Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow.

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56 and mature cytotoxic NK CD56. Therefore, the ability to discriminate CD56 from CD56 could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56 to CD56. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56 to CD56 and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.