Peripheral Immune Cell Populations Associated with Cognitive Deficits and Negative Symptoms of Treatment-Resistant Schizophrenia

Abstract

Background: Hypothetically, psychotic disorders could be caused or conditioned by immunological mechanisms. If so, one might expect there to be peripheral immune system phenotypes that are measurable in blood cells as biomarkers of psychotic states.

Methods: We used multi-parameter flow cytometry of venous blood to quantify and determine the activation state of 73 immune cell subsets for 18 patients with chronic schizophrenia (17 treated with clozapine), and 18 healthy volunteers matched for age, sex, BMI and smoking. We used multivariate methods (partial least squares) to reduce dimensionality and define populations of differentially co-expressed cell counts in the cases compared to controls.

Results: Schizophrenia cases had increased relative numbers of NK cells, naïve B cells, CXCR5+ memory T cells and classical monocytes; and decreased numbers of dendritic cells (DC), HLA-DR+ regulatory T-cells (Tregs), and CD4+ memory T cells. Likewise, within the patient group, more severe negative and cognitive symptoms were associated with decreased relative numbers of dendritic cells, HLA-DR+ Tregs, and CD4+ memory T cells. Motivated by the importance of central nervous system dopamine signalling for psychosis, we measured dopamine receptor gene expression in separated CD4+ cells. Expression of the dopamine D3 (DRD3) receptor was significantly increased in clozapine-treated schizophrenia and covaried significantly with differentiated T cell classes in the CD4+ lineage.

Conclusions: Peripheral immune cell populations and dopaminergic signalling are disrupted in clozapine-treated schizophrenia. Immuno-phenotypes may provide peripherally accessible and mechanistically specific biomarkers of residual cognitive and negative symptoms in this treatment-resistant subgroup of patients.

Fig 1. Immune cell populations associated with the case-control difference: patients with clozapine-treated schizophrenia versus healthy volunteers.

(A) Separation of participants based on partial least squares (PLS) analysis of the diagnostic response variable: x-axis, each subject’s scores on the first PLS component; y-axis, participant scores on the second PLS component; healthy volunteers = blue points, people with schizophrenia = red points. The diagnostic boundary (broken black line) correctly classifies 97% of all participants; the loading of the binary predictor variable (red line orthogonal to the diagnostic boundary) indicates that diagnosis of schizophrenia is associated with increased scores on both the first two components. (B) Scatter plot of the weights of individual predictor variables (immune cell counts; black points) on the top two PLS components; the diagnostic loading vector is again represented (red line). Text labels identify cell classes that had the highest positive weights (red text) or highest negative weights (blue text) on one or both of the first two PLS components (based on a bootstrapping procedure). Using the threshold that Z-normalised absolute weight must be greater than 3, we have highlighted 5 immune cell populations that have positive weights, and 8 populations that have negative weights, in people with schizophrenia. (C) The weighting of selected cell populations on PLS components relates directly to their relative cell counts in people with schizophrenia and healthy volunteers. Negatively weighted cell populations, like dendritic cells, were decreased in patients; whereas positively weighted cells, like NK cells, were increased in patients. See Table E in S1 File for weights on the first two PLS components, for all immune cell classes with Z-normalised absolute weight greater than 1; and Table G in S1 File for absolute cell counts of major leukocyte populations in both groups. (D) Cytometry tree diagram (simplified) showing all cell classes that were strongly positively weighted (4 red boxes), or strongly negatively weighted (8 blue boxes), on either component in people with schizophrenia. Cell classes that were equally strongly negatively weighted on the first component of the second, symptom-related PLS analysis (Fig 2) are highlighted by a black border.

Fig 2. Immune cell populations associated with cognitive deficit and negative symptom severity in patients with clozapine-treated schizophrenia.

(A) Scatter plot of the weight of individual predictor variables (immune cell populations) on the first two components of the PLS analysis. The loading vectors for each response variable are also shown: cognitive scores (BACS; red line), negative symptom scores (CGI-N; cyan line), positive symptom scores (CGI-P; green line) and overall psychotic symptom scores (CGI-O; magenta line). The first component is essentially a contrast between high BACS scores (indicating good cognitive function) and high CGI-N scores (indicating severe negative symptoms). Text labels identify cell classes that had the highest positive weights (red text) or highest negative weights (blue text) on the first PLS component (based on a bootstrapping procedure). (B) The weighting of selected cell populations on the first component predicts how the corresponding cell counts are correlated with cognitive and symptom severity scores. Negatively weighted cell classes, like dendritic cells, Treg and CD4⁺ memory T cells, have reduced relative counts in patients with more severe negative symptoms (CGI-N) and more impaired cognitive function (BACS); whereas positively weighted cells, like naïve CD4⁺ and CD8⁺ cells, have increased relative counts in more severely symptomatic patients. (C) Using the threshold that bootstrapped Z-normalised absolute weight must be greater than 3, we have highlighted 2 classes that have positive weights, and 6 classes that have negative weights, on the first PLS component. Cell classes that were equally strongly negatively weighted on the first component of the first, diagnostic PLS analysis (Fig 1) are highlighted by a black border. See Table I in S1 File for weights on the first two PLS components, for all immune cell classes with Z-normalised absolute weight greater than 1.

Fig 3. Dopamine D3 receptor gene expression on peripheral T cells.

(A) Boxplots showing increased DRD3 receptor gene expression by CD4⁺ T cells in patients with schizophrenia compared to healthy volunteers (HV) (t = -2.2, df = 30, P = 0.035), with no significant group-difference in DRD4 expression. (B) Scatter plot of the weight of individual predictor variables (immune cell populations) on the first two components of the PLS analysis. The loading vector for the response variable (DRD3 expression) is shown with the solid (dashed) red line indicating increased (decreased) DRD3 expression. Text labels identify four cell classes that had the highest positive weights (red text) or highest negative weights (blue text) on the first two PLS components (|Z|>3; bootstrapping). The cell types most strongly predictive of DRD3 expression were HLADR⁺ Treg and memory Treg. (C) Scatterplot shows the negative correlation (r = -0.53, df = 30, P = 0.002) between DRD3 receptor expression and HLADR⁺ Treg relative cell counts; red = cases, blue = controls. (D) Hierarchy of the 34 CD4⁺ sub-classes included in the PLS analysis, highlighting the 4 cell classes most strongly related to CD4⁺ DRD3 expression. Cell classes that were equally strongly negatively weighted in the case-control and within-group PLS analyses (Figs 1 and 2) are highlighted in bold.