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Detecting Transient Trapping from a Single Trajectory: A Structural Approach. ENTROPY 2021; 23:e23081044. [PMID: 34441183 PMCID: PMC8394669 DOI: 10.3390/e23081044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/30/2021] [Accepted: 08/04/2021] [Indexed: 11/21/2022]
Abstract
In this article, we introduce a new method to detect transient trapping events within a single particle trajectory, thus allowing the explicit accounting of changes in the particle’s dynamics over time. Our method is based on new measures of a smoothed recurrence matrix. The newly introduced set of measures takes into account both the spatial and temporal structure of the trajectory. Therefore, it is adapted to study short-lived trapping domains that are not visited by multiple trajectories. Contrary to most existing methods, it does not rely on using a window, sliding along the trajectory, but rather investigates the trajectory as a whole. This method provides useful information to study intracellular and plasma membrane compartmentalisation. Additionally, this method is applied to single particle trajectory data of β2-adrenergic receptors, revealing that receptor stimulation results in increased trapping of receptors in defined domains, without changing the diffusion of free receptors.
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Zhao H, Ge F, Zhang Y, Huang Z, Shi X, Xiong B, Liao X, Zhang S, He Y. Uncover Single Nanoparticle Dynamics on Live Cell Membrane with Data-Driven Historical Experience Analysis. Anal Chem 2021; 93:9559-9567. [PMID: 34210134 DOI: 10.1021/acs.analchem.1c01666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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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Affiliation(s)
- Hansen Zhao
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Feng Ge
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Yongyu Zhang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Zhenrong Huang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.,State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Xiangjun Shi
- School of Pharmaceutical Sciences, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, P. R. China
| | - Bin Xiong
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Xuebin Liao
- School of Pharmaceutical Sciences, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, P. R. China
| | - Sichun Zhang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Yan He
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
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FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins. Sci Rep 2020; 10:19954. [PMID: 33203884 PMCID: PMC7672080 DOI: 10.1038/s41598-020-75814-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 09/28/2020] [Indexed: 12/14/2022] Open
Abstract
Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these issues, we present here a robust computer program, called FluoSim, which is an interactive simulator of membrane protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. FluoSim integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell-cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments.
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