ABSTRACT
Droplet microfluidics has emerged as a powerful tool for a diverse range of biomedical and industrial applications such as single-cell analysis, directed evolution, and metabolic engineering. In these applications, droplet sorting has been effective for isolating small droplets encapsulating molecules, cells, or crystals of interest. Recently, there is an increased interest in extending the applicability of droplet sorting to larger droplets to utilize their size advantage. However, sorting throughputs of large droplets have been limited, hampering their wide adoption. Here, we report our demonstration of high-throughput fluorescence-activated droplet sorting of 1 nL droplets using an upgraded version of the sequentially addressable dielectrophoretic array (SADA), which we reported previously. The SADA is an array of electrodes that are individually and sequentially activated/deactivated according to the speed and position of a droplet passing nearby the array. We upgraded the SADA by increasing the number of driving electrodes constituting the SADA and incorporating a slanted microchannel. By using a ten-electrode SADA with the slanted microchannel, we achieved fluorescence-activated droplet sorting of 1 nL droplets at a record high throughput of 1752 droplets/s, twice as high as the previously reported maximum sorting throughput of 1 nL droplets.
Subject(s)
Microfluidics , ElectrodesABSTRACT
In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = -0.82, P = 2.1 × 10-5) between a morphological meta-feature recognized via our machine learning algorithms and the Cu2+ removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.
