This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Cell membranes and the proteins within them control many vital processes and play a key role in health and disease. But studying them in 3D images of cells has so far meant slow, manual work.
A team from Helmholtz Munich, the Technical University of Munich (TUM) and the Biozentrum of the University of Basel has developed MemBrain v2, an AI tool that automates this task—cutting work that once took weeks down to a few hours. The study is published in Nature Methods. The freely available software finds membranes, locates specific membrane proteins and analyzes how they are spatially arranged, showing how cellular processes are organized at the molecular level.
Depending on the application, the AI requires little or no additional training data to do this. That lets researchers around the world study how cells work in detail—faster and on a much larger scale. Cryo-electron tomography (cryo-ET) is a special microscopy technique that lets researchers look inside cells—in three dimensions and at very high resolution.
Because the cells are flash-frozen for this, they are preserved almost unchanged, from whole-cell structures down to individual molecules. Membranes, however, have so far been difficult to analyze in this kind of data. "One challenge is that cryo-ET images can contain gaps in information due to technical limitations of the imaging process.
As a result, certain membrane orientations are difficult or partly impossible to see. This is exactly where MemBrain v2 comes in, automating the process," explains first author Lorenz Lamm. MemBrain-seg detects membranes directly, without requiring users to provide additional annotations or training data.
MemBrain-pick also requires only a small amount of training data: In one test, researchers manually annotated the positions of protein complexes on just a single membrane. Based on these annotations, the tool localized the corresponding protein complexes on additional membranes with an F1 score of 91%. Until now, this 3D image data had to be labeled painstakingly by hand, and the results could rarely be reused for new data sets.
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