Barchas, J. D. | bioRxiv Science

bioRxiv2024

Cell-Type Specific Reductions in Interneuron Gene Expression within Subregions of the Anterior and Posterior Cingulate Gyrus of Schizophrenia and Bipolar Disorder Subjects

Schizophrenia (SZ) and bipolar disorder (BP) patients share overlapping and distinct neurocognitive deficits. Neuroimaging of these patients and postmortem gene expression analyses suggest that compromised cingulate gyrus GABA-ergic interneurons may contribute to cognitive impairments in SZ and BP. Therefore, we investigated potential gene expression signatures for SZ and BP using interneuron cell-type specific markers including glutamic acid decarboxylase (GAD67), parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP) within cingulate Brodmanns areas (BA). We report reduced GAD67 mRNA in anterior midcingulate cortex (aMCC) of SZ and BP subjects with BA24c being most dysregulated across disorders, demonstrating reduced PV (SZ), SST (BP), and VIP mRNA (SZ and BP). Dorsal posterior cingulate (dPCC) displayed decreased SST (BP) whereas retrosplenial cortex (RSC) showed reduced PV (SZ and BP) and SST mRNA (BP). Our results show shared and unique transcription signatures of two disorders in specific cingulate gyrus regions and cell types. SZ and BP show a similar profile of aMCC gene expression reductions suggesting subregional dysregulation within areas associated with error/action monitoring and the saliency network. In dPCC/RSC, transcriptional changes are associated with disease-specific gene/subregion signatures, possibly underlying differential effects on allocation of attentional resources and visuospatial memory processing unique to each disorder.

neuroscience↗

bioRxiv2016

INFERENCE OF CELL TYPE COMPOSITION FROM HUMAN BRAIN TRANSCRIPTOMIC DATASETS ILLUMINATES THE EFFECTS OF AGE, MANNER OF DEATH, DISSECTION, AND PSYCHIATRIC DIAGNOSIS

Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released: https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.\n\nThis variability was not simply due to dissection - the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

bioinformatics↗