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.

细胞异质性以及细胞内多种分子过程相互作用所固有的复杂性，给当前的单细胞生物学研究带来了严峻挑战。我们提出了一种基于「超细胞统计（supercell statistics）」概念的方法，该方法可通过多参数单细胞测量结果识别疾病表型：首先执行基于单细胞的平均化处理流程，随后辅以机器学习分类方案。本方法能够在平均单细胞的数量与所需测量参数的数量之间寻得最优权衡，从而精准捕捉健康与患病患者间的表型差异，以及其他难以通过常规手段诊断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：一类是利用细胞核图像诊断早老性疾病的数据集，另一类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——即贝赫切特病（Behçet's disease）与结节病（sarcoidosis）的眼部临床表现——表型的数据集。在前述研究场景中，仅需对30个细胞组的细胞核形态进行一次测量，即可将样本分为健康或患病类别，这与常规实验室操作规范相符。在后一场景中，本方法可识别出由5个标记物组成的最小特征集，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+ T细胞进行检测。尽管已有的研究表明本方法所识别的分子标记物在自身免疫性疾病中发挥着重要作用，但本研究首次指出，CD8+ T细胞可用于区分两种全身性炎症性疾病。除上述具体应用场景外，本研究提出的方法还可推广至其他各类前沿及新兴单细胞技术所生成的数据集，例如多维质谱流式细胞术（multidimensional mass cytometry）、单细胞基因表达（single-cell gene expression）以及单细胞全基因组测序技术（single-cell full genome sequencing techniques）等。