Abstract RNA-sequencing studies of brain tissue homogenates have shed light on the molecular processes underlying schizophrenia (SCZ) but lack biological granularity at the cell type level. Laser capture microdissection (LCM) can isolate selective cell populations with intact cell bodies to allow complementary gene expression analyses of mRNA and protein. We used LCM to collect excitatory neuron-enriched samples from CA1 and subiculum (SUB) of the hippocampus and layer III of the dorsolateral prefrontal cortex (DLPFC), from which we generated gene, transcript, and peptide level data. In a machine learning framework, LCM-derived expression achieved superior regional identity predictions as compared to bulk tissue, with further improvements when using isoform-level transcript and protein quantifications. LCM-derived co-expression also had increased co-expression strength of neuronal gene sets compared to tissue homogenates. SCZ risk co-expression pathways were identified and replicated across transcript and protein networks and were consistently enriched for glutamate receptor complex and post-synaptic functions. Finally, through inter-regional co-expression analyses, we show that CA1 to SUB transcriptomic connectivity may be altered in SCZ.

Transcriptomics and proteomics of projection neurons in a circuit linking hippocampus with dorsolateral prefrontal cortex in human brain