Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image-processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground-truth data sets mimicking a series of compositions along a tieline of Ld + Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase with >90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain-size distributions, number of domains, and phase-area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts.

中文翻译：

---

使用机器学习方法分析相分离脂质双层囊泡的冷冻电镜图像


生物膜中的横向脂质异质性（即筏形成）在活细胞中发挥着功能作用。低熔点和高熔点脂质加胆固醇的三组分混合物为筏域提供了简化的实验模型，其中液体无序（Ld）相与液体有序（Lo）相共存。使用此类模型，我们最近表明低温电子显微镜（cryo-EM）可以根据双层厚度的差异检测脂质囊泡中的相分离。然而，冷冻电镜数据中存在相当大的噪声，这对在高空间分辨率下准确确定膜相状态提出了重大挑战。为此，我们开发了一种图像处理流程，利用机器学习（ML）来预测脂质囊泡投影图像中的双层相。重要的是，ML 方法利用了 Lo 与 Ld 的厚度和分子密度的差异，从而改进了物相识别。为了评估准确性，我们使用了 Lo 和 Ld 相的全原子分子动力学模拟生成的相分离脂质囊泡的人工图像。创建了模拟沿着 Ld + Lo 共存关系线的一系列成分的合成地面实况数据集，然后使用各种 ML 模型进行分析。对于所有联络线组合物，我们发现 ML 方法可以以 >90% 的准确度正确识别双层相，从而提供一种分离共存 Ld 和 Lo 相的强度分布的方法，并准确确定域尺寸分布、数量域和相面积分数。 这里描述的方法提供了一个表征膜中纳米级横向异质性的框架，并为更详细地了解生物环境中的筏特性铺平了道路。