Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics
This work addresses a challenge in biomedical imaging for researchers by providing an image-free method to differentiate cells based on morphology, though it appears incremental as it builds on existing ghost cytometry approaches.
The study tackled the problem of classifying cell populations with similar gross morphology but different spatial fluorescence distributions using ghost cytometry, achieving classification without requiring strict controls for detailed morphologic analysis.
Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals, thereby relieving the computational bottleneck observed in high-throughput cytometry based on morphological information. While this image-free approach could distinguish different cell types using the same fluorescence staining method, further strict controls are sometimes required to clearly demonstrate that the classification is based on detailed morphologic analysis. In this study, we show that ghost cytometry can be used to classify cell populations of the same type but with different fluorescence distributions in space, supporting the strength of our image-free approach for morphologic cell analysis.