Research

Droplet microfluidics is an established tool as a micro vehicle for cells, and provides a lot of merits for single-cell analysis. For example, single-cell level secretion detection can be made possible by encapsulating cells in droplets. In order to improve the usefulness of the droplets, we developed multiple high-throughput droplet manipulation methods so far. In addition, we are trying to develop unique micro vehicles for cells.

The first invented method is a sequentially addressable dielectrophoresis array (SADA), which enables large droplet sorting at a higher-throughput manner than previously reported by a factor of >20. Below is an abstract of our paper published in Science Advances in 2020.

Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of Saccharomyces cerevisiae based on their growth rate. ( Isozaki et al., Science Advances 6, eaba6712, 2020 )

A combination of lab-on-a-chip technology and AI technology can create a good synergy effect because the former can provide high-quality massive data sets and the latter can develop high-performance algorithms to analyze objects. Furthermore, the performance of lab-on-a-chip devices can be improved by implementing AI algorithms. When Isozaki belonged to the University of Tokyo, he proposed a new term for expressing the field of this combination in 2020, which is "AI on a Chip" (Isozaki et al., Lab Chip 20, 3074, 2020). In the field, we proposed a few methods and demonstrated biological applications. Especially, Isozaki et al. first developed intelligent image-activated cell sorting (iIACS) in 2018, updated it for higher throughput and image quality reported in 2020, developed surrounding technologies for further improvements, and demonstrated its applications. The left animation movie shows the functionality of the iIACS machine. Currently, we are keeping updating the method and demonstrating biological applications by collaborating with biologists. Below is an abstract of the first paper on intelligent image-activated cell sorting published in Cell in 2018. 

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences. ( Nitta et al., Cell 175, 266, 2018 )