Cerebrospinal fluid flow cytometry distinguishes psychosis spectrum disorders from differential diagnoses

Association of symptom severity and cerebrospinal fluid alterations in recent onset psychosis in schizophrenia-spectrum disorders - An individual patient data meta-analysis

Neuroinflammation and blood-cerebrospinal fluid barrier (BCB) disruption could be key elements in schizophrenia-spectrum disorderś(SSDs) etiology and symptom modulation. We present the largest two-stage individual patient data (IPD) meta-analysis, investigating the association of BCB disruption and cerebrospinal fluid (CSF) alterations with symptom severity in first-episode psychosis (FEP) and recent onset psychotic disorder (ROP) individuals, with a focus on sex-related differences. Data was collected from PubMed and EMBASE databases. FEP, ROP and high-risk syndromes for psychosis IPD were included if routine basic CSF-diagnostics were reported. Risk of bias of the included studies was evaluated. Random-effects meta-analyses and mixed-effects linear regression models were employed to assess the impact of BCB alterations on symptom severity. Published (6 studies) and unpublished IPD from n = 531 individuals was included in the analyses. CSF was altered in 38.8 % of individuals. No significant differences in symptom severity were found between individuals with and without CSF alterations (SMD = -0.17, 95 %CI -0.55-0.22, p = 0.341). However, males with elevated CSF/serum albumin ratios or any CSF alteration had significantly higher positive symptom scores than those without alterations (SMD = 0.34, 95 %CI 0.05-0.64, p = 0.037 and SMD = 0.29, 95 %CI 0.17-0.41p = 0.005, respectively). Mixed-effects and simple regression models showed no association (p > 0.1) between CSF parameters and symptomatic outcomes. No interaction between sex and CSF parameters was found (p > 0.1). BCB disruption appears highly prevalent in early psychosis and could be involved in positive symptomś severity in males, indicating potential difficult-to-treat states. This work highlights the need for considering BCB breakdownand sex-related differences in SSDs clinical trials and treatment strategies.

Fluid biomarkers unveil signatures of pathological aging

Aging is a multifaceted and highly varied process in the brain. Identifying aging biomarkers is one means of distinguishing pathological from physiological aging. The aim of this narrative review is to focus on two new developments in the field of fluid biomarkers and draw attention to this excellent tool for the early detection of potential brain pathologies that delay, alter, or enable physiological aging to become pathological. Pathological aging can lower the threshold for the development of specific diseases such as late-onset epilepsy. Fluid biomarkers can reveal pathological levels at an early stage and thus indicate disease processes in the brain that begin before symptoms develop; they thus differ from physiological aging.

The expression of immune related genes and potential regulatory mechanisms in schizophrenia

OBJECTIVE

This study aimed to investigate the role of immune dysfunction in the pathogenesis of schizophrenia through single-cell transcriptome and bulk RNA data analyses.

METHODS

The single-cell RNA sequencing (scRNA-seq) was selected to assess the cellular composition and gene expression profiles of the brain tissue. Further, bulk RNA sequencing data was utilized to corroborate findings from the single-cell analyses and provide additional insights into the molecular changes associated with the disease. Gene-drug interaction data was also included to identify potential therapeutic drugs targeting these dysregulated immune-related genes in schizophrenia.

RESULTS

We discovered significant differences in cellular composition within schizophrenia tissue, including increased infiltration of fibroblasts, horizontal basal cells, monocytes, mesenchymal cells, and smooth muscle cells. The investigation of immune-related genes revealed significantly different expression of genes such as S100A2, CCL14, IGHA1, BPIFA1, GDF15, IL32, BPIFB2, HLA-DRA, S100A8, PTX3, TPM2, TNFRSF12A, GREM1 and others. These genes possibly contribute to the progression of schizophrenia through various pathways such as humoral immune response, IL-17 signaling pathway, adaptive immune response, antigen processing and presentation, and gut IgA production. Our findings also suggest possible transcriptional regulation in schizophrenia's immune dysfunction by transcription factors in monocytes, neutrophils, endothelial cells, and epithelial cells. Lastly, potential therapeutic drugs were identified through gene-drug interaction data, such as those targeting HLA-A and HLAB.

CONCLUSION

The cellular heterogeneity and immune-related gene dysregulation play important roles in schizophrenia, which provides a foundation for understanding the pathogenesis and developing new treatment methods.

Immunophenotypes in psychosis: is it a premature inflamm-aging disorder?

Immunopsychiatric field has rapidly accumulated evidence demonstrating the involvement of both innate and adaptive immune components in psychotic disorders such as schizophrenia. Nevertheless, researchers are facing dilemmas of discrepant findings of immunophenotypes both outside and inside the brains of psychotic patients, as discovered by recent meta-analyses. These discrepancies make interpretations and interrogations on their roles in psychosis remain vague and even controversial, regarding whether certain immune cells are more activated or less so, and whether they are causal or consequential, or beneficial or harmful for psychosis. Addressing these issues for psychosis is not at all trivial, as immune cells either outside or inside the brain are an enormously heterogeneous and plastic cell population, falling into a vast range of lineages and subgroups, and functioning differently and malleably in context-dependent manners. This review aims to overview the currently known immunophenotypes of patients with psychosis, and provocatively suggest the premature immune "burnout" or inflamm-aging initiated since organ development as a potential primary mechanism behind these immunophenotypes and the pathogenesis of psychotic disorders.

Immune Cell Alterations in Psychotic Disorders: A Comprehensive Systematic Review and Meta-Analysis

BACKGROUND

A comprehensive meta-analysis on the composition of circulating immune cells from both the myeloid and the lymphoid lines including specialized subsets in blood and cerebrospinal fluid (CSF) of patients with psychotic disorders compared with healthy control participants has been lacking.

METHODS

Multiple databases (PubMed, EMBASE, Cochrane Library, Web of Science, ClinicalTrials.gov, and PsycINFO) were searched for eligible studies up until October 18, 2022. All studies investigating circulating immune cells in the blood and CSF from patients with psychotic disorders (ICD-10: F20 and F22-29) compared with healthy control participants were included.

RESULTS

A total of 86 studies were included in the meta-analysis. In the blood, the following categories of immune cells were elevated: leukocyte count (31 studies, standardized mean difference [SMD] = 0.35; 95% CI, 0.24 to 0.46), granulocyte count (4 studies, SMD = 0.57; 95% CI, 0.12 to 1.01), neutrophil granulocyte count (21 studies, SMD = 0.32; 95% CI, 0.11 to 0.54), monocyte count (23 studies, SMD = 0.40; 95% CI, 0.23 to 0.56), and B lymphocyte count (10 studies, SMD = 0.26; 95% CI, 0.04 to 0.48). Additionally, the neutrophil/lymphocyte ratio (23 studies, SMD = 0.40; 95% CI, 0.19 to 0.60), the monocyte/lymphocyte ratio (9 studies, SMD = 0.31; 95% CI, 0.04 to 0.57), and the platelet/lymphocyte ratio (10 studies, SMD = 0.23; 95% CI, 0.03 to 0.43) were elevated. The CSF cell count showed a similar tendency but was not significantly elevated (3 studies, SMD = 0.14; 95% CI, -0.04 to 0.32).

CONCLUSIONS

The results indicate a broad activation of the immune system in psychotic disorders, with cells from both the myeloid and the lymphoid line being elevated. However, CSF analyses were lacking in most of the studies, and many studies were hampered by insufficient adjustment for confounding factors such as body mass index and smoking.

Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls

Introduction

Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches.

Methods

Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree.

Results

Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data.

Discussion

Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.

The multimodal Munich Clinical Deep Phenotyping study to bridge the translational gap in severe mental illness treatment research

Introduction

Treatment of severe mental illness (SMI) symptoms, especially negative symptoms and cognitive dysfunction in schizophrenia, remains a major unmet need. There is good evidence that SMIs have a strong genetic background and are characterized by multiple biological alterations, including disturbed brain circuits and connectivity, dysregulated neuronal excitation-inhibition, disturbed dopaminergic and glutamatergic pathways, and partially dysregulated inflammatory processes. The ways in which the dysregulated signaling pathways are interconnected remains largely unknown, in part because well-characterized clinical studies on comprehensive biomaterial are lacking. Furthermore, the development of drugs to treat SMIs such as schizophrenia is limited by the use of operationalized symptom-based clusters for diagnosis.

Methods

In line with the Research Domain Criteria initiative, the Clinical Deep Phenotyping (CDP) study is using a multimodal approach to reveal the neurobiological underpinnings of clinically relevant schizophrenia subgroups by performing broad transdiagnostic clinical characterization with standardized neurocognitive assessments, multimodal neuroimaging, electrophysiological assessments, retinal investigations, and omics-based analyzes of blood and cerebrospinal fluid. Moreover, to bridge the translational gap in biological psychiatry the study includes in vitro investigations on human-induced pluripotent stem cells, which are available from a subset of participants.

Results

Here, we report on the feasibility of this multimodal approach, which has been successfully initiated in the first participants in the CDP cohort; to date, the cohort comprises over 194 individuals with SMI and 187 age and gender matched healthy controls. In addition, we describe the applied research modalities and study objectives.

Discussion

The identification of cross-diagnostic and diagnosis-specific biotype-informed subgroups of patients and the translational dissection of those subgroups may help to pave the way toward precision medicine with artificial intelligence-supported tailored interventions and treatment. This aim is particularly important in psychiatry, a field where innovation is urgently needed because specific symptom domains, such as negative symptoms and cognitive dysfunction, and treatment-resistant symptoms in general are still difficult to treat.

Social interaction, psychotic disorders and inflammation: A triangle of interest

Social interaction difficulties are a hallmark of psychotic disorders, which in some cases can be definitely traced back to autoimmunological causes. Interestingly, systemic and intrathecal inflammation have been shown to significantly influence social processing by increasing sensitivity to threatening social stimuli, which bears some resemblance to psychosis. In this article, we review evidence for the involvement of systemic and intrathecal inflammatory processes in psychotic disorders and how this might help to explain some of the social impairments associated with this group of disorders. Vice versa, we also discuss evidence for the immunomodulatory function of social interactions and their potential role for therapeutic interventions in psychotic disorders.

Inflammation and cognition in severe mental illness: patterns of covariation and subgroups

A potential relationship between dysregulation of immune/inflammatory pathways and cognitive impairment has been suggested in severe mental illnesses (SMI), such as schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, multivariate relationships between peripheral inflammatory/immune-related markers and cognitive domains are unclear, and many studies do not account for inter-individual variance in both cognitive functioning and inflammatory/immune status. This study aimed to investigate covariance patterns between inflammatory/immune-related markers and cognitive domains and further elucidate heterogeneity in a large SMI and healthy control (HC) cohort (SZ = 343, BD = 289, HC = 770). We applied canonical correlation analysis (CCA) to identify modes of maximum covariation between a comprehensive selection of cognitive domains and inflammatory/immune markers. We found that poor verbal learning and psychomotor processing speed was associated with higher levels of interleukin-18 system cytokines and beta defensin 2, reflecting enhanced activation of innate immunity, a pattern augmented in SMI compared to HC. Applying hierarchical clustering on covariance patterns identified by the CCA revealed a high cognition-low immune dysregulation subgroup with predominantly HC (24% SZ, 45% BD, 74% HC) and a low cognition-high immune dysregulation subgroup predominantly consisting of SMI patients (76% SZ, 55% BD, 26% HC). These subgroups differed in IQ, years of education, age, CRP, BMI (all groups), level of functioning, symptoms and defined daily dose (DDD) of antipsychotics (SMI cohort). Our findings suggest a link between cognitive impairment and innate immune dysregulation in a subset of individuals with severe mental illness.

Supporting the differential diagnosis of connective tissue diseases with neurological involvement by blood and cerebrospinal fluid flow cytometry

OBJECTIVE

Neurological manifestations of autoimmune connective tissue diseases (CTD) are poorly understood and difficult to diagnose. We here aimed to address this shortcoming by studying immune cell compositions in CTD patients with and without neurological manifestation.

METHODS

Using flow cytometry, we retrospectively investigated paired cerebrospinal fluid (CSF) and blood samples of 28 CTD patients without neurological manifestation, 38 CTD patients with neurological manifestation (N-CTD), 38 non-inflammatory controls, and 38 multiple sclerosis (MS) patients, a paradigmatic primary neuroinflammatory disease.

RESULTS

We detected an expansion of plasma cells in the blood of both N-CTD and CTD compared to non-inflammatory controls and MS. Blood plasma cells alone distinguished the clinically similar entities N-CTD and MS with high discriminatory performance (AUC: 0.81). Classical blood monocytes indicated higher disease activity in systemic lupus erythematosus (SLE) patients. Surprisingly, immune cells in the CSF did not differ significantly between N-CTD and CTD, while CD4⁺ T cells and the CD4⁺/CD8⁺ ratio were elevated in the blood of N-CTD compared to CTD. Several B cell-associated parameters partially overlapped in the CSF in MS and N-CTD. We built a machine learning model that distinguished N-CTD from MS with high discriminatory power using either blood or CSF.

CONCLUSION

We here find that blood flow cytometry alone surprisingly suffices to distinguish CTD with neurological manifestations from clinically similar entities, suggesting that a rapid blood test could support clinicians in the differential diagnosis of N-CTD.

Association of polysialic acid serum levels with schizophrenia spectrum and bipolar disorder-related structural brain changes and hospitalization

Expression of polysialic acid (polySia) in the adult brain is enriched in areas of continuous neurogenesis and plasticity such as the hippocampus. Genome-wide association studies identified variants of glycosylation enzyme-encoding genes, required for the generation of polySia, to be associated with the development of schizophrenia and bipolar disorder. Here, we report that serum levels of polySia are increased in patients with schizophrenia spectrum disorder compared to patients with major depressive disorders or demographically matched healthy controls. Furthermore, elevated polySia serum levels are associated with structural hippocampal gray matter decline in schizophrenia spectrum and bipolar disorder. In patients with schizophrenia spectrum disorder, polySia serum levels correlate with the number, duration of disease-related hospitalizations, early retirement and medical leave as estimators of detrimental long-term disease trajectories. Our data show that polySia serum levels are linked to structural hippocampal brain changes in schizophrenia spectrum and bipolar disorders, and suggest a contribution of polySia to the pathophysiology of these diseases.

Epilepsy, Immunity and Neuropsychiatric Disorders

Several studies have focused on the emerging role of immunity and inflammation in a wide range of neurological disorders. Autoimmune diseases involving central nervous system share well defined clinical features including epileptic seizures and additional neuropsychiatric symptoms, like cognitive and psychiatric disturbances. The growing evidence about the role of immunity in the pathophysiologic mechanisms underlying these conditions lead to the concept of autoimmune epilepsy. This relatively-new term has been introduced to highlight the etiological and prognostic implications of immunity in epileptogenesis. In this review, we aim to discuss the role of autoimmunity in epileptogenesis and its clinical, neurophysiological, neuroimaging and therapeutic implications. Moreover, we wish to address the close relationship between immunity and additional symptoms, particularly cognitive and psychiatric features, which deeply impact clinical outcomes in these patients. To assess these aspects, we first analyzed Rasmussen's encephalitis. Subsequently, we have covered autoimmune encephalitis, particularly those associated with autoantibodies against surface neuronal antigens, as these autoantibodies express a direct immune-mediated mechanism, different from those against intracellular antigens. Then, we discussed the connection between systemic immune disorders and neurological manifestations. This review aims to highlight the need to expand knowledge about the role of inflammation and autoimmunity in the pathophysiology of neurological disorders and the importance to early recognize these clinical entities. Indeed, early identification may result in faster recovery and a better prognosis.

Myelinating Co-Culture as a Model to Study Anti-NMDAR Neurotoxicity

Anti-NMDA receptor (NMDAR) encephalitis is frequently associated with demyelinating disorders (e.g., multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein-associated disease (MOGAD)) with regard to clinical presentation, neuropathological and cerebrospinal fluid findings. Indeed, autoantibodies (AABs) against the GluN1 (NR1) subunit of the NMDAR diminish glutamatergic transmission in both neurons and oligodendrocytes, leading to a state of NMDAR hypofunction. Considering the vital role of oligodendroglial NMDAR signaling in neuron-glia communication and, in particular, in tightly regulated trophic support to neurons, the influence of GluN1 targeting on the physiology of myelinated axon may be of importance. We applied a myelinating spinal cord cell culture model that contains all major CNS cell types, to evaluate the effects of a patient-derived GluN1-specific monoclonal antibody (SSM5) on neuronal and myelin integrity. A non-brain reactive (12D7) antibody was used as the corresponding isotype control. We show that in cultures at the late stage of myelination, prolonged treatment with SSM5, but not 12D7, leads to neuronal damage. This is characterized by neurite blebbing and fragmentation, and a reduction in the number of myelinated axons. However, this significant toxic effect of SSM5 was not observed in earlier cultures at the beginning of myelination. Anti-GluN1 AABs induce neurodegenerative changes and associated myelin loss in myelinated spinal cord cultures. These findings may point to the higher vulnerability of myelinated neurons towards interference in glutamatergic communication, and may refer to the disturbance of the NMDAR-mediated oligodendrocyte metabolic supply. Our work contributes to the understanding of the emerging association of NMDAR encephalitis with demyelinating disorders.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Immunopsychiatry – Innovative Technology to Characterize Disease Activity in Autoantibody-Associated Psychiatric Diseases

Background

Anti-neural autoantibody-associated psychiatric disease is a novel field in immunopsychiatry that has been attracting attention thanks to its potentially positive therapeutic outcome and distinct prognosis compared with non-organic psychiatric disease. This review aims to describe recent novel technological developments for improving diagnostics in the field of autoantibody-related psychiatric disease.

Methods

We screened for relevant articles in PubMed for this narrative article. We focused on research methods such as neuroimaging, immune cells and inflammation markers, and molecular biomarkers in human biofluids like serum and cerebrospinal fluid and plasma proteomics.

Results

We introduce several novel methods for investigating autoinflammation with the aim of optimizing therapies for autoantibody-associated psychiatric disease. We describe measuring the translocator protein 18kDa in activated microglia via positron emission tomography imaging, brain volumetric assessment, flow cell cytometry of cerebrospinal fluid and blood, and blood biological probes as well as psychopathological cues to help us gain insights into diagnosing inflammation and brain damage better in psychiatric patients presenting a suspected autoimmune etiology.

Conclusion

Our short methodological review provides an overview of recent developments in the field of autoantibody-related immunopsychiatry. More research is needed to prove their usefulness in diagnosing and treating autoantibody-associated psychiatric disease and its subtypes.