A machine learning approach for single cell

The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework.


Fig: Overall pipeline to train and test the SVM for interphase cell cycle staging. The processing pipeline involves the analysis of DAPI and Fucci2 images, which are required to train and test the classifier (blue box, a–g). The final single cell cycle staging procedure uses the classifier parameters, obtained in the training phase, to identify the cell cycle phase of new DAPI images (yellow box); SVM support vector machine, Fucci fluorescent ubiquitination-based cell cycle indicator.

Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

Narotamo, H., Fernandes, M.S., Moreira, A.M. et al. A machine learning approach for single cell interphase cell cycle staging. Sci Rep 11,19278 (2021). https://doi.org/10.1038/s41598-021-98489-5

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