Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)
Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)
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DOI：
10.1073/pnas.1321405111
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发表时间：
2014-01-07
影响因子：
11.1
通讯作者：
Chakraborty, Arup K.
中科院分区：
文献类型：
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作者：
Shekhar, Karthik;Brodin, Petter;Chakraborty, Arup K.
质谱流式细胞术能够以高通量在单个细胞中测量前所未有数量的参数，但所得数据的大维度严重限制了依赖于手动“门控”的方法。“基于表型相似性的细胞聚类会损失单细胞分辨率，并且通常亚群的数量是先验未知的。在这里，我们描述了ACCENSE，一种结合了非线性降维与基于密度的分区的工具，并在2D图上显示多变量细胞表型。我们将ACCENSE应用于来自特定无病原体和无菌小鼠的CD8（+）T细胞的35参数质谱细胞术数据，并将细胞分层为表型亚群。我们的研究结果表明，在已知的CD8（+）T细胞亚群中存在显著的异质性，特别值得注意的是，我们在特定的无病原体和无菌小鼠中发现了一个大型的新亚群，这在以前没有描述过。当通过ACCENSE分析时，该亚群具有不同于常规幼稚和记忆亚群的表型特征，但在标准标记物的双轴图上不可区分。我们能够根据分析的所有蛋白质自动识别细胞亚群，从而帮助充分利用强大的新单细胞技术，如质谱细胞术。
Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8(+) T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8(+) T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.