Lan Yiqing, Huang Nannan, Fu Yiru, Liu Kehao, Zhang He, Li Yuzhou, Yang Sheng
Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China.
Front Bioeng Biotechnol. 2022 Jan 27;9:802794. doi: 10.3389/fbioe.2021.802794. eCollection 2021.
Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.
在干细胞治疗、组织工程和再生医学中，迫切需要对干细胞的成骨分化进行早期、高通量且准确的识别。在本研究中，我们建立了一种自动深度学习算法，即成骨卷积神经网络（OCNN），以定量测量大鼠骨髓间充质干细胞（rBMSCs）的成骨分化。在早期分化阶段（第0、1、4和7天）用F-肌动蛋白和DAPI染色的rBMSCs，通过激光共聚焦扫描显微镜捕获以训练OCNN。结果，OCNN在非常早期阶段（24小时）就能成功区分分化细胞，曲线下面积（AUC）较高（0.94±0.04），且与传统生化标志物相关。同时，与单一形态学参数和支持向量机相比，OCNN表现出更好的预测性能。此外，OCNN成功预测了小分子成骨药物和一种细胞因子的剂量依赖性效应。基于OCNN的在线学习模型可以进一步识别在几种材料表面培养的rBMSCs的成骨分化。因此，本研究初步证明了OCNN在下一代组织工程和干细胞研究的成骨药物和生物材料筛选中的前景。
