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Improved split fluorescent proteins for endogenous protein labeling

Author

Listed:
  • Siyu Feng

    (The UC Berkeley-UCSF Graduate Program in Bioengineering)

  • Sayaka Sekine

    (University of California in San Francisco)

  • Veronica Pessino

    (University of California, San Francisco)

  • Han Li

    (University of California, San Francisco)

  • Manuel D. Leonetti

    (University of California, San Francisco)

  • Bo Huang

    (University of California in San Francisco
    University of California, San Francisco
    Chan Zuckerberg Biohub)

Abstract

Self-complementing split fluorescent proteins (FPs) have been widely used for protein labeling, visualization of subcellular protein localization, and detection of cell–cell contact. To expand this toolset, we have developed a screening strategy for the direct engineering of self-complementing split FPs. Via this strategy, we have generated a yellow–green split-mNeonGreen21–10/11 that improves the ratio of complemented signal to the background of FP1–10-expressing cells compared to the commonly used split GFP1–10/11; as well as a 10-fold brighter red-colored split-sfCherry21–10/11. Based on split sfCherry2, we have engineered a photoactivatable variant that enables single-molecule localization-based super-resolution microscopy. We have demonstrated dual-color endogenous protein tagging with sfCherry211 and GFP11, revealing that endoplasmic reticulum translocon complex Sec61B has reduced abundance in certain peripheral tubules. These new split FPs not only offer multiple colors for imaging interaction networks of endogenous proteins, but also hold the potential to provide orthogonal handles for biochemical isolation of native protein complexes.

Suggested Citation

  • Siyu Feng & Sayaka Sekine & Veronica Pessino & Han Li & Manuel D. Leonetti & Bo Huang, 2017. "Improved split fluorescent proteins for endogenous protein labeling," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00494-8
    DOI: 10.1038/s41467-017-00494-8
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    Cited by:

    1. Anwar SidAhmed & Francesco Holecz & Luca Gatti & Massimo Barbieri & Alyas Ahmed & Abdalla Gafar & Mohamed A. E. AbdelRahman & Abdalazeem Yassin, 2025. "Inference of forest tree volume using synthetic aperture in central Sudan," Letters in Spatial and Resource Sciences, Springer, vol. 18(1), pages 1-24, December.

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