Dr. Li Dak Sum - Yip Yio Chin Center for Stem Cells and Regenerative Medicine and Department of Orthopedic Surgery of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Key Laboratory of Tissue Engineering and Regenerative Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Stem Cells. 2021 May;39(5):511-521. doi: 10.1002/stem.3336. Epub 2021 Feb 15.
When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardless of their culture medium and subgroup. For example, when using mesenchymal stem cells (MSCs) in cell therapy, researchers do not consider their source and culture method nor their application angle and function (soft tissue regeneration, hard tissue regeneration, suppression of immune function, or promotion of immune function). By combining machine learning methods (such as deep learning) with data sets obtained through single-cell RNA sequencing (scRNA-seq) technology, we can discover the hidden structure of these cells, predict their effects more accurately, and effectively use subpopulations with differentiation potential for stem cell therapy. scRNA-seq technology has changed the study of transcription, because it can express single-cell genes with single-cell anatomical resolution. However, this powerful technology is sensitive to biological and technical noise. The subsequent data analysis can be computationally difficult for a variety of reasons, such as denoising single cell data, reducing dimensionality, imputing missing values, and accounting for the zero-inflated nature. In this review, we discussed how deep learning methods combined with scRNA-seq data for research, how to interpret scRNA-seq data in more depth, improve the follow-up analysis of stem cells, identify potential subgroups, and promote the implementation of cell therapy and regenerative medicine measures.
当用于细胞治疗和再生医学策略时，干细胞有可能治疗许多以前无法治愈的疾病。然而，目前使用干细胞的应用方法还不够发达，因为这些细胞直接使用，而不考虑它们的培养基和亚群。例如，在细胞治疗中使用间充质干细胞（MSCs）时，研究人员不考虑其来源和培养方法，也不考虑其应用角度和功能（软组织再生、硬组织再生、免疫功能抑制或促进免疫功能）。通过将机器学习方法（如深度学习）与通过单细胞 RNA 测序（scRNA-seq）技术获得的数据集相结合，我们可以发现这些细胞的隐藏结构，更准确地预测它们的作用，并有效地利用具有分化潜力的亚群进行干细胞治疗。scRNA-seq 技术改变了转录的研究，因为它可以用单细胞解剖分辨率表达单细胞基因。然而，这种强大的技术对生物和技术噪声很敏感。由于各种原因，如去噪单细胞数据、降低维度、插补缺失值以及考虑零膨胀性质，后续数据分析可能在计算上很困难。在这篇综述中，我们讨论了深度学习方法如何与 scRNA-seq 数据结合进行研究，如何更深入地解释 scRNA-seq 数据，改进对干细胞的后续分析，识别潜在的亚群，并促进细胞治疗和再生医学措施的实施。
