RT Journal Article
SR Electronic
T1 Inferring membrane properties during clathrin-mediated endocytosis using machine learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.01.11.523591
DO 10.1101/2023.01.11.523591
A1 Zhiwei Lin
A1 Zhiping Mao
A1 Rui Ma
YR 2023
UL http://biorxiv.org/content/early/2023/01/13/2023.01.11.523591.abstract
AB Endocytosis is a fundamental cellular process for eukaryotic cells to transport molecules into the cell. To understand the molecular mechanisms behind the process, researchers have obtained abundant biochemical information about the protein dynamics involved in endocytosis via fluorescence microscopy and geometric information about membrane shapes via electron tomography. However, measuring the biophysical information, such as the osmotic pressure and the membrane tension, remains a problem due to the small dimension of the endocytic invagination. In this work, we combine Machine Learning and Helfrich model of the membrane, as well as the dataset of membrane shapes extracted from the electron tomography to infer biophysical information about endocytosis. Our results show that Machine Learning is able to find solutions that both match the experimental profile and fulfill the membrane shape equations. Furthermore, we show that at the early stage of endocytosis, the inferred membrane tension is negative, which implies strong compressive forces acting at the boundary of the endocytic invagination. This method provides a generic framework to extract membrane information from the super-resolution imaging.Competing Interest StatementThe authors have declared no competing interest.