Luminescent nanomaterials for droplet tracking in a microfluidic trapping array

AbstractThe use of high-throughput multiplexed screening platforms has attracted significant interest in the field of on-site disease detection and diagnostics for their capability to simultaneously interrogate single-cell responses across different populations. However, many of the current approaches are limited by the spectral overlap between tracking materials (e.g., organic dyes) and commonly used fluorophores/biochemical stains, thus restraining their applications in multiplexed studies. This work demonstrates that the downconversion emission spectra offered by rare earth (RE)-doped β-hexagonal NaYF4 nanoparticles (NPs) can be exploited to address this spectral overlap issue. Compared to organic dyes and other tracking materials where the excitation and emission is separated by tens of nanometers, RE elements have a large gap between excitation and emission which results in their spectral independence from the organic dyes. As a proof of concept, two differently doped NaYF4 NPs (europium: Eu3+, and terbium: Tb3+) were employed on a fluorescent microscopy-based droplet microfluidic trapping array to test their feasibility as spectrally independent droplet trackers. The luminescence tracking properties of Eu3+-doped (red emission) and Tb3+-doped (green emission) NPs were successfully characterized by co-encapsulating with genetically modified cancer cell lines expressing green or red fluorescent proteins (GFP and RFP) in addition to a mixed population of live and dead cells stained with ethidium homodimer. Detailed quantification of the luminescent and fluorescent signals was performed to confirm no overlap between each of the NPs and between NPs and cells. Thus, the spectral independence of Eu3+-doped and Tb3+-doped NPs with each other and with common fluorophores highlights the potential application of this novel technique in multiplexed systems, where many such luminescent NPs (other doped and co-doped NPs) can be used to simultaneously track different input conditions on the same platform. Graphical abstractᅟ