基于卷积神经网络迁移学习的诱导神经细胞分化阶段分析

doi:10.3969/j.issn.0258-8021.2025.02.001

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Abstract With the high prevalence of neurological diseases, neural stem cell therapy has emerged as a significant research focus. However, current technologies still face challenges in accurately evaluating the neural stem cell differentiation process. This work aimed to develop a method using convolutional neural networks (CNNs) with transfer learning to efficiently classify images of label-free neuronal cells at different differentiation stages. Additionally, single-cell transcriptome data was leveraged to enhance the accuracy of cell differentiation stage detection and to validate the method′s effectiveness. The dataset including1 026 bright-field images of PC12 cells across different differentiation stages, supplemented by 100 images of HEK-293A or HT1080 cells obtained from publicly available online databases. Four CNNs including ResNet34, were evaluated. The models were conducted pre-training on the ImageNet dataset, followed by fine-tuning using a cell image dataset with annotation. The transcriptomic analysis was conducted on each cell after classifying the differentiation stages of thebright-field neural cell images. The results showed that the ResNet34-TL model performed best in classifying neural cells with an accuracy of 95.8%. The transcriptomic analysis of cells classified as undifferentiated, low-differentiated, and highly-differentiated by ResNet34-TL model indicates significant differences, particularly between the undifferentiated and highly-differentiated groups. Cells in the low-differentiated group exhibited characteristics of both extremes, suggesting a transitional state. The differential expression level of characteristic genes across the three groups revealed that the transcriptome-based classification was consistent with the model's classification results. ResNet34-TL exhibited strong generalization ability in the analysis of neuronal cell differentiation stages, and its capacity to effectively distinguish neural cells at different differentiation stages was further validated through transcriptomic analysis.

Key words： identification of neuronal differentiation label-free cell images single-cell transcriptome deep learning transfer learning

Received: 05 September 2024

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R318

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	Articles by authors
	Huang Xingye
	Wei Guochao
	Guo Yunxia
	Shen Jiahao
	Bi Kun
	Zhang Zequn
	Huang Yan
	Zhao Xiangwei

Cite this article:

Huang Xingye,Wei Guochao,Guo Yunxia, et al. Analysis of Induced Neural Cells Differentiation Stages Based on Transfer Learning with CNN[J]. Chinese Journal of Biomedical Engineering, 2025, 44(2): 129-141.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.02.001 OR http://cjbme.csbme.org/EN/Y2025/V44/I2/129

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