Luminescent nanomaterials for droplet tracking in a microfluidic trapping array

Abstract

The 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.

FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data

“…The details about cell culture, reagents and equipment used in all the three applications [35, 36, 41] are described in S1 Method.…”

“…The microfluidic device used here is a two-layered droplet generator which was designed and fabricated as described by Vaithiyanathan et al [35] and Safa et al [36]. A brief description on this is presented in S2 Method.…”

“…The experimental steps describing sample preparation and execution of the three test systems (dose response analysis of OPM2 cells to Bortezomib using a droplet trapping array, tracking single cells using luminescent nanoparticles in a droplet trapping array [35], and understanding CPP uptake in intact cancer cells by measuring intracellular fluorescence) [36] are explained in S3 Method.…”

“…The detailed description of the theory and validation of the automated quantification is given in the following sections. The manual analysis of the first two test systems consisted of counting live and dead single/multiple cells and luminescent NPs within droplets to register for encapsulation data, cell viability data and droplet tracking data [35]. For the third test system, the mean intracellular fluorescence intensities of cells were manually quantified through gray values by drawing a polygon scan region of interest (ROI) across each cell using LAS X software 3.3.0.…”

“…The algorithm was designed to handle overlay images obtained using fluorescent microscopy which consisted of both brightfield and fluorescence overlays. To demonstrate the wide-range utility of FluoroCellTrack, it was utilized for three different applications: (1) to characterize and quantify a heterogeneous population of single OPM2 cancer cells based on their dynamic response to different concentrations of a drug; (2) to distinguish cells from clusters of spectrally independent luminescent nanoparticles (NPs) co-encapsulated in droplets which were successfully used as droplet trackers in prior work by Vaithiyanathan et al [35] and (3) to quantify heterogeneous uptake of fluorescent cell penetrating peptides (CPPs) in single cancer cells through intracellular fluorescence as described in previous work by Safa et al [36]. The algorithm was able to automatically (i) distinguish droplets from cells, (ii) distinguish and count live, dead and overlapping cells in each droplet, (iii) retrieve cell and NP co-encapsulation information to track single cells in each droplet, and (iv) quantify the intracellular fluorescence of intact cells.…”