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<p>Sister chromatid cohesion (SCC) is mediated by a protein complex called cohesin and by regulatory proteins that control cohesin function. A commonly used approach to evaluate the involvement of cohesin regulatory proteins is to classify the shape of the chromosomes after depletion of the target protein and analyze their distribution. Currently, shape classification is often performed manually by researchers, which is not only time-consuming but also subject to individual interpretation. Therefore, our research group developed image classification models for automating chromosome shape classification. However, in this method, unclassifiable chromosomes that arise when cropping single chromosomes must be removed manually, creating a significant barrier to the fully automated detection of SCC-defective chromosomes. In this study, we propose a method that utilizes an object detection model to detect chromosomes with SCC defects without the need to crop single chromosomes. Several pretrained object detection models were selected and fine-tuned, and their performances were compared. Among the models, the one based on You Only Look Once v8 (YOLOv8) achieved a maximum concordance rate of 89.40% with manual analysis and successfully identified differences in the distribution of wild-type (WT) and <i>DDX11</i><sup><i>−/−</i></sup>cells. These results indicate that the YOLOv8-based model enables fully automated analysis of SCC-defective chromosomes.</p>

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Detection and classification of chromosomes with sister chromatid cohesion defects using object detection models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • Shinya Matsumoto,
  • Miku Sojo,
  • Kosuke Oshima,
  • Kiyoshi Nishikawa,
  • Kan Okubo,
  • Takuya Abe

摘要

Sister chromatid cohesion (SCC) is mediated by a protein complex called cohesin and by regulatory proteins that control cohesin function. A commonly used approach to evaluate the involvement of cohesin regulatory proteins is to classify the shape of the chromosomes after depletion of the target protein and analyze their distribution. Currently, shape classification is often performed manually by researchers, which is not only time-consuming but also subject to individual interpretation. Therefore, our research group developed image classification models for automating chromosome shape classification. However, in this method, unclassifiable chromosomes that arise when cropping single chromosomes must be removed manually, creating a significant barrier to the fully automated detection of SCC-defective chromosomes. In this study, we propose a method that utilizes an object detection model to detect chromosomes with SCC defects without the need to crop single chromosomes. Several pretrained object detection models were selected and fine-tuned, and their performances were compared. Among the models, the one based on You Only Look Once v8 (YOLOv8) achieved a maximum concordance rate of 89.40% with manual analysis and successfully identified differences in the distribution of wild-type (WT) and DDX11−/−cells. These results indicate that the YOLOv8-based model enables fully automated analysis of SCC-defective chromosomes.