PROJECT SUMMARY
The symptoms of schizophrenia (SCZ) compromise major aspects of a patient's life and that of their families, and
the etiology of the disease is still unknown. There is agreement on the role of genetic vulnerability, but the
risk genes are so many that it is difficult to explain by which mechanisms they confer liability to SCZ. Recent
evidence shows that the co-regulation of gene expression may be a mechanism of SCZ risk, i.e., the concerted
action of genes, more than the function of individual genes, may be critical for SCZ etiology.
This project proposal stems from two research questions. First, how gene co-expression networks unfold
across the lifespan. Computational advancements have greatly increased the accuracy of RNA sequencing
measures, and it is now possible to investigate the variation of gene co-expression over the lifespan. The
underlying hypothesis is that genetic variation is translated into pathophysiology via molecular
alteration of neurodevelopmental trajectories. Second, most existing gene coexpression studies involve
brain tissue homogenates, which contain many cell types with likely differing co-expression patterns. Instead,
laser capture microdissection (LCM) allows isolating distinct cellular populations with intact cell bodies prior to
RNA quantification, thus affording relative cell-specificity. Several biological functions affected in SCZ involve
neuronal genes, hence gene expression in neurons may reveal co-expression patterns with greater
relevance to the pathophysiology of SCZ than tissue homogenates. This project will identify and
resolve the technical challenges of studying the course of temporally co-regulated gene expression (Aim 1)
and provide preliminary evidence for its feasibility in neuronal populations from multiple brain regions in a
canonical circuit linking hippocampus with prefrontal cortex (Aim 2).
Aim 1. Preliminary data from RNAseq analysis of dorsolateral prefrontal (DLPFC) homogenate tissue show the
feasibility and potential of the approach; however, these techniques have not yet been applied at a cell-specific
level. Two additional available datasets, i.e., hippocampal homogenate tissue and dentate gyrus cells (DG)
obtained via LCM, will allow for further insight and technical development. The comparison of DLPFC with
hippocampal homogenate data will reveal whether archicortical structures present specific challenges to the
assumptions of gene co-expression network analysis; comparing hippocampal homogenate with DG data will
ensure that LCM affords sufficient signal to noise ratio to compute valid dynamic networks in a homogeneous
cell population.
Aim2. This project will study 20 individuals, ten patients with SCZ and ten neurotypical subjects, as a proof of
concept of the feasibility and importance of studying single cell gene co-expression patterns. This approach will
be applied to neurons from the CA1 of the hippocampus, the subiculum and the DLPFC, regions
monosynaptically connected to form a critical brain circuit implicated in SCZ. Building on the evidence collected
in this project proposal, future studies will use the laboratory protocols developed here to collect a unique
larger dataset of LCM-based RNA sequencing data in developmental ages from SCZ-associated
brain circuits.