs were then bound with 21 metal-conjugated antibodies to surface markers and 15 metal-conjugated antibodies to intracellular signaling molecules before analysis by CyTOF. (C) Cell abundance was evaluated on PBMCs from an unstimulated condition. The stimulations and antibody probes generated a total of 4200 intracellular signaling responses (35 PMBC subtypes under eight stimulating conditions and assayed for 15 intracellular responses), which were used to identify the potential immune features with the aid of cell signaling knowledge, machine learning methods, and statistical analysis.

  • Fig. 2 Responses from the same intracellular signaling proteins are highly correlated to each other.

    (A) Correlation network (Spearman’s coefficient) of immune features obtained from CyTOF data of HC-I, AD, and PD colored by the type of stimulant. The edges of the network represent features with Spearman’s coefficient higher than 0.8. (B) An unsupervised algorithm clustered the network into 24 communities, where their annotations are based on commonly shared feature attributes (PBMC subtype, stimulation, or signaling property) within the community. ERK-1/2, extracellular signal–regulated kinase–1/2.

  • Fig. 3 The iEN model can satisfactorily classify AD/HC-I in both discovery and validation cohorts with the most important model components associated with signals from pPLCγ2 and pSTATs.

    (A) Box plots showing the predicted values from iEN model with Wilcoxon rank sum test P value for discovery and validation cohorts. (B) ROC curves from the iEN model predictions of discovery and validation cohorts with their respective AUC and P values from unpaired t test indicating no significant difference between the discovery and validation’s AUCs. (C) Correlation network colored by iEN model components with red and blue colors highlighting the components that are indicative of AD and HC-I, respectively. The size of the nodes represents the Spearman’s coefficient of the immune feature to the respective ground truths. (D) A model reduction analysis looking at the effect of the number of included features on iEN performance. (E) The correlation network colored and annotated only for the top 14 features that were associated with components selected from model reduction.

  • Fig. 4 Heatmaps and box plots of the intracellular response in the PBMC of the selected communities highlight immune features for AD/HC-I classification and examination of the predictive power by F1-score of pPLCγ2 as a standalone biomarker.

    (A to C) Heatmap of the pPLCγ2, pSTAT1, and pSTAT5 responses by PBMC subtypes and stimulations. The color of the heatmap scaled with the Wilcoxon rank sum test P value of the difference in response of the immune feature between HC-I and patients with AD. The network communities annotated with these responses (communities 12, 17, 18, and 20) were depicted on the left-hand side of the heatmap. The size of the nodes in the community corresponds to the Spearman’s coefficient of the immune feature. The features within the communities that were selected by reduced iEN model (14 components) retained their red/blue colors corresponding to the direction of the component. (D to H) Box plots showing the significant difference of the selected immune features from the heatmap. These are mostly features associated with the most informing components of the iEN model. (I) The distribution of the mean F1-score in the test set from 1000 iterations of leave-group out test for each of the 280 pPLCγ2 features from different cell types and stimulating conditions in the discovery set. (J) The mean F1-score and its SD for the top seven performing pPLCγ2 features in the discovery cohort compared to the iEN predicted values. (K) The F1-score for each of the top seven features and iEN predicted values in the validation set. Tregs, regulatory T cells.

  • Fig. 5 Disease cross-prediction reveals similarities between AD and PD.

    (A) Performance of the disease cross-prediction using iEN components developed from AD/HC-I diagnosis to classify PD/HC-Isub and AD/PD. (B) The iEN predicted values for each diagnostic group. (C) The correlation network with node size corresponding to the Wilcoxon rank sum test P value of each feature for PD/HC-Isub diagnosis, with the color of each node representing the magnitude and direction of the associated iEN components developed from AD/HC-I. The network highlighted possible regions, such as in the labeled clusters, where AD and PD signals can overlap.

  • Table 1 Summary of participants in the discovery (n = 108) and validation cohorts (n = 24) diagnosed with AD, PD, or HCs.

    DiscoveryHC-IM2975 ± 8
    F2468 ± 6
    ADM1474 ± 10
    F1467 ± 9
    HC-IsubM2273 ± 5
    F1271 ± 2
    PDM1170 ± 5
    F671 ± 3
    HC-IIM440 ± 9
    F640 ± 10
    ValidationHC-VM671 ± 10
    F970 ± 6
    AD-VM474 ± 12
    F571 ± 10

Supplementary Materials

  • Supplementary Materials

    Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases

    Thanaphong Phongpreecha, Rosemary Fernandez, Dunja Mrdjen, Anthony E. Culos, Chandresh Gajera, Adam M. Wawro, Natalie Stanley, Brice Gaudilli�re, Kathleen L. Poston, Nima Aghaeepour, Thomas J. Montine

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    • Supplementary Methods
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