High-content video flow cytometry with digital cell filtering for label-free cell classification by machine learning

Recent development of imaging flow cytometry (IFC) has enabled the measurements of single cells with high throughput, where fluorescent labels provide specificity for cellular diagnosis. The fluorescent labels may disturb the cell functions, and the requirements for high-throughput measurements limit the cell image quality. Here, we develop the high-content video flow cytometry (VFC) that measures unlabeled single cells with a rate of approximately 1000 cells per minute. For the obtained big data, the frame of interest (FOI) is automatically prepared by a digital cell filtering technique with machine learning. Cervical carcinoma cell lines (Caski, HeLa and C33-A cells) are differentiated with an accuracy of 91.5%, 90.5%, and 90.5% by deep learning in a three-way classification, respectively. The high-content VFC not only provides high-quality images of single cells with high throughput and rewinding, but also performs automatic digital cell filtering and label-free cell classification that may have clinical applications.

Automated summary

Recent development of imaging flow cytometry enables high-throughput measurements of single cells using fluorescent labels for cellular diagnosis. However, the use of fluorescent labels may disrupt cell functions and the requirement for high-throughput measurements limits cell image quality. In this study, a high-content video flow cytometry is developed to measure unlabeled single cells at a rapid rate, with automatically prepared frames of interest using a digital cell filtering technique and machine learning. Deep learning accurately distinguishes cervical carcinoma cell lines with high accuracy.