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In silico-labeled ghost cytometry.

ABSTRACT: Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

SUBMITTER: Ugawa M

PROVIDER: S-EPMC8691837 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Flow cytometry is a powerful method, which is widely used for high-throughput quantitative and qualitative analysis of cells. However, its straightforward applicability for extracellular vesicles (EVs) and mainly exosomes is hampered by several challenges, reflecting mostly the small size of these vesicles (exosomes: ~80-200 nm, microvesicles: ~200-1,000 nm), their polydispersity, and low refractive index. The current best and most widely used protocol for beads-free flow cytometry of exosomes uses ultracentrifugation (UC) coupled with floatation in sucrose gradient for their isolation, labeling with lipophilic dye PKH67 and antibodies, and an optimized version of commercial high-end cytometer for analysis. However, this approach requires an experienced flow cytometer operator capable of manual hardware adjustments and calibration of the cytometer. Here, we provide a novel and fast approach for quantification and characterization of both exosomes and microvesicles isolated from cell culture media as well as from more complex human samples (ascites of ovarian cancer patients) suitable for multiuser labs by using a flow cytometer especially designed for small particles, which can be used without adjustments prior to data acquisition. EVs can be fluorescently labeled with protein-(Carboxyfluoresceinsuccinimidyl ester, CFSE) and/or lipid- (FM) specific dyes, without the necessity of removing the unbound fluorescent dye by UC, which further facilitates and speeds up the characterization of microvesicles and exosomes using flow cytometry. In addition, double labeling with protein- and lipid-specific dyes enables separation of EVs from common contaminants of EV preparations, such as protein aggregates or micelles formed by unbound lipophilic styryl dyes, thus not leading to overestimation of EV numbers. Moreover, our protocol is compatible with antibody labeling using fluorescently conjugated primary antibodies. The presented methodology opens the possibility for routine quantification and characterization of EVs from various sources. Finally, it has the potential to bring a desired level of control into routine experiments and non-specialized labs, thanks to its simple bead-based standardization.

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In silico-labeled ghost cytometry.

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