Back

FilaBuster: A Strategy for Rapid, Specific, and Spatiotemporally Controlled Intermediate Filament Disassembly

Moore, A. S.; Krug, T.; Hansen, S. B.; Ludlow, A. V.; Grimm, J. B.; Ayala, A. X.; Plutkis, S. E.; Wang, N.; Goldman, R. D.; Medalia, O.; Lavis, L. D.; Weitz, D. A.; Lippincott-Schwartz, J.

2025-04-21 cell biology
10.1101/2025.04.20.649718 bioRxiv
Show abstract

Intermediate filaments (IFs) play key roles in cellular mechanics, signaling, and organization, but tools for their rapid, selective disassembly remain limited. Here, we introduce FilaBuster, a photochemical approach for efficient and spatiotemporally controlled IF disassembly in living cells. FilaBuster uses a three-step strategy: (1) targeting HaloTag to IFs, (2) labeling with a covalent photosensitizer ligand, and (3) light-induced generation of localized reactive oxygen species to trigger filament disassembly. This modular strategy applies broadly across IF subtypes--including vimentin, GFAP, desmin, peripherin, and keratin 18--and is compatible with diverse dyes and imaging platforms. Using vimentin IFs as a model system, we establish a baseline implementation in which vimentin-HaloTag labeled with a photosensitizer HaloTag ligand triggers rapid and specific IF disassembly upon light activation. We then refine this approach by (i) expanding targeting strategies to include a vimentin nanobody-HaloTag fusion, (ii) broadening the range of effective photosensitizers, and (iii) optimizing irradiation parameters to enable precise spatial control over filament disassembly. Together, these findings position FilaBuster as a robust platform for acute, selective, and spatiotemporally precise disassembly of IF networks, enabling new investigations into their structural and functional roles in cell physiology and disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.