A well-defined surface architecture is essential to generate water-dispersible UCNPs that are long-term stable and enable a wealth of bioanalytical applications.

Photon-upconverting nanoparticles (UCNPs) are lanthanide-doped nanocrystals that emit visible light under near-infrared excitation (anti-Stokes emission). This unique optical property precludes background fluorescence and light scattering from biological materials. The emission of multiple and narrow emission lines is an additional hallmark of UCNPs that opens up new avenues for optical encoding. Distinct emission signatures can be obtained if the multiple emission of UCNPs is tuned by their dopant composition or by surface modification with dyes. Tuning the intensity of only one of the multiple emission lines and using another one as a constant reference signal enables the design of ratiometric codes that are resistant to fluctuations in absolute signal intensities. Combining several UCNPs each displaying a distinct set of emission lines expands the coding capacity exponentially and lays the foundation for highly multiplexed analyte detection. This Review highlights the potential of UCNPs for labeling and encoding biomolecules, microspheres, and even whole cells.

Individual enzyme molecules have been observed to possess discrete and different turnover rates due to the presence of long-lived activity states. These stable activity states are thought to result from different molecular conformations or post-translational modifications. The distributions in kinetic activity observed in previous studies were obtained from small numbers of single enzyme molecules. Due to this limitation, it has not been possible to fully characterize the different kinetic and equilibrium binding parameters of single enzyme molecules. In this paper, we analyze hundreds of single beta-galactosidase molecules simultaneously; using a high-density array of 50,000 fL-reaction chambers, we confirm the presence of long-lived kinetic states within a population of enzyme molecules. Our analysis has isolated the source of kinetic variability to kcat. The results explain the kinetic variability within enzyme molecule populations and offer a deeper understanding of the unique properties of single enzyme molecules. Gaining a more fundamental understanding of how individual enzyme molecules work within a population should provide insight into how they affect downstream biochemical processes. If the results reported here can be generalized to other enzymes, then the stochastic nature of individual enzyme molecule kinetics should have a substantial impact on the overall metabolic activity within a cell.