Optical signature dataset for living macrophages and monocytes

Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes
play a fundamental role in the immune response. The interplay and
consequently the classification between these two functional subtypes is
significant for many therapeutic applications. Albeit, a fast
classification of macrophage phenotypes is challenging. For instance,
image-based classification systems need cell staining and coloration,
which is usually time and cost-consuming, such as multiple cell surface
markers, transcription factors and cytokine profiles are needed. A simple
alternative would be to identify such cell types by using a single-cell,
label-free and high-throughput light scattering pattern analyses combined
with a straightforward machine-learning-based classification. Here, we
compared different machine learning algorithms to classify distinct
macrophage phenotypes based on their optical signature obtained from an
ad-hoc developed wide angle static light scattering apparatus. As the main
result, we were able to identify unpolarized macrophages from M1- and
M2-polarized phenotypes and distinguished them from naive monocytes with
an average accuracy above 85%. Therefore, we suggest that optical
single-cell signatures within a lab-on-a-chip approach along with machine
learning could be used as a fast, affordable, non-invasive macrophage
phenotyping tool to supersede resource-intensive cell labelling.