Deep Learning-Assisted Automated Multidimensional Single Particle Tracking in Living Cells-Reference-Cited by-同舟云学术

Deep Learning-Assisted Automated Multidimensional Single Particle Tracking in Living Cells

Published:2024-02-28 Issue:10 Volume:24 Page:3082-3088
ISSN:1530-6984
Container-title:Nano Letters
language:en
Short-container-title:Nano Lett.

Author:

Song Dongliang¹,Zhang Xin¹,Li Baoyun¹,Sun Yuanfang¹,Mei Huihui¹,Cheng Xiaojuan²,Li Jieming³^ORCID,Cheng Xiaodong²^ORCID,Fang Ning¹^ORCID

Affiliation:

1. State Key Laboratory of Physical Chemistry of Solid Surfaces, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China, 361005

2. School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China, 325035

3. Bristol Myers Squibb Company, New Brunswick, New Jersey 08901, United States

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province

Key Laboratory of Spectrochemical Analysis & Instrumentation, Xiamen University, Ministry of Education

Kunpeng Action Plan award

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.3c04870

Reference39 articles.

1. Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion

2. Single quantum dot tracking reveals the impact of nanoparticle surface on intracellular state

3. Quantum dot-loaded monofunctionalized DNA icosahedra for single-particle tracking of endocytic pathways

4. Single particle trajectories reveal active endoplasmic reticulum luminal flow

5. Automated Plasmonic Resonance Scattering Imaging Analysis via Deep Learning