Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids

Abstract

3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testing. However, a common limitation of immunostainings is the number of dyes that can be imaged simultaneously, as overlaps in the spectral profiles of the different dyes may result in cross talk. We therefore present a 3D deep learning method, able to predict fluorescent stainings of specific antigens on the basis of a nuclei staining. Using the proliferation marker Ki67, we showed that the presented model was able to predict the Ki67 staining with a strong correlation to the real signal. Additional analysis showed, that the model was not relying on signal cross talk. This approach, based on staining of the cell nuclei and subsequent prediction of the target antigen, could reduce the number of parallel antigen stains to a minimum and incompatible staining panels could be circumvented in the future.

Keywords

• label synthesis
• 3d
• fluorescence microscopy
• deep learning
• ki67 prediction