Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Auguste-Piccard-Hof 1, Zurich 8093, Switzerland.
Swiss Institute of Bioinformatics, Zurich 8093, Switzerland.
Nat Commun. 2017 Apr 6;8:14825. doi: 10.1038/ncomms14825.
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%.
稀有细胞群体在癌症等疾病的发生和发展中起着关键作用。然而，此类亚群的鉴定仍然是一项艰巨的任务。本工作描述了 CellCnn，这是一种使用高维单细胞测量来检测与疾病相关的稀有细胞亚群的表示学习方法。使用 CellCnn，我们在外周血中鉴定了旁分泌信号、艾滋病发病和罕见 CMV 感染相关的细胞亚群，以及在类似微小残留病的情况下频率低至 0.01%的极罕见的白血病原始细胞群体。
