An on-chip imaging droplet-sorting system: A real-time shape recognition method to screen target cells in droplets with single cell resolution

Mathias Girault, Hyonchol Kim, Hisayuki Arakawa, Kenji Matsuura, Masao Odaka, Akihiro Hattori, Hideyuki Terazono, Kenji Yasuda

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Article number40072
JournalScientific reports
Volume7
DOIs
Publication statusPublished - 2017 Jan 6

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'An on-chip imaging droplet-sorting system: A real-time shape recognition method to screen target cells in droplets with single cell resolution'. Together they form a unique fingerprint.

Cite this