Parkinson's disease (PD) is a progressive, neurodegenerative disorder characterized by motor and non-motor symptoms. To date, no specific treatment to halt disease progression is available, only medication to alleviate symptoms can be prescribed. The main pathological hallmark of PD is the development of neuronal inclusions, positive for α-synuclein (α-syn), which are termed Lewy bodies (LBs) or Lewy neurites. However, the cause of the inclusion formation and the loss of neurons remain largely elusive. Various genetic determinants were reported to be involved in PD etiology, including SNCA, DJ-1, PRKN, PINK1, LRRK2, and GBA. Comprehensive insights into pathophysiology of PD critically depend on appropriate models. However, conventional model organisms fall short to faithfully recapitulate some features of this complex disease and as a matter-of-fact access to physiological tissue is limiting. The development of disease models replicating PD that are close to human physiology and dynamic enough to analyze the underlying molecular mechanisms of disease initiation and progression, as well as the generation of new treatment options, is an important and overdue step. Recently, the establishment of induced pluripotent stem cell (iPSC)-derived neural models, particularly from genetic PD-variants, developed into a promising strategy to investigate the molecular mechanisms regarding formation of inclusions and neurodegeneration. As these iPSC-derived neurons can be generated from accessible biopsied samples of PD patients, they carry pathological alterations and enable the possibility to analyze the differences compared to healthy neurons. This review focuses on iPSC models carrying genetic PD-variants of α-syn that will be especially helpful in elucidating the pathophysiological mechanisms of PD. Furthermore, we discuss how iPSC models can be instrumental in identifying cellular targets, potentially leading to the development of new therapeutic treatments. We will outline the enormous potential, but also discuss the limitations of iPSC-based α-syn models.

Schizophrenia is a neuropsychiatric disorder, caused by a combination of genetic and environmental factors. Recently, metabolomic studies based on patients’ biofluids and post-mortem brain specimens have revealed altered levels of distinct metabolites between healthy individuals and patients with schizophrenia (SCZ). However, a putative link between dysregulated metabolites and distorted neurodevelopment has not been assessed and access to patients’ material is restricted. In this study, we aimed to investigate a presumed correlation between transcriptomics and metabolomics in a SCZ model using patient-derived induced pluripotent stem cells (iPSCs). iPSCs were differentiated towards cortical neurons and samples were collected longitudinally at defined developmental stages, such as neuroepithelium, radial glia, young and mature neurons. Samples were subsequently analyzed by bulk RNA-sequencing and targeted metabolomics. The transcriptomic analysis revealed dysregulations in several extracellular matrix-related genes in the SCZ samples observed in early neurogenesis, including members of the collagen superfamily. At the metabolic level, several lipid and amino acid discrepancies were correlated to the SCZ phenotype. By employing a novel in silico analysis, we correlated the transcriptome with the metabolome through the generation of integrative networks. The network comparison between SCZ and healthy controls revealed a number of consistently affected pathways in SCZ, related to early stages of cortical development, indicating abnormalities in membrane composition, lipid homeostasis and amino acid imbalances. Ultimately, our study suggests a novel approach of correlating in vitro metabolic and transcriptomic data obtained from a patient-derived iPSC model. This type of analysis will offer novel insights in cellular and genetic mechanisms underlying the pathogenesis of complex neuropsychiatric disorders, such as schizophrenia.