MONTE CARLO SIMULATION OF FLUORESCENCE CORRELATION SPECTROSCOPY DATA

被引:3
|
作者
Kosovan, Peter [1 ]
Uhlik, Filip [1 ]
Kuldova, Jitka [1 ]
Stepanek, Miroslav [1 ]
Limpouchova, Zuzana [1 ]
Prochazka, Karel [1 ]
Benda, Ales [2 ]
Humpolickova, Jana [2 ]
Hof, Martin [2 ]
机构
[1] Charles Univ Prague, Fac Sci, Dept Phys & Macromol Chem, Prague 12843 2, Czech Republic
[2] Acad Sci Czech Republic, J Heyrovsky Inst Phys Chem, VVI, Prague 8, Czech Republic
关键词
Diffusion coefficient; Dynamic light scattering; Fluorescence spectroscopy; Monte Carlo method; Rotational diffusion; Translational diffusion; ATOMIC-FORCE MICROSCOPY; LIGHT-SCATTERING; BEHAVIOR; MICELLES; BINDING;
D O I
10.1135/cccc2009526
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
We employed the Monte Carlo simulation methodology to emulate the diffusion of fluorescently labeled particles and understand the source of differences between values of diffusion coefficients (and consequently hydrodynamic radii) of fluorescently labeled nanoparticles measured by fluorescence correlation spectroscopy (FCS) and dynamic light scattering (DLS). We used the simulation program developed in our laboratory and studied the diffusion of spherical particles of different sizes, which are labeled on their surface. In this study, we focused on two complicating effects: (i) multiple labeling and (ii) rotational diffusion which affect the fluorescence signal from large particles and hinder the analysis of autocorrelation functions according to simple analytical models. We have shown that the fluorescence fluctuations can be well fitted using the analytical model for small point-like particles, but the obtained parameters deviate in some cases significantly from the real ones. It means that the current data treatment yields apparent values of diffusion coefficients and other parameters only and the interpretation of experimental results for systems of particles with sizes comparable to the size of the active illuminated volume requires great care and precaution.
引用
收藏
页码:207 / 222
页数:16
相关论文
共 50 条
  • [41] Hydration and conformations of polycytidylic acid from the data of infrared spectroscopy, EHF dielectrometry and Monte Carlo simulation
    Maleev, VY
    Semenov, MA
    Kashpur, VA
    Bolbukh, TV
    Shestopalova, AV
    SPECTROSCOPY OF BIOLOGICAL MOLECULES: NEW DIRECTIONS, 1999, : 227 - 228
  • [42] Correlation of Calculated Vancomycin Trough Concentrations and Exposure: A Monte Carlo Simulation
    Haag, Hans
    Lau, Anthony
    ANNALS OF PHARMACOTHERAPY, 2023, 57 (12) : 1410 - 1414
  • [43] MONTE CARLO SIMULATION OF A PROTON-PROTON SPIN CORRELATION EXPERIMENT
    BRYANT, HC
    JARMIE, N
    PHYSICAL REVIEW, 1967, 155 (05): : 1444 - &
  • [44] Estimating the chemical rank of three-way fluorescence data by vector subspace projection with Monte Carlo simulation
    Li, Yong
    Wu, Hai-Long
    Zhang, Xiao-Hua
    Chen, Yao
    Gu, Hui-Wen
    Zuo, Qi
    Zhang, Yan
    Guo, Shan-Shan
    Liu, Xin-Yi
    Yu, Ru-Qin
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2014, 136 : 15 - 23
  • [45] MONTE-CARLO SIMULATION OF MULTIPLE-SCATTERING IN COMPTON SPECTROSCOPY
    PERSLIDEN, J
    ACTA RADIOLOGICA, 1992, 33 (04) : 384 - 387
  • [46] Monte Carlo Simulation Of The Data Acquisition Chain Of Scintillation Detectors
    Binda, F.
    Ericsson, G.
    Hellesen, C.
    Hjalmarsson, A.
    Eriksson, J.
    Skiba, M.
    Conroy, S.
    Weiszflog, M.
    FUSION REACTOR DIAGNOSTICS, 2014, 1612 : 101 - 104
  • [47] REVERSE MONTE-CARLO SIMULATION FOR THE ANALYSIS OF EXAFS DATA
    GURMAN, SJ
    MCGREEVY, RL
    JOURNAL OF PHYSICS-CONDENSED MATTER, 1990, 2 (48) : 9463 - 9473
  • [48] Use of Monte Carlo Simulation in Remote Sensing Data Analysis
    Ebrahimi, Hamideh
    Aslebagh, Shadi
    Jones, Linwood
    2013 PROCEEDINGS OF IEEE SOUTHEASTCON, 2013,
  • [49] A Monte Carlo triple-GEM simulation tuned with data
    Cossio, F.
    Alexeev, M.
    Amoroso, A.
    Ferroli, R. Baldini
    Balossino, I
    Bertani, M.
    Bettoni, D.
    Bianchi, F.
    Bortone, A.
    Calcaterra, A.
    Canale, N.
    Capodiferro, M.
    Cassariti, V
    Cerioni, S.
    Chai, J.
    Cheng, W.
    Chiozzi, S.
    Cibinetto, G.
    Ramusino, A. Cotta
    Cotto, G.
    De Mori, F.
    Destefanis, M.
    Dong, J.
    Evangelisti, F.
    Farinelli, R.
    Fava, L.
    Felici, G.
    Fioravanti, E.
    Garzia, I
    Gatta, M.
    Giraudo, G.
    Greco, M.
    Lavezzi, L.
    Leng, C.
    Li, H.
    Maggiora, M.
    Malaguti, R.
    Mangoni, A.
    Marcello, S.
    Melchiorri, M.
    Mezzadri, G.
    Mignone, M.
    Morello, G.
    Pacetti, S.
    Patteri, P.
    Pellegrino, J.
    Pelosi, A.
    Rivetti, A.
    da Rocha Rolo, M.
    Savrie, M.
    2018 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE PROCEEDINGS (NSS/MIC), 2018,
  • [50] Nuclear data generation & implementation for analog Monte Carlo simulation
    Ramirez, Camilo Cordero
    Jouanne, Cedric
    15TH INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY, ND2022, 2023, 284