A U-Net–Based Automated Fluorescence and Morphological Analysis of CH25H Knockdown and Overexpressing Cell Lines

Elucidating the influence of $\mathbf{C H 2 5 H}$ on cellular metabolism, immune regulation, and viral infection at the molecular level holds significant promise for understanding the pathogenesis of related diseases. Conventional fluorescence microscopy-based quantitative methods are time-consuming and prone to subjective bias in large-scale experiments. To address these limitations, this study established stable cell lines with CH25H knockdown and overexpression, employing a U-Net model for automated segmentation and quantitative analysis of fluorescence images. This approach encompasses cell-line construction, microscopic imaging, high-throughput image segmentation, and statistical evaluation. The results demonstrate that U-Net achieves high accuracy in delineating cellular contours and quantifying fluorescence signals, effectively identifying morphological and expression-level variations across different clones. By reducing experimental duration and operator-induced bias, this automated analysis method proves valuable for subsequent $\mathbf{C H 2 5 H}$ function investigations and the design of largescale cellular assays.