Data from: From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.

细胞异质性（Cell heterogeneity）以及细胞内多种分子过程相互作用所引发的内在复杂性，给当前单细胞生物学领域带来了诸多严峻挑战。我们提出了一种基于‘超级细胞统计（supercell statistics）’概念的方法，可从多参数单细胞测量数据中识别疾病表型：该方法先执行基于单细胞的平均化处理流程，再辅以机器学习分类方案。我们能够在平均单细胞数目与所需测量参数数目之间寻得最优平衡，以精准捕捉健康与患病个体间的表型差异，以及其他常规手段难以确诊的不同疾病间的表型差异。我们将该方法应用于两类单细胞数据集：一是利用细胞核图像诊断早老性疾病，二是基于多色流式细胞术（multicolor flow cytometry）分析两种非感染性葡萄膜炎——贝赫切特病（Behçet's disease）与结节病（sarcoidosis）的眼部表型。在前述场景中，对30个细胞组成的群体开展一次细胞核形态测量，即可完成健康与患病样本的分类，这与常规实验室操作规范相符。在后一场景中，我们的方法可识别出仅需5种标志物的最小集合，能够精准区分贝赫切特病与结节病。这是首次实现这两种疾病间的定量化表型区分。为获取这一清晰的表型特征，需对约100个CD8阳性T淋巴细胞（CD8+ T cells）进行检测。尽管已报道的上述分子标志物在自身免疫性疾病中发挥关键作用，但本研究首次证实CD8阳性T淋巴细胞可用于区分两种全身性炎症性疾病。除上述特定应用场景外，本研究提出的方法还可推广至其他前沿及新兴单细胞技术所产生的数据集，例如多维质谱流式细胞术（multidimensional mass cytometry）、单细胞基因表达分析及单细胞全基因组测序技术。