Brain cell laser powered by deep-learning-enhanced laser modes

Abstract

Single cellular lasers have recently attracted tremendous research due to their outstanding lasing characteristics for cell sensing and tracking. Thanks to enhanced light−cell interactions in Fabry–Pérot microcavities, transverse laser modes from cellular lasers are highly correlated to the spatial biophysical properties of cells. However, the huge complexity and randomness of laser modes set a critical challenge towards practical applications in cell analysis. In this study, deep learning is applied to unravel the complex laser modes generated from single-cell lasers by establishing the correlation between laser modes and cellular physical properties. Primary cells extracted from rat brains and cell-like droplets are investigated and trained through a convolutional neuron network based on laser mode images. Detailed simulations and experiments are conducted to study the effect of cell size on laser modes. Predictions of cell diameters with a sub-micron accuracy are achieved with deep learning. Finally, the potential application of using deep-learning-enhanced laser modes for cell classification is demonstrated. Neuron and glial cells extracted from rat brains are classified through hyperspectral images of laser modes. The results demonstrate that deep learning has the potential to enable laser modes with biological significance and functions, offering new possibilities for biophotonic applications.