Literature DB >> 26865463

What Is the Best Method to Fit Time-Resolved Data? A Comparison of the Residual Minimization and the Maximum Likelihood Techniques As Applied to Experimental Time-Correlated, Single-Photon Counting Data.

Kalyan Santra1,2, Jinchun Zhan3, Xueyu Song1,2, Emily A Smith1,2, Namrata Vaswani3, Jacob W Petrich1,2.   

Abstract

The need for measuring fluorescence lifetimes of species in subdiffraction-limited volumes in, for example, stimulated emission depletion (STED) microscopy, entails the dual challenge of probing a small number of fluorophores and fitting the concomitant sparse data set to the appropriate excited-state decay function. This need has stimulated a further investigation into the relative merits of two fitting techniques commonly referred to as "residual minimization" (RM) and "maximum likelihood" (ML). Fluorescence decays of the well-characterized standard, rose bengal in methanol at room temperature (530 ± 10 ps), were acquired in a set of five experiments in which the total number of "photon counts" was approximately 20, 200, 1000, 3000, and 6000 and there were about 2-200 counts at the maxima of the respective decays. Each set of experiments was repeated 50 times to generate the appropriate statistics. Each of the 250 data sets was analyzed by ML and two different RM methods (differing in the weighting of residuals) using in-house routines and compared with a frequently used commercial RM routine. Convolution with a real instrument response function was always included in the fitting. While RM using Pearson's weighting of residuals can recover the correct mean result with a total number of counts of 1000 or more, ML distinguishes itself by yielding, in all cases, the same mean lifetime within 2% of the accepted value. For 200 total counts and greater, ML always provides a standard deviation of <10% of the mean lifetime, and even at 20 total counts there is only 20% error in the mean lifetime. The robustness of ML advocates its use for sparse data sets such as those acquired in some subdiffraction-limited microscopies, such as STED, and, more importantly, provides greater motivation for exploiting the time-resolved capacities of this technique to acquire and analyze fluorescence lifetime data.

Entities:  

Year:  2016        PMID: 26865463     DOI: 10.1021/acs.jpcb.6b00154

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  7 in total

1.  Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy.

Authors:  Shulei Wang; Jenu V Chacko; Abdul K Sagar; Kevin W Eliceiri; Ming Yuan
Journal:  Biomed Opt Express       Date:  2019-10-03       Impact factor: 3.732

2.  Global analysis and Decay Associated Images (DAI) derived from Fluorescence Lifetime Imaging Microscopy (FLIM).

Authors:  Mitchell Harling; Gregory R Alspaugh; Alessio Andreoni; Aleksandr V Smirnov; Rozhin Penjweini; Michael Murphy; Marie-Paule Strub; Jay R Knutson
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-02-22

3.  Electrically controlling and optically observing the membrane potential of supported lipid bilayers.

Authors:  Shimon Yudovich; Adan Marzouqe; Joseph Kantorovitsch; Eti Teblum; Tao Chen; Jörg Enderlein; Evan W Miller; Shimon Weiss
Journal:  Biophys J       Date:  2022-05-25       Impact factor: 3.699

4.  Empirical Bayes method using surrounding pixel information for number and brightness analysis.

Authors:  Ryosuke Fukushima; Johtaro Yamamoto; Masataka Kinjo
Journal:  Biophys J       Date:  2021-04-01       Impact factor: 3.699

5.  Review of in vivo optical molecular imaging and sensing from x-ray excitation.

Authors:  Brian W Pogue; Rongxiao Zhang; Xu Cao; Jeremy Mengyu Jia; Arthur Petusseau; Petr Bruza; Sergei A Vinogradov
Journal:  J Biomed Opt       Date:  2021-01       Impact factor: 3.170

6.  Giant light-harvesting nanoantenna for single-molecule detection in ambient light.

Authors:  Kateryna Trofymchuk; Andreas Reisch; Pascal Didier; François Fras; Pierre Gilliot; Yves Mely; Andrey S Klymchenko
Journal:  Nat Photonics       Date:  2017-09-29       Impact factor: 38.771

7.  Optimal parameters in variable-velocity scanning luminescence lifetime microscopy.

Authors:  Zdeněk Petrášek; Juan M Bolivar; Bernd Nidetzky
Journal:  Microsc Res Tech       Date:  2020-07-28       Impact factor: 2.769

  7 in total

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