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Cited by 10Genomics and Rare DiseasesNuclear Structure and FunctionCell Image Analysis Techniques

Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects

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Pendyala, Sriram, Partington, Katie, Bradley, Nicholas, McEwen, Abbye E., Straub, Gwenneth, Kim, Hyeon-Jin, Fayer, Shawn, Holmes, Daniel Lee +15 more·2026·Cell
DOI 10.1016/j.cell.2026.04.031PMC PMC13235708

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Abstract

Genetic variants produce complex phenotypic effects that confound current assays and predictive models. We developed variant in situ sequencing (VIS-seq), a pooled, image-based method measuring variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to ∼3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles capturing changes in protein abundance, localization, activity, and cell architecture. VIS-seq identified a subset of linker-subdomain LMNA variants that increase nuclear circularity, in contrast to aggregating or low-abundance rod-subdomain variants that decrease circularity. VIS-seq also identified autism-associated PTEN variants that mislocalize and accurately distinguished autism-linked from tumor syndrome-linked and gnomAD control variants. Most variants impacted a multidimensional phenotypic continuum not recapitulated by any single functional readout. By linking variants to cell images at scale, VIS-seq illuminates how variant effects cascade from molecules to subcellular structures to cells, providing a framework for resolving the complexity of variant function.

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  • Imaging-based MAVE readouts have recently been applied successfully to LMNA and PTEN, both genes with complicated genotype-phenotype relationships and endocrine disease associations.

    “Such imaging approaches as MAVE readouts has recently been applied successfully to LMNA and PTEN, both genes with complicated genotype–phenotype relationships and endocrine disease associations (97).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [97]

    The Introduction and Discussion report successful image-based profiling of LMNA and PTEN variants and describe both genes as having complex relationships between variants and disease (page numbers unavailable). The cited paper does not establish endocrine disease associations for both genes: its PTEN Results discuss tumor predisposition and autism, while a reference title mentions LMNA-associated lipodystrophy. (LLM, conf=0.88)

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Authors
Pendyala, S., Partington, K., Bradley, N., McEwen, A. E., Straub, G., Kim, H.-J., Fayer, S., Holmes, D. L., Sitko, K. A., Garge, R. K., Wang, Z. R., Wheelock, M. K., Vandi, A. J., Powell, R. L., Friedman, C. E., McDermot, E., Kishore, N., Roth, F. P., Rubin, A. F., Yang, K.-C., Starita, L. M., Noble, W. S., & Fowler, D. M.
Affiliations
  • University of Washington (US)
  • University of Toronto (CA)
  • University of Pittsburgh (US)
  • The University of Melbourne (AU)
Corresponding author
Fowler, Douglas M.
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2026-06
Volume
189
Issue
13
Pages
4040-4058.e31
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2026.04.031
PMC
PMC13235708
OpenAlex
W4412056932
Cited by
10
References
1
Citations by year
2026: 10
FWCI
32.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • Alex's Lemonade Stand Foundation
  • US Department of Veterans Affairs
  • National Heart Lung and Blood Institute
  • The Chan Zuckerberg Initiative
  • National Human Genome Research Institute
  • Novo Nordisk Foundation
  • National Institute of General Medical Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Nuclear Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords
LMNA mutations, protein mislocalization, subcellular localization, variant effect prediction, cell morphology

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