Deep and high-resolution 3D tracking of single particles using nonlinear and multiplexed illumination (TSUNAMI)

Molecular trafficking within cells, tissues, and engineered 3D multicellular models is critical to the understanding of the development and treatment of various diseases including cancer. However, current tracking methods are either confined to two dimensions or limited to an interrogation depth of ~15 μm. To achieve deep and high-resolution 3D tracking, we have developed a two-photon, 3D single-particle tracking (2P-3D-SPT) method capable of tracking particles at depths up to 200 μm in scattering samples with 22/90 [xy/z] nm spatial localization precision and 50 µs temporal resolution. At shallow depths the localization precision can be as good as 35 nm in all three dimensions. The approach is based on passive pulse splitters used for nonlinear microscopy to achieve spatiotemporally multiplexed 2P excitation and temporally demultiplexed detection to discern the 3D position of the particle. The z-tracking range is up to ± 50 μm (limited by the objective z-piezo stage) and the method enables simultaneous fluorescence lifetime measurements on the tracked particles. A major advantage of this method over previous tracking approaches is that it requires only one detector for SPT and is compatible with multi-color two-photon microscopy. We describe our approach and demonstrate its capabilities by tracking single fluorescent beads in aqueous solutions that include scattering, as well as tracking prescribed motions in these controlled environments. We then demonstrate tracking of EGFR (epidermal growth factor receptor) complexes tagged with fluorescent beads in tumor spheroids, demonstrating deep 3D SPT in multicellular models. We have coined this technique TSUNAMI (Tracking Single particles Using Nonlinear And Multiplexed Illumination; US patent 10281399). We are currently using TSUNAMI to study membrane receptor motion and drug delivery. More details can be found in Perillo et al., Nature Communications, 2015; Li et al., Cancer Cell, 2018; Liu, ACS Nano, 2020.