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Studies of induced pluripotent stem cells (iPSCs) from schizophrenia patients and control individuals revealed that the disorder is programmed at the preneuronal stage, involves a common dysregulated mRNA transcriptome, and identified Integrative Nuclear FGFR1 Signaling a common dysregulated mechanism. We used human embryonic stem cell (hESC) and iPSC-derived cerebral organoids from four controls and three schizophrenia patients to model the first trimester of in utero brain development. The schizophrenia organoids revealed an abnormal scattering of proliferating Ki67+ neural progenitor cells (NPCs) from the ventricular zone (VZ), throughout the intermediate (IZ) and cortical (CZ) zones. TBR1 pioneer neurons and reelin, which guides cortico-petal migration, were restricted from the schizophrenia cortex. The maturing neurons were abundantly developed in the subcortical regions, but were depleted from the schizophrenia cortex. The decreased intracortical connectivity was denoted by changes in the orientation and morphology of calretinin interneurons. In schizophrenia organoids, nuclear (n)FGFR1 was abundantly expressed by developing subcortical cells, but was depleted from the neuronal committed cells (NCCs) of the CZ. Transfection of dominant negative and constitutively active nFGFR1 caused widespread disruption of the neuro-ontogenic gene networks in hESC-derived NPCs and NCCs. The fgfr1 gene was the most prominent FGFR gene expressed in NPCs and NCCs, and blocking with PD173074 reproduced both the loss of nFGFR1 and cortical neuronal maturation in hESC cerebral organoids. We report for the first time, progression of the cortical malformation in schizophrenia and link it to altered FGFR1 signaling. Targeting INFS may offer a preventive treatment of schizophrenia.

BackgroundPathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.MethodsWe developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification.ResultsOur digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen’s kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity.ConclusionsComputationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

The watershed-hypothesis of schizophrenia asserts that over 200 different mutations dysregulate distinct pathways that converge on an unspecified common mechanism(s) that controls disease ontogeny. Consistent with this hypothesis, our RNA-sequencing of neuron committed cells (NCCs) differentiated from established iPSCs of 4 schizophrenia patients and 4 control subjects uncovered a dysregulated transcriptome of 1349 mRNAs common to all patients. Data reveal global dysregulation of developmental genome, deconstruction of coordinated mRNA, and mRNA networks, and the formation of aberrant, new coordinated mRNA networks indicating a concerted action of the responsible factor(s). Sequencing of miRNA transcriptomes demonstrated an overexpression of 16 miRNAs and deconstruction of coordinated miRNA–mRNA networks in schizophrenia NCCs. ChiPseq revealed that the nuclear (n) form of FGFR1, a pan-ontogenic regulator, is overexpressed in schizophrenia NCCs and overtargets dysregulated mRNA and miRNA genes. The nFGFR1 targeted 54% of all human gene promoters and 84.4% of schizophrenia dysregulated genes. The upregulated genes reside within major developmental pathways that control neurogenesis and neuron formation, whereas downregulated genes are involved in oligodendrogenesis. Our results indicate (i) an early (preneuronal) genomic etiology of schizophrenia, (ii) dysregulated genes and new coordinated gene networks are common to unrelated cases of schizophrenia, (iii) gene dysregulations are accompanied by increased nFGFR1-genome interactions, and (iv) modeling of increased nFGFR1 by an overexpression of a nFGFR1 lead to up or downregulation of selected genes as observed in schizophrenia NCCs. Together our results designate nFGFR1 signaling as a potential common dysregulated mechanism in investigated patients and potential therapeutic target in schizophrenia.