Single-molecule detection provides researchers with a unique method to probe kinetics of biomolecules in their native environment, without the need to synchronize the molecular states. However, current single-molecule measurements of DNA hybridization kinetics are mostly performed on a surface or inside an electrokinetic trap, which are not physiologically relevant conditions. Recently we demonstrate a time-resolved, 3D single-molecule tracking (3D-SMT) method that that can follow individual DNA molecules diffusing inside a mammalian cell and observe multiple annealing and melting events on the same molecules. By comparing the hybridization kinetics of the same DNA strand in vitro, we found the association constants can be 13- to 163-fold higher in the molecular crowding cellular environment.

In contrast to other confocal-feedback 3D single-particle tracking demonstrations, we tracked single DNA reporter strands inside a live cell and measured their annealing-melting kinetics. Although camera-based techniques combined with point-spread function engineering can achieve 3D tracking in live cells, they do not offer any lifetime monitoring capability that can be used to reveal the molecular binding kinetics. While two-color colocalization and 2D tracking can provide a full dimerization kinetics analysis of G proteincoupled receptor in live cells, the 2D-TIRF imaging method is not suitable to probe the binding kinetics of a biomolecule deep in cytoplasm. On the contrary, our 3D-SMT method uses multiple single-photon detectors or multiplexed pulsed laser illuminations to achieve spatial filtering, which not only allows for high-resolution 3D localization of single molecules in live cells, but enables simultaneous characterization of molecular binding state through a continuous lifetime measurement. The data acquired can be used to generate new models that can predict in-cellulo hybridization kinetics from sequence, study the molecular crowding inside cells and probe the cellular development and transition states. More details can be found in Chen et al., JACS, 2019.

The ability to design and synthesize nanomaterials with specific photophysical properties is not only a great intellectual challenge, but also one with important practical consequences. To address this challenge, we are currently exploring a new class of biolabels termed few-atom noble metal nanoclusters. Noble metal nanoclusters are collections of small numbers of gold or silver atoms (2-30 atoms) with physical sizes close to the Fermi wavelength of an electron (~0.5 nm for gold and silver). Providing the missing link between atomic and nanoparticle behavior in noble metals, these nanoclusters exhibit optical, electronic, and chemical properties dramatically different from those of much larger nanoparticles or bulk materials. Among those water-soluble noble metal nanoclusters newly developed, DNA-templated silver nanoclusters (DNA/AgNCs) have attracted great interest in biosensing owing to a number of useful photophysical and photochemical properties. For instance, controlled conversion of DNA/AgNCs between bright and dark states by guanine proximity has led to the invention of a new molecular probe, termed a NanoCluster Beacon (NCB), that “lights up” upon binding with a DNA target. Not relying on Főrster energy transfer as the fluorescence on/off switching mechanism, NCBs have the potential to reach an ultrahigh signal-to-background (S/B) ratio in molecular sensing. Since the fluorescence enhancement is caused by intrinsic nucleobases, NCB detection is simple, inexpensive, and compatible with commercial DNA synthesizers. We hold 3 US patents on the silver nanocluster probes (10407715, 9499866, 8476014) and are currently collaborating with Dr. Jennifer Brodbelt in UT Chemisty and Dr. Minjun Kim at SMU in using mass spectrometry and nanopores to study DNA/AgNCs. More details can be foud in Yeh et al., Nano Letters, 2010; Yeh et al., JACS, 2012; Obliosca et al., ACS Nano, 2014; Chen et al., JACS, 2015; Blevins, ACS Nano, 2019.

We are currently using deep learning models to predictively design silver nanocluster sensors with desired colors, on/off ratios and photostabilities.

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.