Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2014 Jan 7;111(1):202-7. doi: 10.1073/pnas.1321405111. Epub 2013 Dec 16.
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.
质谱流式细胞术能够在单个细胞中以高通量的方式测量前所未有的数量的参数，但由此产生的数据的巨大维度严重限制了依赖于手动“门控”的方法。基于表型相似性对细胞进行聚类会损失单细胞分辨率，并且通常事先不知道亚群的数量。在这里，我们描述了 ACCENSE，这是一种将非线性降维和基于密度的分区相结合的工具，并在 2D 图上显示多变量细胞表型。我们将 ACCENSE 应用于源自无特定病原体和无菌小鼠的 CD8(+) T 细胞的 35 个参数的质谱流式细胞术数据，并将细胞分层为表型亚群。我们的结果显示，在已知的 CD8(+) T 细胞亚群中存在显著的异质性，值得注意的是，我们在无特定病原体和无菌小鼠中都发现了一个以前未描述的大型新型亚群。该亚群在通过 ACCENSE 分析时具有与传统幼稚和记忆亚群不同的表型特征，但在标准标志物的双轴图上无法区分。我们能够基于分析的所有蛋白质自动识别细胞亚群，从而有助于充分利用像质谱流式细胞术这样强大的新型单细胞技术。
