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Label-free identification of protein aggregates using deep learning

Author

Listed:
  • Khalid A. Ibrahim

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Kristin S. Grußmayer

    (Delft University of Technology)

  • Nathan Riguet

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Lely Feletti

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Hilal A. Lashuel

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Aleksandra Radenovic

    (École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington’s disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information.

Suggested Citation

  • Khalid A. Ibrahim & Kristin S. Grußmayer & Nathan Riguet & Lely Feletti & Hilal A. Lashuel & Aleksandra Radenovic, 2023. "Label-free identification of protein aggregates using deep learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43440-7
    DOI: 10.1038/s41467-023-43440-7
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    References listed on IDEAS

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    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Montserrat Arrasate & Siddhartha Mitra & Erik S. Schweitzer & Mark R. Segal & Steven Finkbeiner, 2004. "Inclusion body formation reduces levels of mutant huntingtin and the risk of neuronal death," Nature, Nature, vol. 431(7010), pages 805-810, October.
    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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