Sadao Ota

Decoupling Fabrication from Encoding: DNA-Addressable Template Microparticles for Large, User-Defined Optical Barcode Libraries

This paper introduces a method to create large optical barcode libraries using DNA-addressable microparticles, achieving high accuracy and scalability.

Randomized Spatial Barcoding for Time-Lapse Flow Cytometry

The paper introduces RASPBerry, a hydrogel-based spatial barcoding platform for time-lapse flow cytometry, enabling tracking of suspended cells with high accuracy and throughput.

Organoid-in-Bead (OrB): vortex-based compartmentalization enables scalable, high-density intestinal organoid culture

The paper introduces OrB, a vortex-based method for high-density intestinal organoid culture that reduces reagent use and increases scalability.

Microfluidic Core-Shell Encapsulation Enables Scalable Generation of Apical-Out Intestinal Spheroids

This paper introduces a microfluidic method to generate scalable apical-out intestinal spheroids by encapsulating cells in core-shell hydrogel microcapsules.

High-throughput CRISPR live-cell imaging of low-frequency chromosomal events quantifies the latent efficiency of chromosome engineering

Hi-CRI quantifies low-frequency chromosomal events in living cells with a 0.01% detection limit, revealing pre-readout attrition in chromosomal transfer experiments.

Large-scale generation of uniform sub-100 μm adipocyte spheroids in hydrogel microcapsules using a flow-focusing microfluidic device

This paper develops a microfluidic method to generate uniform adipocyte spheroids for obesity research, enabling large-scale production and analysis.

Space-efficient method for high-throughput generation of uniform cell-laden hollow agarose microcapsules

The paper presents a space-efficient method for generating uniform hollow agarose microcapsules using a sealed bottle and vortex mixer, enabling high-throughput encapsulation in confined environments.

Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity

This paper develops a system to predict hematopoietic stem cell diversity using quantitative phase imaging and machine learning, capturing temporal kinetics rather than single snapshots.