Biology subjects

Kurabayashi, K.

Publications and source records attributed to Kurabayashi, K..

3 recordsLinked to original sources

bioRxiv2020

Machine Learning-Based Protein Microarray Digital Assay Analysis

Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, we developed a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay achieved 14-plexed biomarker detection at concentrations < 10pg/mL with a sample volume < 10 L, including all processes from sampling to analyzed data delivery within 30 min, while only requiring a 5-min assay incubation. The assay procedure applied both a spatial-spectral microfluidic encoding scheme and an image data analysis algorithm based on machine learning with a convolutional neural network (CNN) for pre-equilibrated single-molecule protein digital counting. This unique approach remarkably reduced errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 14 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy manifested the powerful biomarker profiling capability and great potential of the assay for its translation to near-real-time bedside immune status monitoring.

bioengineering↗

bioRxiv2020

Optimized gene expression from bacterial chromosome by high-throughput integration and screening

Chromosomal integration of recombinant genes is desirable compared to expression from plasmids due to increased stability, reduced cell-to-cell variability, and the elimination of antibiotics for plasmid maintenance. Here, we present a new approach for tuning pathway gene expression levels via random integrations and high-throughput screening. We demonstrate multiplexed gene integration and expression-level optimization for isobutanol production in Escherichia coli. The integrated strains could, with significantly lower expression levels than plasmid-based expression, produce high titers (10.0 {+/-} 0.9 g/L isobutanol in 48 h) and yields (69 % of the theoretical maximum). Close examination of pathway expression in the top-performing, as well as other isolates, reveals the complexity of cellular metabolism and regulation, underscoring the need for precise optimization while integrating pathway genes into the chromosome. This new method for multiplexed pathway gene integration and expression optimization could be readily extended to a wide range of pathways and chassis to create robust and efficient production strains.

synthetic biology↗

bioRxiv2019

Syntrophic co-culture amplification of production phenotype for high-throughput screening of microbial strain libraries

Microbes can be engineered to synthesize a wide array of bioproducts, yet production phenotype evaluation remains a frequent bottleneck in the design-build-test cycle where strain development requires iterative rounds of library construction and testing. Here, we present Syntrophic Co-culture Amplification of Production phenotype (SnoCAP). Through a metabolic cross-feeding circuit, the production level of a target molecule is translated into highly distinguishable co-culture growth characteristics, which amplifies differences in production into highly distinguishable growth phenotypes. We demonstrate SnoCAP with the screening of Escherichia coli strains for production of two target molecules: 2-ketoisovalerate, a precursor of the drop-in biofuel isobutanol, and L-tryptophan. The dynamic range of the screening can be tuned by employing an inhibitory analog of the target molecule. Screening based on this framework requires compartmentalization of individual producers with the sensor strain. We explore three formats of implementation with increasing throughput capability: confinement in microtiter plates (102-104 assays/experiment), spatial separation on agar plates (104-105 assays/experiment), and encapsulation in microdroplets (105-107 assays/experiment). Using SnoCAP, we identified an efficient isobutanol production strain from a random mutagenesis library, reaching a final titer that is 5-fold higher than that of the parent strain. The framework can also be extended to screening for secondary metabolite production using a push-pull strategy. We expect that SnoCAP can be readily adapted to the screening of various microbial species, to improve production of a wide range of target molecules.

synthetic biology↗