Reveal heterogeneous motion states in single nanoparticle trajectory using its own history

Zhao, Hansen; Ge, Feng; Zhang, Sichun; Zhang, Xinrong; He, Yan

doi:10.1007/s11426-020-9896-8

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…Synthesis and characterization of AuNRs with a size of 40 nm × 85 nm (Method S1 and Figure S1) were performed as previously described. , AuNRs and cells were imaged using a darkfield microscope (DFM). The DFM was equipped with a 60× plan objective with iris and a darkfield condenser (NA: 1.20–1.43) on a Nikon 80i upright microscope.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…26 To investigate the complex particle dynamics such as the NP-cell membrane interactions, which is too complicated to predict how many states there exist for the intrinsic spatiotemporal heterogeneity on the live cell membrane, a data-driven method is needed to analyze the time series data in an objective and unbiased manner to minimize the prior knowledge needed and avoid intensive fitting or averaging operations. To achieve this goal, we have proposed an algorithm named SEES 27 that lets the inner differences of the particle dynamics emerge by themselves as an intuitive colored pattern. After that, one can interpret the biophysical mechanism of the trajectory according to the colored pattern as well as further statistical/physical model-based analysis (post-process).…”

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confidence: 99%

“…Given the fundamental design logic of SEES and the general performance of the method, 27 in this work, we focused on its application and compared its performance with the existing methods. We chose HMM, one of the most widely used model-based techniques, as a reference algorithm to validate the robustness and the generality of the SEES.…”

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confidence: 99%

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Uncover Single Nanoparticle Dynamics on Live Cell Membrane with Data-Driven Historical Experience Analysis

Zhao

Zhang

et al. 2021

Anal. Chem.

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Understanding the spatiotemporal dynamics of particles in a complex biological environment is crucial for the study of related biological processes. To analyze the complicated trajectories recorded from single-particle tracking (SPT), we have proposed a method named SEES based on historical experience vector analysis, which allows both the global patterns and local state continuities of a trajectory to emerge by themselves as color segments without predefined models. This method implements a data-driven strategy and thus uncovers the hidden information with less prior knowledge or subjective bias. Here, we demonstrate its efficiency by comparing its performance with the Hidden Markov model (HMM), one of the most widely used methods in time series processing. The results demonstrated that the SEES operator was more sensitive in identifying rare events and could utilize multivariable observations in the dynamic processes to uncover more details. We applied the method to analyze the dynamics of nanoparticles interacting with live cells expressing programmed death ligand 1 (PD-L1) on the membrane. The results showed that the SEES operator can successfully pinpoint the transmembrane rare events, visualize the on-membrane "Brownian searching" motion, and evaluate different dynamics among multiple trajectories. Furthermore, we found that the PD-L1 expression level on the cell membrane affected the rotation behavior of the nanoparticle as well as the cellular uptake efficiency. These findings enabled by SEES could potentially help the rational design of highly efficient nanocargoes.

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Section: Materials and Methodsmentioning

confidence: 99%

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confidence: 99%