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Uncover Single Nanoparticle Dynamics on Live Cell Membrane with Data-Driven Historical Experience Analysis.
Hansen Zhao1, Feng Ge1, Yongyu Zhang1
1Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.
Analytical Chemistry
|July 2, 2021
Summary
SEES analysis reveals hidden patterns in particle movement within biological systems. This data-driven method, superior to Hidden Markov Models, identifies rare events and informs nanocargo design.
Area of Science:
- Biophysics
- Nanotechnology
- Cell Biology
Background:
- Analyzing spatiotemporal dynamics of particles in biological environments is essential for understanding biological processes.
- Existing methods like Hidden Markov Model (HMM) have limitations in analyzing complex trajectories and identifying rare events.
Purpose of the Study:
- To introduce a novel data-driven method, Spatiotemporal Experience-based Exploration Strategy (SEES), for analyzing complex particle trajectories.
- To compare the efficacy of SEES with HMM in uncovering hidden information and identifying rare events in biological dynamics.
- To apply SEES to investigate nanoparticle interactions with programmed death ligand 1 (PD-L1) expressing cells.
Main Methods:
- Developed SEES, a method based on historical experience vector analysis, to identify global patterns and local state continuities in trajectories without predefined models.
- Compared SEES performance against the Hidden Markov Model (HMM) using simulated and experimental single-particle tracking (SPT) data.
- Applied SEES to analyze the dynamics of nanoparticles interacting with live cells expressing PD-L1.
Main Results:
- SEES demonstrated higher sensitivity in identifying rare events and better utilization of multivariable observations compared to HMM.
- SEES successfully pinpointed rare transmembrane events, visualized nanoparticle 'Brownian searching' motion on cell membranes, and differentiated dynamics among trajectories.
- Found that PD-L1 expression levels influenced nanoparticle rotation and cellular uptake efficiency.
Conclusions:
- SEES is an effective data-driven approach for analyzing complex biological particle dynamics, offering advantages over traditional methods like HMM.
- The findings provide insights into nanoparticle-cell interactions, particularly concerning PD-L1 expression.
- SEES-enabled insights can guide the rational design of more efficient nanocargoes for therapeutic and diagnostic applications.
