Abstract
A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.
Highlights
Detection of an unlabelled object encapsulated in a droplet requires an appropriate image-processing algorithm to successfully recognise target cells inside the droplet with single cell resolution
The latter application integrated the following steps in a single microfluidic chip: (i) encapsulation of live planktons in a droplet; (ii) searching for cells in the droplet and identifying its boundary in each frame captured by a high speed camera; (iii) identifying the cells based on the morphological characteristics of targets; (iv) sorting the droplet of interest for collection using direct current pulses applied to the unique electrode design, (v) storing the target droplets in a collection reservoir in the chip for incubation experiments
A series of seamless direct microfluidic procedures from cell encapsulation with droplet formation, cell detection, to droplet sorting was fabricated in a chip
Summary
Detection of an unlabelled object encapsulated in a droplet requires an appropriate image-processing algorithm to successfully recognise target cells inside the droplet with single cell resolution. The hardware consists of a droplet image recognition setup, microfluidic controller, and consumable microfluidic chip with liquid electrodes for effective droplet sorting to reduce both fabrication time and cost. The latter application integrated the following steps in a single microfluidic chip: (i) encapsulation of live planktons in a droplet; (ii) searching for cells in the droplet and identifying its boundary in each frame captured by a high speed camera; (iii) identifying the cells based on the morphological characteristics of targets; (iv) sorting the droplet of interest for collection using direct current pulses applied to the unique electrode design, (v) storing the target droplets in a collection reservoir in the chip for incubation experiments. These 24-h incubations provide experimental conditions to test the effect of nanoliter incubation chambers on the growth rate of planktons
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