Modern immunologic research increasingly requires high-dimensional analyses to understand the complex milieu of cell types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the coexpression patterns of hundreds of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and the identification of previously unknown cellular heterogeneity in the lungs of melanoma metastasis–bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost, and accessible solution to single-cell proteomics in complex tissues.

中文翻译：

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使用传统流式细胞术和机器学习对数百种蛋白质进行高通量单细胞定量

现代免疫学研究越来越需要高维分析来了解构成疾病组织微环境的细胞类型的复杂环境。为实现这一目标，我们开发了 Infinity Flow，它使用机器学习结合了数百个重叠的流式细胞仪面板，从而能够同时分析数百万个单个细胞中数百种表面表达蛋白的共表达模式。在这项研究中，我们证明这种方法可以全面分析稳态小鼠肺的细胞成分，并鉴定黑色素瘤转移小鼠肺中以前未知的细胞异质性。我们表明，通过使用受监督的机器学习，Infinity Flow 提高了聚类或降维算法的准确性和深度。