Deep Learning-Assisted Automated Multidimensional Single Particle Tracking in Living Cells.
Nano Lett
; 24(10): 3082-3088, 2024 Mar 13.
Article
em En
| MEDLINE
| ID: mdl-38416583
ABSTRACT
The translational and rotational dynamics of anisotropic optical nanoprobes revealed in single particle tracking (SPT) experiments offer molecular-level information about cellular activities. Here, we report an automated high-speed multidimensional SPT system integrated with a deep learning algorithm for tracking the 3D orientation of anisotropic gold nanoparticle probes in living cells with high localization precision (<10 nm) and temporal resolution (0.9 ms), overcoming the limitations of rotational tracking under low signal-to-noise ratio (S/N) conditions. This method can resolve the azimuth (0°-360°) and polar angles (0°-90°) with errors of less than 2° on the experimental and simulated data under S/N of â¼4. Even when the S/N approaches the limit of 1, this method still maintains better robustness and noise resistance than the conventional pattern matching methods. The usefulness of this multidimensional SPT system has been demonstrated with a study of the motions of cargos transported along the microtubules within living cells.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Nanopartículas Metálicas
/
Aprendizado Profundo
Idioma:
En
Revista:
Nano Lett
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Estados Unidos