Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP
Fluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often ncomp kinetic processes underlie the observed fluorescence at all locations, but the intensity of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2007-01-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v18/i08/paper |
| _version_ | 1819151578203947008 |
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| author | Sergey Laptenok Katharine M. Mullen Jan Willem Borst Ivo H. M. van Stokkum Vladimir V. Apanasovich Antonie J. W. G. Visser |
| author_facet | Sergey Laptenok Katharine M. Mullen Jan Willem Borst Ivo H. M. van Stokkum Vladimir V. Apanasovich Antonie J. W. G. Visser |
| author_sort | Sergey Laptenok |
| collection | DOAJ |
| description | Fluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often ncomp kinetic processes underlie the observed fluorescence at all locations, but the intensity of the fluorescence associated with each process varies per-location, i.e., per-pixel imaged. Then the statistical challenge is global analysis of the image: use of the fluorescence decay in time at all locations to estimate the ncomp lifetimes associated with the kinetic processes, as well as the amplitude of each kinetic process at each location. Given that typical FLIM images represent on the order of 102 timepoints and 103 locations, meeting this challenge is computationally intensive. Here the utility of the TIMP package for R to solve parameter estimation problems arising in FLIM image analysis is demonstrated. Case studies on simulated and real data evidence the applicability of the partitioned variable projection algorithm implemented in TIMP to the problem domain, and showcase options included in the package for the visual validation of models for FLIM data. |
| first_indexed | 2024-12-22T14:35:37Z |
| format | Article |
| id | doaj.art-a5906df421e240ab82d48a03a6504ed0 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-22T14:35:37Z |
| publishDate | 2007-01-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-a5906df421e240ab82d48a03a6504ed02022-12-21T18:22:39ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-01-01188Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMPSergey LaptenokKatharine M. MullenJan Willem BorstIvo H. M. van StokkumVladimir V. ApanasovichAntonie J. W. G. VisserFluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often ncomp kinetic processes underlie the observed fluorescence at all locations, but the intensity of the fluorescence associated with each process varies per-location, i.e., per-pixel imaged. Then the statistical challenge is global analysis of the image: use of the fluorescence decay in time at all locations to estimate the ncomp lifetimes associated with the kinetic processes, as well as the amplitude of each kinetic process at each location. Given that typical FLIM images represent on the order of 102 timepoints and 103 locations, meeting this challenge is computationally intensive. Here the utility of the TIMP package for R to solve parameter estimation problems arising in FLIM image analysis is demonstrated. Case studies on simulated and real data evidence the applicability of the partitioned variable projection algorithm implemented in TIMP to the problem domain, and showcase options included in the package for the visual validation of models for FLIM data.http://www.jstatsoft.org/v18/i08/paperFLIMglobal analysisspectroscopyseparable nonlinear least squaressuperposition |
| spellingShingle | Sergey Laptenok Katharine M. Mullen Jan Willem Borst Ivo H. M. van Stokkum Vladimir V. Apanasovich Antonie J. W. G. Visser Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP Journal of Statistical Software FLIM global analysis spectroscopy separable nonlinear least squares superposition |
| title | Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP |
| title_full | Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP |
| title_fullStr | Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP |
| title_full_unstemmed | Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP |
| title_short | Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP |
| title_sort | fluorescence lifetime imaging microscopy flim data analysis with timp |
| topic | FLIM global analysis spectroscopy separable nonlinear least squares superposition |
| url | http://www.jstatsoft.org/v18/i08/paper |
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