Abstract
Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional (3D) biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today's best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, which are unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 d in HT-1080 fibrosarcoma xenografts in living mice using a data set of 38,000 cells and compared the induced phenotypes. In contrast to results with 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo.