Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method's accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

中文翻译：

---

使用流式细胞仪和多阶段机器学习来发现无标记的藻类脂质积聚特征。

流式细胞术或细胞分选的大多数应用都依赖于荧光染料与特定生物标记物的结合。但是，标记的生物标志物并非总是可用，它们可能很昂贵，并且可能破坏天然细胞的行为。基于机器学习方法的无标签定量可以帮助纠正这些问题，但是当应用的标签或测量中的其他修改无意中修改了固有的细胞特性时，很难发现标签替换策略。在这里，我们展示了一种基于特征选择和线性回归分析的新方法，但该方法简单易行，可整合从标记和未标记细胞群体收集的统计信息，并确定用于无标记单细胞准确定量的模型。我们验证方法' 可以准确预测氮饥饿和脂质积累时间过程中藻类细胞（Picochlorum soloecismus）中脂质的含量。我们的通用方法有望改善对其他生物或途径的无标记单细胞分析，在这些生物或途径中生物标记不方便，昂贵或破坏下游细胞过程。