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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 8 — Apr. 12, 2010
  • pp: 8688–8696
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Precise fluorophore lifetime mapping in live-cell, multi-photon excitation microscopy

Ching-Wei Chang and Mary-Ann Mycek  »View Author Affiliations


Optics Express, Vol. 18, Issue 8, pp. 8688-8696 (2010)
http://dx.doi.org/10.1364/OE.18.008688


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Abstract

Fluorophore excited state lifetime is a useful indicator of micro-environment in cellular optical molecular imaging. For quantitative sensing, precise lifetime determination is important, yet is often difficult to accomplish when using the experimental conditions favored by live cells. Here we report the first application of temporal optimization and spatial denoising methods to two-photon time-correlated single photon counting (TCSPC) fluorescence lifetime imaging microscopy (FLIM) to improve lifetime precision in live-cell images. The results demonstrated a greater than five-fold improvement in lifetime precision. This approach minimizes the adverse effects of excitation light on live cells and should benefit FLIM applications to high content analysis and bioimage informatics.

© 2010 OSA

1. Introduction

Fluorophore excited state lifetime is an intrinsic property of fluorophores that is sensitive to micro-environmental conditions such as temperature, pH, and interactions with other molecules. Hence, it can be employed as an optical sensor to indicate, for example, Förster resonance energy transfer, oxygen levels, and the conformational state of endogenous / exogenous fluorophores in live-cell and in vivo studies [1

1. S. Bloch, F. Lesage, L. McIntosh, A. Gandjbakhche, K. X. Liang, and S. Achilefu, “Whole-body fluorescence lifetime imaging of a tumor-targeted near-infrared molecular probe in mice,” J. Biomed. Opt. 10(5), 054003 (2005). [CrossRef] [PubMed]

5

5. D. Sud, W. Zhong, D. G. Beer, and M. A. Mycek, “Time-resolved optical imaging provides a molecular snapshot of altered metabolic function in living human cancer cell models,” Opt. Express 14(10), 4412–4426 (2006). [CrossRef] [PubMed]

]. Importantly, fluorescence lifetimes are relatively insensitive to the factors affecting intensity: variation in excitation source intensity, detection gain setting, optical loss in the optical path or sample, variation in sample fluorophore concentration, photobleaching, and microscope focusing [6

6. C. W. Chang, D. Sud, and M. A. Mycek, “Fluorescence lifetime imaging microscopy,” Methods Cell Biol. 81, 495–524 (2007). [CrossRef] [PubMed]

].

To improve the precision of microscopic fluorescence imaging, several “denoising” (noise removal) techniques have been proposed for fluorescence microscopy and FLIM. Wavelet analysis [12

12. C. Vonesch, “Fast and automated wavelet-regularized image restoration in fluorescence microscopy,” Ph.D. thesis, École Polytechnique Fédérale De Lausanne (2009).

] has been used for denoising images from confocal and full-field frequency-domain FLIM [13

13. C. Buranachai, D. Kamiyama, A. Chiba, B. D. Williams, and R. M. Clegg, “Rapid frequency-domain FLIM spinning disk confocal microscope: lifetime resolution, image improvement and wavelet analysis,” J. Fluoresc. 18(5), 929–942 (2008). [CrossRef] [PubMed]

,14

14. B. Q. Spring and R. M. Clegg, “Image analysis for denoising full-field frequency-domain fluorescence lifetime images,” J. Microsc. (Oxford) 235(2), 221–237 (2009). [CrossRef]

]. Non-parametric regression method [15

15. J. Boulanger, J. B. Sibarita, C. Kervrann, and P. Bouthemy, “Non-parametric regression for patch-based fluorescence microscopy image sequence denoising,” 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols 1–4, 748–751 (2008).

] and multiframe SURE-LET (Stein’s unbiased risk estimate -linear expansion of thresholds) denoising [16

16. S. Delpretti, F. Luisier, S. Ramani, T. Blu, and M. Unser, “Multiframe SURE-LET denoising of timelapse fluorescence microscopy images,” 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols 1–4, 149–152 (2008).

] have been reported for fluorescence microscopy image denoising. However, it has not been reported that denoising can be used in time-domain FLIM for improvements of lifetime determination.

We previously tested the novel TV denoising models developed in our laboratory with artificial images and with images of fluorescent beads and live-cells acquired from a wide-field time-gated FLIM system [24

24. C. W. Chang, “Improving Accuracy and Precision in Biological Applications of Fluorescence Lifetime Imaging Microscopy,” Ph.D. thesis, University of Michigan (2009).

26

26. C. W. Chang and M. A. Mycek, “Increasing precision of lifetime determination in fluorescence lifetime imaging,” Proc. SPIE 7570, 757007 (2010). [CrossRef]

]. Tests with artificial images indicated that there was no lifetime bias in the fluorescence lifetime value, provided that the estimation of noise magnitude was accurate. Tests with fluorescent beads demonstrated that TV denoising could be combined with optimal gating to achieve lifetime precision improvement greater than 4-fold, with the lifetime values basically uniform inside the beads, as expected. Tests with live-cells indicated that the novel denoising models preserved the overall lifetime and amplitude values of the single-exponential decay model while improving local lifetime fitting.

In this study, we further focus on lifetime precision improvements in time-correlated single photon counting (TCSPC) FLIM, a widely used approach, where fluorescence decay curves are constructed by photon emission histograms [6

6. C. W. Chang, D. Sud, and M. A. Mycek, “Fluorescence lifetime imaging microscopy,” Methods Cell Biol. 81, 495–524 (2007). [CrossRef] [PubMed]

], as in Fig. 1
Fig. 1 Concept of virtual gating of TCSPC data. The decay curves were constructed by photon emission histograms, to which virtual gating could be applied by summing up the values of the data points within each virtual gate to form a time-gated intensity image.
. We demonstrate, for the first time, how temporal optimization (optimal virtual gating, see section 2.3) and spatial denoising methods can be used to improve the precision of lifetime determination in live-cell two-photon TCSPC FLIM.

2. Methods

2.1. Live-cell sample

LLC-PK1 live cells expressing mEmerald-EB3 and mCherry-H2B were kindly provided by Dr. Michael Davidson (Florida State University). The cells were cultured in Dulbecco’s minimal essential medium (DMEM) supplemented with 10% fetal bovine serum in humidified 37 °C incubator with 5% CO2. The cells were trypsinized and seeded in MetTek glass-bottom dishes with approximately 20% of confluence 12-24 hours before imaging.

2.2. TCSPC FLIM

Fluorescence lifetime images can be constructed by using raster-scanned time-correlated single photon counting (TCSPC) modules. With this technique, an entire exponential fluorescence decay curve can be constructed by a histogram of single photon collections at each pixel of the image (Fig. 1), and the lifetime value can be retrieved by curve fitting, such as nonlinear least squares fitting, of the data points constructing the detected decay curve.

In the two-photon TCSPC FLIM instrumentation, a Zeiss inverted LSM-510 laser scanning confocal system was used with two-photon excitation (ChameleonTM Vision, Coherent Inc.) and a Becker & Hickl TCSPC module (DCC software version 1.23 and SPCM software version 8.70).

The live-cell images were acquired with the following settings. A 100 x objective was used. mEmerald was excited at λex = 820 nm and the fluorescence was collected at λem = 500-550 nm. Data acquisition time was 100 seconds. The maximum total photon counts were about 2500.

2.3. Optimal “virtual” gating

TCSPC data can be “virtually gated” to form gated intensity images similar to those from time-gated FLIM. Given the values of the gate width, g, and the time interval between the starting points of two consecutive gates, dt (see Fig. 1), virtual gating of TCSPC data is a post-data-acquisition technique and is implemented by summing up the values of the data points within each virtual gate to form an intensity image. In this study, four virtual gates were used for its robustness [27

27. H. C. Gerritsen, M. A. H. Asselbergs, A. V. Agronskaia, and W. G. J. H. M. Van Sark, “Fluorescence lifetime imaging in scanning microscopes: acquisition speed, photon economy and lifetime resolution,” J. Microsc. (Oxford) 206(3), 218–224 (2002). [CrossRef]

], and a four-gate protocol was applied to determine the lifetime values by using Eq. (1) on a pixel-by-pixel basis [28

28. I. Bugiel, K. König, and H. Wabnitz, “Investigation of cell by fluorescence laser scanning microscopy with subnanosecond time resolution,” Lasers Life Sci. 3, 47–53 (1989).

30

30. K. K. Sharman, A. Periasamy, H. Ashworth, J. N. Demas, and N. H. Snow, “Error analysis of the rapid lifetime determination method for double-exponential decays and new windowing schemes,” Anal. Chem. 71(5), 947–952 (1999). [CrossRef] [PubMed]

]:
τp=N(ti2)(ti)2NtilnIi,p(ti)(lnIi,p),
(1)
where τp is the lifetime of pixel p, Ii,p is the intensity of pixel p in image i, ti is the gate delay of image i, and N is the number of images. All sums are over i.

The cellular morphology pattern of the conventional TCSPC lifetime map, shown in Fig. 4(a)
Fig. 4 The lifetime maps of live LLC-PK1 cells expressing mEmerald-EB3 and mCherry-H2B, with only mEmerald being imaged: (a) undenoised and (d) lifetime-denoised, with TCSPC lifetime mapping (curve-fitting of the original TCSPC data); (b) undenoised, (e) lifetime-denoised, and (g) intensity-denoised, with four-gate lifetime mapping after non-optimal virtual gating (dt = 0.4 ns; g = 8 ns); (c) undenoised, (f) lifetime-denoised, and (h) intensity-denoised, with four-gate lifetime mapping after optimal virtual gating (dt = 2 ns; g = 8 ns). Intensity denoising was not applicable to the original, non-gated, TCSPC data for improving lifetime precision. The labeled RSD values were obtained from all pixels with lifetime values greater than 2 ns to remove the variations from the background values. For better comparisons, “reject” (see section 2.3) was set to 100 for non-optimal virtual gating and was set to 15 for optimal virtual gating. Scale bar: 5 μm.
, was used as a reference for further parameter selection in lifetime mapping after virtual gating. Figure 4(a) was generated with “threshold” = 5 in conventional TCSPC lifetime mapping, meaning that the pixels with peak photon numbers lower than 5 would not be analyzed. The “reject” values, below which the intensities were set to zero in the virtually-gated images, were adjusted such that the morphology was approximately the same as in the reference image, Fig. 4(a). This was for better comparisons of the variations only in the morphology that we were interested in. Indeed, lowering the values of “reject” would change the apparent morphology and the relative standard deviation (RSD, defined as the standard deviation divided by the mean value, used as an indicator for the uncertainty of lifetime determination) values by bringing a certain amount of background to foreground. Changing the “threshold” value in TCSPC lifetime mapping had similar effects. Therefore, it was reasonable to adjust “reject” accordingly to match the selected “threshold” value.

RSDτ=120(dtτ)[1TC(1exp(gτ))(36+4exp(dtτ)+4exp(2dtτ)+36exp(3dtτ))]12,
(2)

There are some advantages of virtual gating of TCSPC data. First, it greatly accelerates the lifetime retrieving steps when used with subsequent closed-form lifetime determination methods such as rapid lifetime determination and the four-gate protocol mentioned above. Although virtual gating itself with closed-form lifetime solutions might not improve the precision of lifetime determination, it enables image processing, such as image denoising (section 2.4), for each virtual gate, and this allows further improvements of lifetime imaging.

2.4. Total variation denoising

2.4.1. Denoising algorithms

In this study, two novel total variation (TV) based image denoising models were used to improve the lifetime precision of live-cell two-photon TCSPC FLIM. They can remove Poisson noise and can also be easily adapted for any forms of noise introduced by imaging systems and image processing procedures. These forms of noise can be intensity-dependent, lifetime-dependent, or even spatially-dependent. Therefore, they can provide an accurate estimation of noise magnitude and have been demonstrated to produce no lifetime bias in their applications to FLIM [24

24. C. W. Chang, “Improving Accuracy and Precision in Biological Applications of Fluorescence Lifetime Imaging Microscopy,” Ph.D. thesis, University of Michigan (2009).

26

26. C. W. Chang and M. A. Mycek, “Increasing precision of lifetime determination in fluorescence lifetime imaging,” Proc. SPIE 7570, 757007 (2010). [CrossRef]

]. The first model we used was a general variance-weighted TV (VWTV) model:
E=Ω|u|dxdy+λΩ(fu)2Var(f)dxdy,
(3)
where Ω denotes the signal domain, Var(f) indicates the local variance of f, the given image (as a function of x and y), λ is the fidelity coefficient, the variables x and y represent the spatial location of the pixels, u denotes the processed image, and E denotes energy. The values of λ were determined by the “discrepancy rule” [31

31. T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vis. 27(3), 257–263 (2007). [CrossRef]

], which requires the fidelity term (the second term on the right hand side of Eq. (3)) evaluated with f and the final u to be the same as that evaluated with f and the estimated uncorrupted image [24

24. C. W. Chang, “Improving Accuracy and Precision in Biological Applications of Fluorescence Lifetime Imaging Microscopy,” Ph.D. thesis, University of Michigan (2009).

]. Denoising was implemented through the minimization of energy (E), during which the processed image (u) evolved to a stable state that should be close to the original image without noise corruption. For the specific application of denoising virtually-gated intensity images, a novel f-weighted TV (FWTV) model [24

24. C. W. Chang, “Improving Accuracy and Precision in Biological Applications of Fluorescence Lifetime Imaging Microscopy,” Ph.D. thesis, University of Michigan (2009).

] based on an f-weighted fidelity term was used. In this model, the Var(f) term in Eq. (3) was simply replaced by f, with the assumption that the intensity values followed Poisson distribution due to the single photon counting behavior in TCSPC.

2.4.2. Denoising procedures

Two procedures were used with TV denoising. In “lifetime denoising” (Fig. 2 (a)
Fig. 2 The precision of lifetime determination in TCSPC FLIM was improved by either (a) lifetime denoising, where the estimated variance of lifetime values was used in VWTV for denoising of lifetime maps, or (b) intensity denoising, where FWTV was used for denoising of each intensity image before four-gate lifetime mapping.
), a lifetime map was first constructed, either by four-gate lifetime mapping (for virtually-gated TCSPC FLIM) or by TCSPC lifetime mapping (for regular TCSPC FLIM), before applying denoising directly on lifetime maps. Since the variance of lifetime was not proportional to the lifetime values, VWTV was used. The variance estimation of virtually-gated TCSPC lifetime maps, as a function of τ, g, dt, and total photon counts, was performed by solving analytically the error propagation of the four-gate lifetime mapping formula (Eq. (1), also see Eq. (2)), while the variance estimation of TCSPC lifetime map, as a function of τ and total photon counts, was performed by direct sampling of the uniform regions of lifetime maps (see Fig. 3
Fig. 3 The Var(f) image (values in ns2) used in VWTV denoising (Eq. (3)) of the lifetime map of the live-cell sample (section 2.1) after TCSPC lifetime mapping (see Fig. 2 (a)). The variance was first assumed to be dependent on local τ and total photon counts (TC) values, and the variance value of each pixel was then determined to be 227.677 × τ 2 / (256 × TC0.72) from the regression analysis of three uniform regions sampled: 256 was the number of bins used in the histogram of single photon collections in TCSPC; the exponent of 0.72 was chosen to make the average predicted proportional constant 227.677 stay within the minimum error of 0.03% among all the predictions from the three uniform regions.
). In “intensity denoising” (Fig. 2 (b)), each virtually-gated intensity image was denoised before four-gate lifetime mapping. In this case, TV denoising was performed with FWTV.

3. Results and discussion

3.1. Overall lifetime precision improvement

Figure 4 demonstrates that, for TCSPC FLIM, both virtual gating and our novel TV denoising techniques can remove uncertainties in lifetime maps (the major non-uniformity in the lifetime map should arise from noise, since the fluorescence lifetime should be nearly uniform with the fluorophores in similar environments; even if there was any intrinsic lifetime distribution, it would be preserved due to the edge-preserving property of the TV denoising techniques). Overall, the precision was improved by greater than five-fold (RSD from 18.8% to 3.7%, Fig. 4(a) and (f)) in the lifetime map. The remaining RSD of 3.7% could be attributed to the intrinsic lifetime distribution instead of noise. The improvement from the intensity denoising (1% in this case, from Fig. 4(b) to (g) and from Fig. 4(c) to (h)) was independent of that from optimal virtual gating (2.1% in this case, from Fig. 4(b) to (c)).

3.2. Lifetime precision improvement by virtual gating

3.3. Lifetime precision improvement by total variation denoising

Before virtual gating was applied, there were no gated intensity images for denoising and subsequent four-gate lifetime construction. Therefore, only lifetime denoising was applied and this resulted in an RSD reduction from 18.8% to 8.9% (Fig. 4(a) and (d)).

For either non-optimally or optimally virtually-gated intensity maps, both lifetime denoising and intensity denoising can be applied to achieve even better precision. With a lower RSD to start with after optimal virtual gating, the denoising predictably achieved better precision (RSD = 3.7% and 5.9%, Fig. 4(f) and (h), for lifetime denoising and intensity denoising, respectively) compared to the non-optimal virtual gating (RSD = 4.3% and 8.0%, Fig. 4(e) and (g) for lifetime denoising and intensity denoising, respectively).

3.4. Lifetime denoising versus intensity denoising

4. Conclusion

In this study, we applied optimal virtual gating and total variation image denoising to live-cell two-photon TCSPC FLIM images to remove the uncertainties and improve the precision of lifetime determination by greater than five-fold (relative standard deviation, or RSD, from 18.8% to 3.7%; see Fig. 4(a) and (f)) in the lifetime maps. This approach is in principle applicable to single-photon TCSPC FLIM and time-gated FLIM, and allows low-light live-cell imaging with high precision and minimizes the adverse effects of excitation light on live cells. Therefore, our techniques can help avoid unnecessary high-intensity excitation of biological samples, possible sample damage / photobleaching, and unwanted detection of sample movement with long acquisition time. Improvements in FLIM precision can also influence other applications of fluorescence lifetime imaging, such as high content analysis and bioimage informatics.

Acknowledgments

This work was supported in part by a research grant from the National Institutes of Health (NIH)CA-114542. The authors thank Mr. Louis Kerr of the Central Microscopy Facility at the Marine Biological Laboratory and Dr. Anna Krzywicka-Racka of the University of Massachusetts Medical School for technical assistance, and Dr. Michael Davidson of Florida State University for providing access to living cells.

References and links

1.

S. Bloch, F. Lesage, L. McIntosh, A. Gandjbakhche, K. X. Liang, and S. Achilefu, “Whole-body fluorescence lifetime imaging of a tumor-targeted near-infrared molecular probe in mice,” J. Biomed. Opt. 10(5), 054003 (2005). [CrossRef] [PubMed]

2.

S. Pelet, M. J. R. Previte, D. Kim, K. H. Kim, T. T. J. Su, and P. T. C. So, “Frequency domain lifetime and spectral imaging microscopy,” Microsc. Res. Tech. 69(11), 861–874 (2006). [CrossRef] [PubMed]

3.

C. W. Chang, M. Wu, S. D. Merajver, and M. A. Mycek, “Physiological fluorescence lifetime imaging microscopy improves Förster resonance energy transfer detection in living cells,” J. Biomed. Opt. 14(6), 060502 (2009). [CrossRef]

4.

D. Sud and M. A. Mycek, “Calibration and validation of an optical sensor for intracellular oxygen measurements,” J. Biomed. Opt. 14(2), 020506 (2009). [CrossRef] [PubMed]

5.

D. Sud, W. Zhong, D. G. Beer, and M. A. Mycek, “Time-resolved optical imaging provides a molecular snapshot of altered metabolic function in living human cancer cell models,” Opt. Express 14(10), 4412–4426 (2006). [CrossRef] [PubMed]

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C. W. Chang, D. Sud, and M. A. Mycek, “Fluorescence lifetime imaging microscopy,” Methods Cell Biol. 81, 495–524 (2007). [CrossRef] [PubMed]

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J. Low, S. Huang, W. Blosser, M. Dowless, J. Burch, B. Neubauer, and L. Stancato, “High-content imaging characterization of cell cycle therapeutics through in vitro and in vivo subpopulation analysis,” Mol. Cancer Ther. 7(8), 2455–2463 (2008). [CrossRef] [PubMed]

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J. R. Swedlow, I. G. Goldberg, and K. W. EliceiriJ. R. SwedlowI. G. GoldbergK. W. EliceiriOME Consortium, “Bioimage informatics for experimental biology,” Annu. Rev. Biophys. 38(1), 327–346 (2009). [CrossRef] [PubMed]

12.

C. Vonesch, “Fast and automated wavelet-regularized image restoration in fluorescence microscopy,” Ph.D. thesis, École Polytechnique Fédérale De Lausanne (2009).

13.

C. Buranachai, D. Kamiyama, A. Chiba, B. D. Williams, and R. M. Clegg, “Rapid frequency-domain FLIM spinning disk confocal microscope: lifetime resolution, image improvement and wavelet analysis,” J. Fluoresc. 18(5), 929–942 (2008). [CrossRef] [PubMed]

14.

B. Q. Spring and R. M. Clegg, “Image analysis for denoising full-field frequency-domain fluorescence lifetime images,” J. Microsc. (Oxford) 235(2), 221–237 (2009). [CrossRef]

15.

J. Boulanger, J. B. Sibarita, C. Kervrann, and P. Bouthemy, “Non-parametric regression for patch-based fluorescence microscopy image sequence denoising,” 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols 1–4, 748–751 (2008).

16.

S. Delpretti, F. Luisier, S. Ramani, T. Blu, and M. Unser, “Multiframe SURE-LET denoising of timelapse fluorescence microscopy images,” 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols 1–4, 149–152 (2008).

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24.

C. W. Chang, “Improving Accuracy and Precision in Biological Applications of Fluorescence Lifetime Imaging Microscopy,” Ph.D. thesis, University of Michigan (2009).

25.

C. W. Chang and M. A. Mycek, “Improving precision in time-gated FLIM for low-light live-cell imaging,” Proc. SPIE 7370, 7370091–7370096 (2009).

26.

C. W. Chang and M. A. Mycek, “Increasing precision of lifetime determination in fluorescence lifetime imaging,” Proc. SPIE 7570, 757007 (2010). [CrossRef]

27.

H. C. Gerritsen, M. A. H. Asselbergs, A. V. Agronskaia, and W. G. J. H. M. Van Sark, “Fluorescence lifetime imaging in scanning microscopes: acquisition speed, photon economy and lifetime resolution,” J. Microsc. (Oxford) 206(3), 218–224 (2002). [CrossRef]

28.

I. Bugiel, K. König, and H. Wabnitz, “Investigation of cell by fluorescence laser scanning microscopy with subnanosecond time resolution,” Lasers Life Sci. 3, 47–53 (1989).

29.

X. F. Wang, T. Uchida, D. M. Coleman, and S. Minami, “A two-dimensional fluorescence lifetime imaging system using a gated image intensifier,” Appl. Spectrosc. 45(3), 360–366 (1991). [CrossRef]

30.

K. K. Sharman, A. Periasamy, H. Ashworth, J. N. Demas, and N. H. Snow, “Error analysis of the rapid lifetime determination method for double-exponential decays and new windowing schemes,” Anal. Chem. 71(5), 947–952 (1999). [CrossRef] [PubMed]

31.

T. Le, R. Chartrand, and T. J. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vis. 27(3), 257–263 (2007). [CrossRef]

OCIS Codes
(100.2000) Image processing : Digital image processing
(170.1530) Medical optics and biotechnology : Cell analysis
(170.2520) Medical optics and biotechnology : Fluorescence microscopy
(170.6920) Medical optics and biotechnology : Time-resolved imaging

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: February 19, 2010
Revised Manuscript: April 3, 2010
Manuscript Accepted: April 6, 2010
Published: April 9, 2010

Virtual Issues
Vol. 5, Iss. 8 Virtual Journal for Biomedical Optics

Citation
Ching-Wei Chang and Mary-Ann Mycek, "Precise fluorophore lifetime mapping in live-cell, multi-photon excitation microscopy," Opt. Express 18, 8688-8696 (2010)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-8-8688


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References

  1. S. Bloch, F. Lesage, L. McIntosh, A. Gandjbakhche, K. X. Liang, and S. Achilefu, “Whole-body fluorescence lifetime imaging of a tumor-targeted near-infrared molecular probe in mice,” J. Biomed. Opt. 10(5), 054003 (2005). [CrossRef] [PubMed]
  2. S. Pelet, M. J. R. Previte, D. Kim, K. H. Kim, T. T. J. Su, and P. T. C. So, “Frequency domain lifetime and spectral imaging microscopy,” Microsc. Res. Tech. 69(11), 861–874 (2006). [CrossRef] [PubMed]
  3. C. W. Chang, M. Wu, S. D. Merajver, and M. A. Mycek, “Physiological fluorescence lifetime imaging microscopy improves Förster resonance energy transfer detection in living cells,” J. Biomed. Opt. 14(6), 060502 (2009). [CrossRef]
  4. D. Sud and M. A. Mycek, “Calibration and validation of an optical sensor for intracellular oxygen measurements,” J. Biomed. Opt. 14(2), 020506 (2009). [CrossRef] [PubMed]
  5. D. Sud, W. Zhong, D. G. Beer, and M. A. Mycek, “Time-resolved optical imaging provides a molecular snapshot of altered metabolic function in living human cancer cell models,” Opt. Express 14(10), 4412–4426 (2006). [CrossRef] [PubMed]
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