Combining phenomics with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion

Matthew Tegtmeyer (), Dhara Liyanage, Yu Han, Kathryn B. Hebert, Ruifan Pei, Gregory P. Way, Pearl V. Ryder, Derek Hawes, Callum Tromans-Coia, Beth A. Cimini, Anne E. Carpenter, Shantanu Singh () and Ralda Nehme ()
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Matthew Tegtmeyer: Indiana University School of Medicine
Dhara Liyanage: Broad Institute of MIT and Harvard
Yu Han: Broad Institute of MIT and Harvard
Kathryn B. Hebert: Broad Institute of MIT and Harvard
Ruifan Pei: Broad Institute of MIT and Harvard
Gregory P. Way: University of Colorado School of Medicine
Pearl V. Ryder: Broad Institute of MIT and Harvard
Derek Hawes: Broad Institute of MIT and Harvard
Callum Tromans-Coia: Broad Institute of MIT and Harvard
Beth A. Cimini: Broad Institute of MIT and Harvard
Anne E. Carpenter: Broad Institute of MIT and Harvard
Shantanu Singh: Broad Institute of MIT and Harvard
Ralda Nehme: Broad Institute of MIT and Harvard

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Neuropsychiatric disorders remain difficult to treat due to complex and poorly understood mechanisms. NeuroPainting is a high-content morphological profiling assay based on Cell Painting and optimized for human stem cell–derived neural cell types, including neurons, progenitors, and astrocytes. The assay quantifies over 4000 features of cell structure and organelle organization, generating a dataset suitable for phenotypic screening in neural models. Here, we show that, in studies of the 22q11.2 deletion—a strong genetic risk factor for schizophrenia—we observe cell-type-specific effects, particularly in astrocytes, including mitochondrial disruption, altered endoplasmic reticulum organization, and cytoskeletal changes. Transcriptomic analysis shows reduced expression of cell adhesion genes in deletion astrocytes, consistent with post-mortem brain data. Integration of RNA and morphology data suggests a link between adhesion gene dysregulation and mitochondrial abnormalities. These results illustrate how combining image-based profiling with gene expression analysis can reveal cellular mechanisms associated with genetic risk in neuropsychiatric disease.

Date: 2025
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DOI: 10.1038/s41467-025-61547-x

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