The clinical symptoms of individuals with schizophrenia can be predicted by the activity of neurons derived from the patients’ own stem cells, according to new research published in the Proceedings of the National Academy of Sciences of the United States of America. The findings shed light on the specific biological features that distinguish healthy brain cells from ill ones.
“Our understanding of underlying pathophysiology in schizophrenia is limited and current treatments that address cognitive and negative symptoms in schizophrenia are lacking,” said study author Brady Maher, the lead investigator at the Lieber Institute for Brain Development and an associate professor at Johns Hopkins University. “Stem cells derived from patients provides a new method to model schizophrenia in the lab, which we hope will lead to new insights about pathophysiology and lead to the development of novel therapeutic treatments.”
Previously, researchers have had to rely on animal models and postmortem studies of human brain tissue. Neurons derived from pluripotent stem cells provide scientists with a new method of studying neuropsychiatric disorders at the cellular level. For their study, the researchers compared stem-cell-derived neurons from 13 individuals with a clinical diagnosis of schizophrenia to neurons derived from 15 neurotypical individuals. The subjects were extensively screened by obtaining medical, psychiatric and neurological histories, physical examinations, developmental histories, MRI scans, and genome-wide genotyping.
“Stem cells can be derived from the patient which contain all the genetic variants that contributed to that individual’s development of schizophrenia,” Maher explained to PsyPost. “We can differentiate these stem cells into neurons and the function of these neurons is associated with the patient’s clinical and cognitive dysfunction. Therefore, these stem cell derived neurons are suitable models of schizophrenia and should help us develop new therapies for schizophrenia.”
The researchers observed altered ion channel function in neurons derived from schizophrenia patients compared with those of neurotypical individuals. The channels regulate electrical activity in the brain and their abnormal behavior was associated with clinical symptoms of schizophrenia.
“We studied physiological characteristics of stem cell-derived neurons and determined which neurons predicted meaningful clinical features of the disorder in actual patients, the living donors of the cells,” co-author Stephanie Page explained. “We found a pattern of cell activity that correlated with the degree of psychosis in the donors. We found another pattern of activity that predicted with almost absolute accuracy the level of cognitive impairment of the donors. These clinical features, psychotic symptoms and cognitive deficits, are the cardinal manifestations of schizophrenia.”
The findings could potentially lead to new treatments for schizophrenia.
“Current treatments for schizophrenia address only symptoms of psychosis, like hallucinations and delusions,” Brady said. “Treatments targeting cognitive deficits are entirely lacking. Our study is a first step to developing cognitive therapies in schizophrenia that can treat these negative symptoms. That would ease a lot of suffering for these patients and their families. It could also help individuals with schizophrenia lead more productive lives.”
“Although the study is currently having the largest sample size for this type of study, having a larger sample size will further enhance our confidence in the results. We are currently planning on performing a replication study,” Maher added. “To begin to identify therapeutic targets we need to understand what mechanisms are leading to the differences in ion channel function. For instance, which specific ion channels or auxiliary proteins are involved in the observed differences. These studies are currently underway.”
The study, “Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance“, was authored by Stephanie Cerceo Page, Srinidhi Rao Sripathy, Federica Farinelli, Zengyou Ye, Yanhong Wang, Daniel J. Hiler, Elizabeth A. Pattie, Claudia V. Nguyen, Madhavi Tippani, Rebecca L. Moses, Huei-Ying Chen, Matthew Nguyen Tran, Nicholas J. Eagles, Joshua M. Stolz, Joseph L. Catallini II, Olivia R. Soudry, Dwight Dickinson, Karen F. Berman, Jose A. Apud, Daniel R. Weinberger, Keri Martinowich, Andrew E. Jaffe, Richard E. Straub, and Brady J. Maher.
