Noninvasive detection of macrophage activation with single-cell resolution through machine learning
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
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DOI：
10.1073/pnas.1711872115
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发表时间：
2018-03-20
影响因子：
11.1
通讯作者：
Smith, Nicholas I.
中科院分区：
文献类型：
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作者：
Pavillon, Nicolas;Hobro, Alison J.;Smith, Nicholas I.
我们提出了一种基于通过无标记显微镜获取多个参数的方法，能够对刺激响应的微小细胞变化进行无创研究。检索到的参数与细胞的不同属性相关。从定量相显微镜和自发荧光图像中提取形态变量，同时通过拉曼光谱检索分子指示剂。我们表明，这些独立参数可用于构建基于逻辑回归的多变量统计模型，我们将其应用于单细胞水平上脂多糖（LPS）暴露诱导的巨噬细胞激活的检测，并比较它们在评估个体细胞状态方面各自的表现。从形态学或拉曼生成的模型可以可靠、独立地检测巨噬细胞的激活状态，并通过与巨噬细胞的细胞因子分泌和细胞内免疫反应相关分子的表达进行比较来验证这一点。独立模型在激活程度上达成一致，表明这些特征提供了对细胞反应异质性的洞察。我们发现形态指标与表型相关，而表型主要与下游效应相关，使得这些变量获得的结果具有剂量依赖性。另一方面，拉曼指标代表与特定激活途径相关的上游细胞内分子变化。通过使用孕酮部分抑制 LPS 诱导的激活，我们可以识别几个亚群，表明我们的方法能够识别 LPS 激活的影响、LPS 的特异性抑制以及单独孕酮对巨噬细胞的影响。
We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.