New considerations for hiPSC-based models of neuropsychiatric disorders

Abstract

The development of human induced pluripotent stem cells (hiPSCs) has made possible patient-specific modeling across the spectrum of human disease. Here we discuss recent advances in psychiatric genomics and post-mortem studies that provide critical insights concerning cell type composition and sample size that should be considered when designing hiPSC-based studies of complex genetic disease. We review recent hiPSC-based models of SZ, in light of our new understanding of critical power limitations in the design of hiPSC-based studies of complex genetic disorders. Three possible solutions are a movement towards genetically stratified cohorts of rare variant patients, application of CRISPR technologies to engineer isogenic neural cells to study the impact of common variants, and integration of advanced genetics and hiPSC-based datasets in future studies. Overall, we emphasize that to advance the reproducibility and relevance of hiPSC-based studies, stem cell biologists must contemplate statistical and biological considerations that are already well accepted in the field of genetics. We conclude with a discussion of the hypothesis of biological convergence of disease - through molecular, cellular, circuit and patient level phenotypes - and how this might emerge through hiPSC-based studies.

Stem Cells, Genome Editing, and the Path to Translational Medicine

“…Without detailed knowledge of the underlying genetics, patient-derived hiPSCs after in vitro differentiation provide a nearly unlimited supply of disease-relevant somatic cells for in vitro modelling that carry all the genetic elements implicated in disease development. Comparing neurons derived from patients with schizophrenia or autism spectrum disorders to age-matched unaffected individuals (Figure 1B) identified disease-relevant phenotypic changes providing proof-of concept to modeling sporadic diseases (reviewed in (Ardhanareeswaran et al, 2017; Hoffman et al, 2018). While initially surprising, considering the patients’ genetic heterogeneity, such robust disease-associated phenotypes may result from diverse genetic variants that converge on common developmental pathways such as cortical development, synaptic function and epigenetic processes (Hoffman et al, 2018).…”

“…Comparing neurons derived from patients with schizophrenia or autism spectrum disorders to age-matched unaffected individuals (Figure 1B) identified disease-relevant phenotypic changes providing proof-of concept to modeling sporadic diseases (reviewed in (Ardhanareeswaran et al, 2017; Hoffman et al, 2018). While initially surprising, considering the patients’ genetic heterogeneity, such robust disease-associated phenotypes may result from diverse genetic variants that converge on common developmental pathways such as cortical development, synaptic function and epigenetic processes (Hoffman et al, 2018). To reduce variability and facilitate the identification of disease-associated phenotypes, large-scale hiPSC consortia have significantly increased the number of independent samples (HD iPSC Consortium, 2012; Soares et al, 2014).…”

“…Following in vitro differentiation, patient-derived hiPSCs provide disease-relevant somatic cells, which carry all the genetic elements implicated in the disease reflecting the genetic spectrum of the patient population. While discussing all of these studies is beyond the scope of this work, excellent recent reviews summarize hiPSC diseases models for neurodevelopmental (Ardhanareeswaran et al, 2017), neuropsychiatric (Hoffman et al, 2018) and neurodegenerative diseases including Alzheimer’s disease (Sullivan and Young-Pearse, 2017) and Parkinson’s disease (Shi et al, 2016; Zhang et al, 2017)), motor neuron and Huntington’s disease. The spectrum of phenotypes, which can be assessed in hiPSC-based in vitro models include a wide range of molecular, metabolic, cellular and electrophysiological analysis.…”

“…One approach uses a polygenic risk score, that summarizes the number of GWAS identified risk alleles and the respective effect size for genetically informed patient stratification (Lewis and Vassos, 2017). Stratification of patients with schizophrenia by polygenic risk scores (Hoffman et al, 2018) or patients with autism spectrum disorders by common clinical features such as macrocephaly or microcephaly have facilitated the identification of robust diseases- associated molecular and cellular phenotypes (Marchetto et al, 2015; Mariani et al, 2017). A similar approach using genetic risk information has been successfully applied to neurodegenerative diseases to stratify patients, which carry rare, high-risk mutations in GBA (glucosylceramidase beta) associated with increased risk to develop Parkinson’s disease (Schöndorf et al, 2014) or specific APOE (Huang et al, 2017) or SORL1 (sortilin-related receptor, L(DLR class) A repeats containing) alleles (Young et al, 2015) associated with increased risk to develop Alzheimer’s disease.…”

Next‐generation disease modeling with direct conversion: a new path to old neurons

“…Thus, material-intensive technologies or big screens are challenging with iNs [171]. Here, iNs provide the opportunity to advance the reproducibility and relevance of human disease modeling studies, as higher patient and control numbers can be used, thereby making the application of powerful statistical/bioinformatical tools for data analysis useful [172]. Biological variability between human samples and cell lines of different genetic backgrounds has been identified as a major challenge in human iPSC-based disease modeling [60].…”