JAMA psychiatry · 2026
Bridging the Scales via Personalized Cellular Modeling and Deep Phenotyping in Schizophrenia.
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Raabe Florian J, Popovic David, Vetter Clara, Fischer Laura E, Hasanaj Genc, Karsli Berkhan, Schäfer Tim J, Almeida Valeria, Atella Alessia, Gagliardi Miriam, Boudriot Emanuel, Yakimov Vladislav, Trastulla Lucia, Jiang Tengjia, Weyer Clara, Roell Lukas, Moussiopoulou Joanna, Krcmár Lenka, Galinski Sabrina, Papazova Irina, Pogarell Oliver, Hasan Alkomiet, Schulte Eva C, Schmitt Andrea, Koutsouleris Nikolaos, Levina Anna, Wagner Elias, Rossner Moritz J, Papiol Sergi, Falkai Peter, Keeser Daniel, Ziller Michael J, CDP Working Group
Аннотация
IMPORTANCE: While growing evidence implicates synaptic dysfunction as a key pathophysiological mechanism in cognitive impairments in schizophrenia (SCZ), it remains unknown how individual alterations in synaptic connectivity translate into corresponding neural circuit dysfunction and cognitive deficits.
OBJECTIVE: To test whether genetically driven variability in excitatory neurons' transcriptome and synapse density in patient-derived neurons in vitro explain individual changes in cortical morphology, electrophysiology, and cognitive impairments in vivo.
DESIGN, SETTING, AND PARTICIPANTS: This multimodal case-control study integrated deep clinical phenotyping (magnetic resonance imaging, electroencephalography, and cognitive assessments) across 2 independent cohorts with schizophrenia and healthy controls (N = 461) with donor-matched induced pluripotent stem cell (iPSC)-derived neurons (n = 80). Machine learning, transcriptome imputation, and reverse dynamic causal modeling were applied to link cellular and systems-level phenotypes. Data were collected between September 16, 2014, and November 10, 2023, and analyzed from January 2022 to January 2026.
MAIN OUTCOMES AND MEASURES: The primary outcome was associations between cellular phenotypes (gene expression, synapse density) and individual-level brain structure, electrophysiology, and cognition.
RESULTS: This multiscale translational framework was implemented in 461 individuals with SCZ and healthy controls across 2 independent cohorts. In both cohorts (cohort 1 [C1]: mean [SD] age: 35.1 [11.6] years; 46 female participants [31.1%]; cohort 2 [C2]: mean [SD] age, 36.9 [11.7] years; 140 female participants [44.57%]), cognitive impairments in SCZ were associated with specific gray matter volume reductions across multiple brain regions, in particular the right dorsolateral prefrontal cortex, as well as disturbed electrophysiological activity in the gamma band. Importantly, the individual-level differences in the genetically driven neuronal gene expression patterns and synapse density in vitro predicted the macro-scale alterations of brain structural (C1: r = 0.39; 95% CI, 0.21-0.55; P < .001; iPSC: r = 0.31; 95% CI, -0.07 to 0.60; P = .049; C2: r = 0.23; 95% CI, 0.07-0.37; P = .003), electrophysiological (theta: r = 0.19; 95% CI, 0.04-0.32; P = .05; gamma1: r = 0.17; 95% CI, 0.028-0.31; P = .005; gamma2: r = 0.22; 95% CI, 0.07-0.35; P < .001), and cognitive (C1: r = 0.76; 95% CI, 0.66-0.83; P < .001; iPSC: r = 0.77; 95% CI, 0.57-0.89; P < .001; C2: r = 0.17; 95% CI, 0.02-0.32; P = .02) phenotypes in vivo, providing a mechanistic link from synapse deficits to cognitive impairments in SCZ.
CONCLUSIONS AND RELEVANCE: These findings establish a patient-specific link between genetically driven alterations in gene expression, synaptic dysfunction, and large-scale brain and cognitive phenotypes in SCZ. This multiscale framework provides a foundation for mechanism-based stratification and precision target identification for cognitive impairment.
OBJECTIVE: To test whether genetically driven variability in excitatory neurons' transcriptome and synapse density in patient-derived neurons in vitro explain individual changes in cortical morphology, electrophysiology, and cognitive impairments in vivo.
DESIGN, SETTING, AND PARTICIPANTS: This multimodal case-control study integrated deep clinical phenotyping (magnetic resonance imaging, electroencephalography, and cognitive assessments) across 2 independent cohorts with schizophrenia and healthy controls (N = 461) with donor-matched induced pluripotent stem cell (iPSC)-derived neurons (n = 80). Machine learning, transcriptome imputation, and reverse dynamic causal modeling were applied to link cellular and systems-level phenotypes. Data were collected between September 16, 2014, and November 10, 2023, and analyzed from January 2022 to January 2026.
MAIN OUTCOMES AND MEASURES: The primary outcome was associations between cellular phenotypes (gene expression, synapse density) and individual-level brain structure, electrophysiology, and cognition.
RESULTS: This multiscale translational framework was implemented in 461 individuals with SCZ and healthy controls across 2 independent cohorts. In both cohorts (cohort 1 [C1]: mean [SD] age: 35.1 [11.6] years; 46 female participants [31.1%]; cohort 2 [C2]: mean [SD] age, 36.9 [11.7] years; 140 female participants [44.57%]), cognitive impairments in SCZ were associated with specific gray matter volume reductions across multiple brain regions, in particular the right dorsolateral prefrontal cortex, as well as disturbed electrophysiological activity in the gamma band. Importantly, the individual-level differences in the genetically driven neuronal gene expression patterns and synapse density in vitro predicted the macro-scale alterations of brain structural (C1: r = 0.39; 95% CI, 0.21-0.55; P < .001; iPSC: r = 0.31; 95% CI, -0.07 to 0.60; P = .049; C2: r = 0.23; 95% CI, 0.07-0.37; P = .003), electrophysiological (theta: r = 0.19; 95% CI, 0.04-0.32; P = .05; gamma1: r = 0.17; 95% CI, 0.028-0.31; P = .005; gamma2: r = 0.22; 95% CI, 0.07-0.35; P < .001), and cognitive (C1: r = 0.76; 95% CI, 0.66-0.83; P < .001; iPSC: r = 0.77; 95% CI, 0.57-0.89; P < .001; C2: r = 0.17; 95% CI, 0.02-0.32; P = .02) phenotypes in vivo, providing a mechanistic link from synapse deficits to cognitive impairments in SCZ.
CONCLUSIONS AND RELEVANCE: These findings establish a patient-specific link between genetically driven alterations in gene expression, synaptic dysfunction, and large-scale brain and cognitive phenotypes in SCZ. This multiscale framework provides a foundation for mechanism-based stratification and precision target identification for cognitive impairment.