High throughput hemogram of T cells using digital holographic microscopy and deep learning

T cells of the adaptive immune system provide effective protection to the human body against numerous pathogenic challenges. Current labelling methods of detecting these cells, such as flow cytometry or magnetic bead labelling, are time consuming and expensive. To overcome these limitations, the label-free method of digital holographic microscopy (DHM) combined with deep learning has recently been introduced which is both time and cost effective. In this study, we demonstrate the application of digital holographic microscopy with deep learning to classify the key CD4+ and CD8+ T cell subsets. We show that combining DHM of varying fields of view, with deep learning, can potentially achieve a classification throughput rate of 78,000 cells per second with an accuracy of 76.2% for these morphologically similar cells. This throughput rate is 100 times faster than the previous studies and proves to be an effective replacement for labelling methods.

Automated summary

T cells of the adaptive immune system are effectively protected against pathogenic challenges. Traditional labelling methods are time-consuming and expensive, but digital holographic microscopy with deep learning proves to be a faster and more cost-effective alternative. The combination of DHM and deep learning achieves high throughput and accuracy in classifying CD4+ and CD8+ T cell subsets.