What can we learn about brain donors? Use of clinical information in human postmortem brain research

A concerted neuron-astrocyte program declines in ageing and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.

Concerted neuron-astrocyte gene expression declines in aging and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Background

Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets.

Method

A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array.

Results

Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder.

Conclusion

Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.

Brain Bank Questionnaire Helps in Differential Diagnosis of Movement Disorders: An Autopsy Study of 150 Patients

Background

As part of the CurePSP brain donation program, a questionnaire was developed to gather basic clinical information on donors; however, its usefulness has not been evaluated.

Objective

To assess the value of information obtained from the questionnaire in differentiating between parkinsonian disorders.

Methods

We reviewed 150 questionnaires, including 50 patients, each with a neuropathologic diagnosis of Lewy body disease (LBD), multiple system atrophy (MSA), or progressive supranuclear palsy. The frequency of clinical features recorded in the questionnaires was compared for the three disorders, and a machine learning algorithm was used to identify features predicting neuropathologic diagnosis.

Results

The information from the questionnaires correlated with core clinical features for each disorder, such as hallucinations for LBD and autonomic dysfunction for MSA. Hallucinations and disorientations were identified as the key variables that contributed most to the prediction of neuropathology.

Conclusion

The questionnaire provides useful clinical information for clinicopathological correlative studies.

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology

INTRODUCTION

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records.

METHODS

We adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimer's disease (AD) neuropathological measures.

RESULTS

Our results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß=0.38, p=0.0004), parietal (ß=0.35, p=0.0008), temporal (ß=0.37, p=0. 0004) and occipital (ß=0.37, p=0.0003) lobes.

DISCUSSION

This proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

Altered expression of somatostatin signaling molecules and clock genes in the hippocampus of subjects with substance use disorder

Substance use disorders are a debilitating group of psychiatric disorders with a high degree of comorbidity with major depressive disorder. Sleep and circadian rhythm disturbances are commonly reported in people with substance use disorder and major depression and associated with increased risk of relapse. Hippocampal somatostatin signaling is involved in encoding and consolidation of contextual memories which contribute to relapse in substance use disorder. Somatostatin and clock genes also have been implicated in depression, suggesting that these molecules may represent key converging pathways involved in contextual memory processing in substance use and major depression. We used hippocampal tissue from a cohort of subjects with substance use disorder (n = 20), subjects with major depression (n = 20), subjects with comorbid substance use disorder and major depression (n = 24) and psychiatrically normal control subjects (n = 20) to test the hypothesis that expression of genes involved in somatostatin signaling and clock genes is altered in subjects with substance use disorder. We identified decreased expression of somatostatin in subjects with substance use disorder and in subjects with major depression. We also observed increased somatostatin receptor 2 expression in subjects with substance use disorder with alcohol in the blood at death and decreased expression in subjects with major depression. Expression of the clock genes Arntl, Nr1d1, Per2 and Cry2 was increased in subjects with substance use disorder. Arntl and Nr1d1 expression in comparison was decreased in subjects with major depression. We observed decreased expression of Gsk3β in subjects with substance use disorder. Subjects with comorbid substance use disorder and major depression displayed minimal changes across all outcome measures. Furthermore, we observed a significant increase in history of sleep disturbances in subjects with substance use disorder. Our findings represent the first evidence for altered somatostatin and clock gene expression in the hippocampus of subjects with substance use disorder and subjects with major depression. Altered expression of these molecules may impact memory consolidation and contribute to relapse risk.

The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress

Growing evidence points to a disruption of cortico-thalamo-cortical circuits in schizophrenia (SZ) and bipolar disorder (BD). Clues for a specific involvement of the thalamic reticular nucleus (TRN) come from its unique neuronal characteristics and neural connectivity, allowing it to shape the thalamo-cortical information flow. A direct involvement of the TRN in SZ and BD has not been tested thus far. We used a combination of human postmortem and rodent studies to test the hypothesis that neurons expressing parvalbumin (PV neurons), a main TRN neuronal population, and associated Wisteria floribunda agglutinin-labeled perineuronal nets (WFA/PNNs) are altered in SZ and BD, and that these changes may occur early in the course of the disease as a consequence of oxidative stress. In both disease groups, marked decreases of PV neurons (immunoreactive for PV) and WFA/PNNs were observed in the TRN, with no effects of duration of illness or age at onset. Similarly, in transgenic mice with redox dysregulation, numbers of PV neurons and WFA/PNN+PV neurons were decreased in transgenic compared with wild-type mice; these changes were present at postnatal day (P) 20 for PV neurons and P40 for WFA/PNN+PV neurons, accompanied by alterations of their firing properties. These results show profound abnormalities of PV neurons in the TRN of subjects with SZ and BD, and offer support for the hypothesis that oxidative stress may play a key role in impacting TRN PV neurons at early stages of these disorders. We put forth that these TRN abnormalities may contribute to disruptions of sleep spindles, focused attention and emotion processing in these disorders.