Yoda Tsuyoshi
Aomori Prefectural Industrial Technology Research Center, Hachinohe Industrial Research Institute, Hachinohe City, Aomori 039-2245, Japan.
The United Graduate School of Agricultural Sciences, Iwate University, Morioka City, Iwate 020-8550, Japan.
Microsc Microanal. 2022 Sep 19:1-8. doi: 10.1017/S1431927622012521.
Recent studies indicated that ergosterol (Erg) helps form strongly ordered lipid domains in membranes that depend on their chemical characters. However, direct evidence of concentration-dependent interaction of Erg with lipid membranes has not been reported. We studied the Erg concentration-dependent changes in the phase behaviors of membranes using cell-sized liposomes containing 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC)/1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC). We observed the concentration range of phase separation in ternary membranes was significantly wider when Erg rather than cholesterol (Chol) was used as the sterol component. We used machine learning for the first time to analyze microscopic images of cell-sized liposomes and identify phase-separated structures. The automated method was successful in identifying homogeneous membranes but performance remained data-limited for the identification of phase separation domains characterized by more complex features.
最近的研究表明,麦角固醇(Erg)有助于在膜中形成高度有序的脂质结构域,这取决于它们的化学特性。然而,尚未有关于Erg与脂质膜浓度依赖性相互作用的直接证据报道。我们使用含有1,2-二油酰基-sn-甘油-3-磷酸胆碱(DOPC)/1,2-二棕榈酰基-sn-甘油-3-磷酸胆碱(DPPC)的细胞大小脂质体,研究了膜相行为中Erg浓度依赖性变化。我们观察到,当使用Erg而非胆固醇(Chol)作为甾醇成分时,三元膜中相分离的浓度范围显著更宽。我们首次使用机器学习来分析细胞大小脂质体的微观图像并识别相分离结构。这种自动化方法成功识别了均匀膜,但对于识别具有更复杂特征的相分离域,其性能仍受数据限制。