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Identifying Multiple Populations from Single‐Molecule Lifetime Distributions
Author(s) -
Kastantin Mark,
Schwartz Daniel K.
Publication year - 2013
Publication title -
chemphyschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 140
eISSN - 1439-7641
pISSN - 1439-4235
DOI - 10.1002/cphc.201200838
Subject(s) - biological system , magnitude (astronomy) , population , molecule , range (aeronautics) , statistical physics , chemistry , statistics , chemical physics , analytical chemistry (journal) , computational physics , physics , mathematics , materials science , chromatography , biology , astrophysics , demography , organic chemistry , sociology , composite material
A major advantage of single‐molecule methods over ensemble‐averaging techniques involves the ability to characterize heterogeneity through the identification of multiple molecular populations. It can be challenging, however, to determine absolute values of dynamic parameters (and to relate these values to those determined from a conventional method) because characteristic timescales of various populations may vary over many orders of magnitude, and under a given set of experimental conditions instrumental sensitivity to various populations may be unequal. Using data obtained from the single‐molecule tracking microscopy of fibrinogen protein adsorption and desorption, it is shown that by performing a combined analysis of molecular trajectories obtained using a range of acquisition times, it is possible to extract quantitative absolute values of multiple population fractions and residence times (with well‐defined uncertainties), even when these values span many orders of magnitude. In particular, as many as six distinct populations are rigorously identified, exhibiting characteristic timescales that vary over nearly three orders of magnitude with population fractions as small as one part in a thousand. This approach will lead to better comparability between single‐molecule experiments and may be useful in connecting single‐molecule to ensemble‐averaged observations.