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

Author

Listed:
  • Matthew Tegtmeyer

    (Indiana University School of Medicine
    Broad Institute of MIT and Harvard
    Purdue University)

  • Dhara Liyanage

    (Broad Institute of MIT and Harvard
    Harvard University)

  • Yu Han

    (Broad Institute of MIT and Harvard)

  • Kathryn B. Hebert

    (Broad Institute of MIT and Harvard
    Harvard University)

  • 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
    Harvard University)

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.

Suggested Citation

  • 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, 2025. "Combining phenomics with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61547-x
    DOI: 10.1038/s41467-025-61547-x
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    References listed on IDEAS

    as
    1. Emi Ling & James Nemesh & Melissa Goldman & Nolan Kamitaki & Nora Reed & Robert E. Handsaker & Giulio Genovese & Jonathan S. Vogelgsang & Sherif Gerges & Seva Kashin & Sulagna Ghosh & John M. Esposito, 2024. "A concerted neuron–astrocyte program declines in ageing and schizophrenia," Nature, Nature, vol. 627(8004), pages 604-611, March.
    2. Ralda Nehme & Olli Pietiläinen & Mykyta Artomov & Matthew Tegtmeyer & Vera Valakh & Leevi Lehtonen & Christina Bell & Tarjinder Singh & Aditi Trehan & John Sherwood & Danielle Manning & Emily Peirent , 2022. "The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    3. Lauren Schiff & Bianca Migliori & Ye Chen & Deidre Carter & Caitlyn Bonilla & Jenna Hall & Minjie Fan & Edmund Tam & Sara Ahadi & Brodie Fischbacher & Anton Geraschenko & Christopher J. Hunter & Subha, 2022. "Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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