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Updated: Apr 3, 2026
Genome Editing in Mammalian Cell Lines using CRISPR-Cas
Published on: April 11, 2019
Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis
Sun Hae Hong1, Christa L Cortesio1, David G Drubin1
1Department of Molecular and Cell Biology, University of California-Berkeley, Berkeley, CA 94720, USA.
Abstract:
Cells internalize various molecules through clathrin-mediated endocytosis (CME). Previous live-cell imaging studies suggested that CME is inefficient, with about half of the events terminated. These CME efficiency estimates may have been confounded by overexpression of fluorescently tagged proteins and inability to filter out false CME sites. Here, we employed genome editing and machine learning to identify and analyze authentic CME sites. We examined CME dynamics in cells that express fluorescent fusions of two defining CME proteins, AP2 and clathrin. Support vector machine classifiers were built to identify and analyze authentic CME sites. From inception until disappearance, authentic CME sites contain both AP2 and clathrin, have the same degree of limited mobility, continue to accumulate AP2 and clathrin over lifetimes >∼20 s, and almost always form vesicles as assessed by dynamin2 recruitment. Sites that contain only clathrin or AP2 show distinct dynamics, suggesting they are not part of the CME pathway.
Insights
This study identifies authentic clathrin-mediated endocytosis (CME) sites using genome editing and machine learning. Authentic CME sites consistently involve both AP2 and clathrin, ensuring efficient vesicle formation.
Area of Science:
- Cell Biology
- Molecular Biology
- Biophysics
Background:
- Clathrin-mediated endocytosis (CME) is a crucial cellular process for molecule internalization.
- Previous live-cell imaging studies reported low CME efficiency, potentially due to experimental limitations.
Purpose of the Study:
- To accurately identify and analyze authentic CME sites.
- To resolve discrepancies in previous CME efficiency estimates.
Main Methods:
- Utilized genome editing to create cells expressing fluorescent fusions of AP2 and clathrin.
- Employed machine learning, specifically support vector machine classifiers, to distinguish authentic CME sites.
- Analyzed dynamics of identified CME sites, including protein colocalization, mobility, and vesicle formation.
Main Results:
- Authentic CME sites consistently contain both AP2 and clathrin throughout their lifespan.
- Identified CME sites exhibit limited mobility and prolonged lifetimes (>∼20 s).
- Almost all authentic sites successfully recruit dynamin2, indicating vesicle formation, unlike sites with only clathrin or AP2.
Conclusions:
- Genome editing and machine learning provide a robust method for identifying authentic CME events.
- CME is a highly efficient process when authentic sites are accurately analyzed.
- Distinct dynamics of clathrin- or AP2-only sites suggest they are not involved in CME.
