Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal
Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal
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DOI：
10.1016/j.cels.2020.11.004
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发表时间：
2020-12-16
期刊：
影响因子：
9.3
通讯作者：
MacArthur, Ben D.
中科院分区：
文献类型：
--
作者：
Arai, Fumio;Stumpf, Patrick S.;MacArthur, Ben D.
干细胞活性的变化可能是衰老的基础。然而，这些变化并没有完全被理解。在这里，我们将单细胞图谱与机器学习和体内功能研究相结合，以探索造血干细胞(HSC)分裂模式如何随着年龄的变化而演变。我们首先训练了人工神经网络(ANN)，以准确识别造血系统中的细胞类型，并根据单细胞基因表达模式预测它们的年龄。然后，我们使用这个神经网络来比较HSC分裂后立即子代细胞的身份，发现单个HSC的自我更新能力随着年龄的增长而下降。此外，尽管HSC细胞分裂在年轻和老年时是确定性的和内在的调节，但在中年时它们是可变的和利基敏感的。这些结果表明，干细胞活动的内在和外在调节之间的平衡随着年龄的增长而显著改变，并有助于解释为什么干细胞数量在一生中增加，而再生能力下降。
Changes in stem cell activity may underpin aging. However, these changes are not completely understood. Here, we combined single-cell profiling with machine learning and in vivo functional studies to explore how hematopoietic stem cell (HSC) divisions patterns evolve with age. We first trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from single-cell gene-expression patterns. We then used this ANN to compare identities of daughter cells immediately after HSC divisions and found that the self-renewal ability of individual HSCs declines with age. Furthermore, while HSC cell divisions are deterministic and intrinsically regulated in young and old age, they are variable and niche sensitive in mid-life. These results indicate that the balance between intrinsic and extrinsic regulation of stem cell activity alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines.