2022-11-02 ワシントン大学セントルイス
研究チームは、ノイズの多い1枚のピクセル画像から、3次元空間における分子の向きと、2次元における分子の位置、5つのパラメータを識別するシステムを設計することができた。
最終的には、このシステムは、アミロイドタンパク質が集合してアルツハイマー病に関連するもつれた構造を形成する方法のような、小さなスケールでの生物学的プロセスの理解を深めるために、研究者を支援することができるようになる。
<関連情報>
- https://source.wustl.edu/2022/11/machine-learning-generates-pictures-of-proteins-in-5d/
- https://engineering.wustl.edu/news/2022/Machine-learning-generates-pictures-of-proteins-in-5D.html
- https://opg.optica.org/oe/fulltext.cfm?uri=oe-30-20-36761&id=505938
Deep-SMOLM:ディープラーニングにより、重なり合う単一分子の3次元配向と2次元位置をナノスケールの最適な解像度で解像する Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution
Tingting Wu, Peng Lu, Md Ashequr Rahman, Xiao Li, and Matthew D. Lew
Optics Express Published: September 21, 2022
DOI:https://doi.org/10.1364/OE.470146
Abstract
Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high-dimensional information–both orientation and position–greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOLM). Here, we report a deep-learning based estimator, termed Deep-SMOLM, that achieves superior 3D orientation and 2D position measurement precision within 3% of the theoretical limit (3.8° orientation, 0.32 sr wobble angle, and 8.5 nm lateral position using 1000 detected photons). Deep-SMOLM also demonstrates state-of-art estimation performance on overlapping images of emitters, e.g., a 0.95 Jaccard index for emitters separated by 139 nm, corresponding to a 43% image overlap. Deep-SMOLM accurately and precisely reconstructs 5D information of both simulated biological fibers and experimental amyloid fibrils from images containing highly overlapped DSFs at a speed ~10 times faster than iterative estimators.
