G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany.
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Front Immunol. 2024 Jan 4;14:1336393. doi: 10.3389/fimmu.2023.1336393. eCollection 2023.
The last decade has led to rapid developments and increased usage of computational tools at the single-cell level. However, our knowledge remains limited in how extracellular cues alter quantitative macrophage morphology and how such morphological changes can be used to predict macrophage phenotype as well as cytokine content at the single-cell level.
Using an artificial intelligence (AI) based approach, this study determined whether (i) accurate macrophage classification and (ii) prediction of intracellular IL-10 at the single-cell level was possible, using only morphological features as predictors for AI. Using a quantitative panel of shape descriptors, our study assessed image-based original and synthetic single-cell data in two different datasets in which CD14+ monocyte-derived macrophages generated from human peripheral blood monocytes were initially primed with GM-CSF or M-CSF followed by polarization with specific stimuli in the presence/absence of continuous GM-CSF or M-CSF. Specifically, M0, M1 (GM-CSF-M1, TNFα/IFNγ-M1, GM-CSF/TNFα/IFNγ-M1) and M2 (M-CSF-M2, IL-4-M2a, M-CSF/IL-4-M2a, IL-10-M2c, M-CSF/IL-10-M2c) macrophages were examined.
Phenotypes were confirmed by ELISA and immunostaining of CD markers. Variations of polarization techniques significantly changed multiple macrophage morphological features, demonstrating that macrophage morphology is a highly sensitive, dynamic marker of phenotype. Using original and synthetic single-cell data, cell morphology alone yielded an accuracy of 93% for the classification of 6 different human macrophage phenotypes (with continuous GM-CSF or M-CSF). A similarly high phenotype classification accuracy of 95% was reached with data generated with different stimuli (discontinuous GM-CSF or M-CSF) and measured at a different time point. These comparably high accuracies clearly validated the here chosen AI-based approach. Quantitative morphology also allowed prediction of intracellular IL-10 with 95% accuracy using only original data.
Thus, image-based machine learning using morphology-based features not only (i) classified M0, M1 and M2 macrophages but also (ii) classified M2a and M2c subtypes and (iii) predicted intracellular IL-10 at the single-cell level among six phenotypes. This simple approach can be used as a general strategy not only for macrophage phenotyping but also for prediction of IL-10 content of any IL-10 producing cell, which can help improve our understanding of cytokine biology at the single-cell level.
过去十年，单细胞水平的计算工具得到了快速发展和广泛应用。然而，我们对于细胞外刺激如何改变定量巨噬细胞形态，以及这种形态变化如何用于预测单细胞水平的巨噬细胞表型和细胞因子含量，仍然知之甚少。
本研究采用基于人工智能（AI）的方法，仅使用形态特征作为 AI 的预测因子，来确定（i）是否可以准确分类巨噬细胞，以及（ii）是否可以预测单细胞内的 IL-10。本研究使用定量形态描述符面板，评估了源自人外周血单核细胞的 CD14+单核细胞衍生的巨噬细胞在初始阶段用 GM-CSF 或 M-CSF 诱导，然后用特定刺激物极化，并在存在/不存在连续 GM-CSF 或 M-CSF 的情况下，在两个不同数据集的原始和合成单细胞图像数据。具体来说，研究了 M0、M1（GM-CSF-M1、TNFα/IFNγ-M1、GM-CSF/TNFα/IFNγ-M1）和 M2（M-CSF-M2、IL-4-M2a、M-CSF/IL-4-M2a、IL-10-M2c、M-CSF/IL-10-M2c）巨噬细胞。
通过 ELISA 和 CD 标志物免疫染色证实了表型。极化技术的变化显著改变了多种巨噬细胞形态特征，表明巨噬细胞形态是表型的高度敏感、动态标志物。使用原始和合成单细胞数据，仅细胞形态就可以实现对 6 种不同人类巨噬细胞表型（有连续 GM-CSF 或 M-CSF）的分类，准确率达到 93%。使用不同刺激物（不连续 GM-CSF 或 M-CSF）生成的数据，并在不同时间点测量，也达到了类似的高表型分类准确率 95%。这些比较高的准确率清楚地验证了这里选择的基于 AI 的方法。定量形态还可以使用仅原始数据以 95%的准确率预测细胞内的 IL-10。
因此，基于形态特征的基于图像的机器学习不仅（i）可以分类 M0、M1 和 M2 巨噬细胞，还可以（ii）分类 M2a 和 M2c 亚型，以及（iii）预测单细胞水平的 6 种表型中的细胞内 IL-10。这种简单的方法不仅可以作为巨噬细胞表型分析的一般策略，还可以用于预测任何产生 IL-10 的细胞的 IL-10 含量，这有助于我们更好地理解细胞因子生物学在单细胞水平上的作用。
