RRC ID 68156
著者 Narotamo H, Fernandes MS, Moreira AM, Melo S, Seruca R, Silveira M, Sanches JM.
タイトル A machine learning approach for single cell interphase cell cycle staging.
ジャーナル Sci Rep
Abstract 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. 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.
巻・号 11(1)
ページ 19278
公開日 2021-9-29
DOI 10.1038/s41598-021-98489-5
PII 10.1038/s41598-021-98489-5
PMID 34588507
PMC PMC8481278
MeSH Animals Cell Line Cell Nucleus / physiology* Datasets as Topic Humans Image Processing, Computer-Assisted* Interphase / physiology* Intravital Microscopy / methods Mice Microscopy, Fluorescence / methods Single-Cell Analysis / methods* Support Vector Machine*
IF 3.998
リソース情報
ヒト・動物細胞 NMuMG/Fucci2(RCB2868)