Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.
Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.
复制标题
使用流式细胞术和多级机器学习来发现藻类脂质积累的无标记特征。
DOI：
10.1088/1478-3975/ab2c60
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发表时间：
2019
期刊：
影响因子：
2
通讯作者：
Munsky,Brian
中科院分区：
文献类型：
--
作者：
Tanhaemami,Mohammad;Alizadeh,Elaheh;Sanders,ClaireK;Marrone,BabettaL;Munsky,Brian
流式细胞术或细胞分选的大多数应用依赖于荧光染料与特定生物标志物的缀合。然而，标记的生物标志物并不总是可用的，它们可能是昂贵的，并且它们可能会破坏自然细胞的行为。基于机器学习方法的无标记量化可以帮助纠正这些问题，但是当测量中应用的标记或其他修改无意中修改内在细胞特性时，标记替换策略可能非常难以发现。在这里，我们展示了一种新的，但简单的方法，基于特征选择和线性回归分析，以整合从标记和未标记的细胞群体收集的统计信息，并确定准确的无标记单细胞定量模型。我们验证了该方法的准确性，以预测藻类细胞（Picochlorum soloecismus）在氮饥饿和脂质积累的时间过程中的脂质含量。我们的一般方法有望改善其他生物体或途径的无标记单细胞分析，其中生物标志物不方便，昂贵或破坏下游细胞过程。
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.