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Multiplex fluorescence detection of single-cell droplet microfluidics and its application in quantifying protein expression levels.
Biomicrofluidics ; 17(6): 064106, 2023 Dec.
Article em En | MEDLINE | ID: mdl-38162228
ABSTRACT
This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and ß-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (ß-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article