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.

中文翻译：

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深度学习在用于细胞治疗和再生医学的单细胞 RNA 测序分析中的潜在应用

当用于细胞疗法和再生医学策略时，干细胞具有治疗许多以前无法治愈的疾病的潜力。然而，目前使用干细胞的应用方法并不发达，因为这些细胞是直接使用的，而不管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，研究人员没有考虑它们的来源和培养方法，也没有考虑它们的应用角度和功能（软组织再生、硬组织再生、抑制免疫功能或促进免疫功能） . 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影响，并有效地使用具有分化潜力的亚群进行干细胞治疗。scRNA-seq 技术改变了转录研究，因为它可以表达具有单细胞解剖分辨率的单细胞基因。然而，这种强大的技术对生物和技术噪音很敏感。由于各种原因，后续的数据分析在计算上可能很困难，例如对单细胞数据进行去噪、降低维度、估算缺失值以及考虑零膨胀性质。在这篇综述中，我们讨论了深度学习方法如何结合 scRNA-seq 数据进行研究，如何更深入地解释 scRNA-seq 数据，改进干细胞的后续分析，识别潜在的亚群，并促进细胞的实施。治疗和再生医学措施。