Complex diseases such as schizophrenia (SCZ) result from multifactorial genetic and environmental perturbations that cause pleiotropic changes in molecular networks, resulting in disease. The goal of our project is to move our CommonMind Consortium (CMC) data generation to the next level, focusing on cell-type specific data generation including transcriptomics, epigenomics, proteomics, and integrated analyses, from critical regions implicated in SCZ to improve our ability to identify and refine genetic risk factors for SCZ. We will characterize cell-type specific transcriptome and epigenome components in our post-mortem CMC SCZ and control cohort and in new control autopsy specimens. In 50 SCZ cases and 50 controls from the CMC, we will 1.) perform RNA, sequencing, 2.) ATACseq, and 3.) Hi-C in nuclei isolated from pools of glutamatergic neurons, GABAergic neurons, and oligodendrocytes isolated from the prefrontal cortex. In the same samples we characterize proteins of post-synaptic density proteins by liquid chromatography (LC)-selective reaction monitoring (SRM)-mass spectrometer (MS) quantitative proteomic analyses. In order to uncover how many distinct types of cells are present in various brain regions of control individuals we will use nanofluidics and bar-coding (Drop-seq) from freshly autopsied, never frozen or fixed control specimens. Integrative analyses will be pursued that will combine genetic, gene expression, epigenomic and proteomic data to identify novel SCZ genes. Finally, we will continue to maintain and upgrade our community workspace that provides for the rapid dissemination and open evaluation of data, analyses, and outcomes derived from the CMC. We will continue to make all data available to the research community through the Sage Bionetworks Synapse Platform. There is a deep need to more cell-type specific information on the transcriptional and epigenetic landscape in the human brain, and in particular in neurons and glia and to integrate this information with human SCZ genetics. We have assembled the critical key personnel, sample resources, technological know-how, and analytic strategies to be able to provide both useful maps for the field, as well as begin to unravel SCZ biology.
In the United States, over a million people have schizophrenia with considerable morbidity, mortality, and personal and societal cost. We propose to use advanced and novel technologies to generate a broad range of genomic, epi-(Greek for `over', `above') genetic, and proteomic data collected from hundreds of postmortem brain samples from controls and from subjects who were diagnosed with schizophrenia. A particular strength of this proposal is that we will do much of this work in specific cell types that are often implicated in the disease. These data will provide a much needed resource to explore the genetic risk architecture and neurobiology of the disorder.
