Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches

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Abstract
The immune system protects its host from infection. Dysfunction of the immune system can cause autoimmune diseases and inflammatory diseases. Monitoring the immune system provides crucial information in informing treatment strategies and assessing the effect of therapies. While measures such as complete blood count to determine the leukocyte subsets are extensively used clinically, our ability to assess leukocyte function is limited, especially for the cells of the innate immune system, such as neutrophils. Here we introduce the idea of assessing neutrophil function from simple-to-obtain phase microscopy images. We developed an experimental pipeline using measurement of reactive oxygen species generation as a label of neutrophil function. We generated a large neutrophil imaging dataset and explored different deep learning approaches to predict neutrophil activation state. Our work demonstrates the potential of using deep learning models to evaluate functional aspects of the immune system, which could provide significant insight into immune disease prognostic monitoring that can be easily adapted to clinical settings.

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