Biology subjects

Ota, S.

Publications and source records attributed to Ota, S..

7 recordsLinked to original sources

bioRxiv2024

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

Adipocyte spheroids are a promising three-dimensional (3D) cell culture model for obesity research because they reproduce 3D adipose tissue structures and cell-cell interactions better than 2D cultures. However, current methods fail to produce uniformly sized, small adipocyte spheroids at large scales, significantly limiting their use in analysis such as large-scale drug screening. Here, we develop a scalable method that combines simple microfluidics with templated emulsification to generate small, uniformly sized adipocyte spheroids. By encapsulating preadipocytes in numerous hollow agarose microcapsules and incubating them for two days, we reproducibly produced more than 100,000 uniform spheroids with diameters of approximately 50 {micro}m (CV: <13%); we then differentiated preadipocyte spheroids into adipocyte spheroids after an 8-day induction period. Our platform enhances large-scale 3D analysis using adipocyte spheroids for obesity research and can be adapted to generate various spheroid and organoid models, advancing biomedical research across diverse fields.

bioengineering↗

bioRxiv2023

Barcoding of small extracellular vesicles with CRISPR-gRNA enables comprehensive, subpopulation-specific analysis of their biogenesis/release regulators

Small extracellular vesicles (sEVs) are important intercellular information transmitters in various biological contexts, but their release processes remain poorly understood. Herein, we describe a high-throughput assay platform, CRISPR-assisted individually barcoded sEV-based release regulator (CIBER) screening, for identifying key players in sEV release. CIBER screening employs sEVs barcoded with CRISPR-gRNA through the interaction of gRNA and dead Cas9 fused with an sEV marker. Barcode quantification enables the estimation of the sEV amount released from each cell in a massively parallel manner. Barcoding sEVs with different sEV markers in a CRISPR pooled-screening format allows genome-wide exploration of sEV release regulators in a subpopulation-specific manner, successfully identifying previously unknown sEV release regulators and uncovering the exosomal/ectosomal nature of CD63+/CD9+ sEVs, respectively, as well as the synchronization of CD9+ sEV release with the cell cycle. CIBER should be a valuable tool for detailed studies on the biogenesis, release, and heterogeneity of sEVs.

cell biology↗

bioRxiv2023

Artificial Intelligence Enables the Label-Free Identification of Chronic Myeloid Leukemia Cells with Mitochondrial Morphological Alterations

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI- mediated identification. Our findings indicate that BCR-ABL1-transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally, BCR-ABL1 enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI- based technology enables the highly sensitive detection of BCR-ABL1-positive cells and early diagnosis of CML.

cancer biology↗

bioRxiv2023

High-throughput 3D imaging flow cytometry of adherent 3D cell cultures

Three-dimensional (3D) cell cultures are indispensable in recapitulating in vivo environments. Among many 3D culture methods, the strategy to culture adherent cells on hydrogel beads to form spheroid-like structures is powerful for maintaining high cell viability and functions through an efficient supply of nutrients and oxygen. However, high-throughput, scalable technologies for 3D imaging of individual cells cultured on the hydrogel scaffolds are lacking. This study reports the development of a high-throughput, scalable 3D imaging flow cytometry (3D-iFCM) platform for analyzing spheroid models on hydrogel beads. This platform is realized by integrating a single objective lens-based fluorescence light-sheet microscopy with a microfluidic device employing a combination of hydrodynamic and acoustofluidic focusing techniques. This integration enabled an unprecedentedly high-throughput, robust optofluidic 3D imaging, processing 513 cells s-1 and a total of more than 104 cells within a minute. The large dataset obtained allows us to quantify and compare the nuclear morphology of adhering and suspended cells, revealing adhering cells have smaller nuclei with non-round surfaces. This platforms high throughput, robustness, and precision for analyzing the morphology of subcellular compartments in 3D culture models holds promising potential for various biomedical analyses, including image-based phenotypic screening of drugs with spheroids or organoids.

bioengineering↗

bioRxiv2023

Label-free ghost cytometry for manufacturing of cell therapy products

Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs, T cells and non-T cells in white blood cells (WBCs), activated T cells and quiescent T cells in PBMCs, and particulate impurities in PBMCs. The data support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.

cell biology↗

bioRxiv2023

Pooled CRISPR screening of high-content cellular phenotypes by ghost cytometry

Fast enrichment of cells based on morphological information remains a challenge, limiting genome-wide perturbation screening for diverse high-content phenotypes of cells. Here we show that multi-modal ghost cytometry-based cell sorting is applicable to fast pooled CRISPR screening for both fluorescence and label-free high-content phenotypes of millions of cells. By employing the high-content cell sorter in the fluorescence mode, we enabled the genome-wide CRISPR screening of genes that affect NF-{kappa}B nuclear translocation. Furthermore, by employing the multi-parametric, label-free mode, we performed the large-scale screening to identify a gene involved in macrophage polarization. Especially the label-free platform can enrich target phenotypes without invasive staining, preserving untouched cells for downstream assays and unlocking the potential to screen for the cellular phenotypes even when suitable markers are lacking. One-Sentence SummaryMachine vision-based cell sorter enabled genome-wide perturbation screens for high-content cell phenotypes even without labeling

cell biology↗

bioRxiv2022

Droplet array-based platform for parallel optical analysis of dynamic extracellular vesicle secretion from single cells

Extracellular vesicles (EVs) are essential intercellular communication tools, but the regulatory mechanisms governing heterogeneous EV secretion are still unclear due to the lack of methods for precise analysis. Monitoring the dynamics of secretion from individually isolated cells is crucial because, in bulk analysis, secretion activity can be perturbed by cell-cell interactions, and a cell population rarely performs secretion in a magnitude- or duration-synchronized manner. Although various microfluidic techniques have been adopted to evaluate the abundance of single-cell-derived EVs, none can track their secretion dynamics continually for extended periods. Here, we have developed a droplet array-based method that allowed us to optically quantify the EV secretion dynamics of >300 single cells every 2 hours for 36 hours, which covers the cell doubling time of many cell types. The experimental results clearly show the highly heterogeneous nature of single-cell EV secretion and suggest that cell division facilitates EV secretion, showing the usefulness of this platform for discovering EV regulation machinery.

bioengineering↗