QTu3G.3.pdf
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High-Throughput Quantitative Fluorescence Lifetime Imaging based on Active Wide-Field Illumination Lingling Zhao,1 Ken Abe,2 Margarida Barroso,2 and Xavier Intes1, 1
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA Center for Cardiovascular Sciences, Albany Medical College, 43 New Scotland Avenue, Albany, New York, 12208, USA Author e-mail address:
[email protected]
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Abstract: We developed an active illumination strategy to acquire optimal fluorescence signals over multi-well plates even in the presence of large fluorophore concentration distributions. We validated the stability of our approach in a 96-well plate setting with fluorophore concentrations ranging >2 orders of magnitude. We report the ability of our method to quantify accurately the lifetime over this concentration range in all wells based on optimized wide-field data and in less than a minute. Our results demonstrate that active wide-field illumination can improve the signalto-noise ratio and weak-signal sensitivity for enhanced accuracy of fluorescence decay curve fitting and lifetime estimation at high acquisition speed. OCIS codes: (170.6920) Time-resolved imaging; (170.3650) Lifetime-based sensing; (170.2945) Illumination design
1. Introduction Time-resolved fluorescence imaging microscopy (TR-FIM) is a quantitative method to investigate the microenvironment of fluorophores in living cells, since fluorescence lifetime is sensitive to biochemical and physical factors in immediate environment of probe, such as temperature, pH, calcium-ion concentration, oxygen, refractive index, polarity and viscosity [1, 2]. TR-FIM has been widely used to measure the cellular molecular environment [4], metabolic activity by autofluorescence properties [3] and intracellular protein-protein interactions via Förster resonance energy transfer (FRET) [5]. However, the large dynamical range of fluorescence emission collected can limit the accuracy of lifetime estimation. If the fluorescence emission photon count distribution is out of the dynamical range of the detection system, the strongest signals are saturated or the weakest signals are lost, and the signal - to - noise ratio (SNR) is reduced as well. These factors unfavorably impact the accuracy of fluorescence lifetime model-based estimate which is depending on the photon counts at peak of the curve. To overcome this challenge, we implemented an active wide-field illumination (AWFI) method that adjusts not only the overall laser power but also the spatial distribution of the wide-field illumination. This method aims at mitigating the range of emitted fluorescence intensity to improve the accuracy of fluorescence lifetime estimate thanks to high SNR. This is achieved by balancing the fluorescence signals at the maximum of fluorescence decay curve over the illumination area till reaching the photon counts limit of the detector. This method not only improves the SNR and weak-signal sensitivity for fluorescence lifetime analysis, but also reduces the acquisition time and photobleaching. 2. Methods We implemented our AWFI method in a time-domain fluorescence lifetime imaging system based on wide-field time-resolved illumination and gated intensified CCD (ICCD) detection, system shown in Fig.1 (a) described in details in [6] and employed for pre-clinical fluorescence studies [7-9]. Briefly, a tunable Ti-Sapphire laser (Mai Tai HP, Spectra-Physics, CA, USA) is used as an illumination source to generate 100fs pulses at 80MHz in the near infrared (NIR) spectral band (690nm-1020nm). The overall laser power is adjustable using a computer-controlled power module (Application Note 30, Newport, CA, USA). A pico-projector (PK101, Optoma USA) with digital light processing technology is used to project the illumination pattern to the imaging stage over ~4cm × 3 cm (up to 8cm x 4cm) with a resolution of 800x600 pixels and 256 grayscale levels. The transmitted light is detected by the ICCD (PicoStar HR, LaVision GmbH, Germany) allowing the measurement of a maximum of 2 12 photons with spatial resolution of 1376 × 1040 pixels. The transmitted fluorescence signal from the sample is spectrally filtered by an emission bandpass filter (FF01720/13-25 for 695nm, FF01-832/37-25 for 780nm; Semrock) and detected by the ICCD. The AWFI iterative procedure consists in optimizing the grayscale levels of the wide-field illumination to change the local laser power distribution illuminating the sample for optimal fluorescence signals acquisition. The pattern is optimized iteratively until the photon counts of the fluorescence signal reach the desired photon counts set by user at any position over the full sample. )). Piecewise polynomial interpolation is used to correct detector readout to be proportional to the input gray levels (See Fig. 1 (b) to (d)).
Imaging and Applied Optics © OSA 2013
Fig. 1. (a) Hardware schematics of AWFI procedure. (b) Grayscale gradient pattern. (c) Grayscale gradient pattern detected (d) Corrected pattern detected. (e) Comparison of laser power (LP) versus photon counts (PC); intensity profiles of detected detector readout (DDR) and corrected detector readout (CDR).
3. Results and Discussion To establish the robustness and high convergence rate of the iterative optimization algorithm, three initial patterns with different spatial intensity distributions were used and optimized towards homogenous field detection (uniform, gradient, and random initial patterns shown in Fig. 2). These three patterns were projected on a piece of diffuse paper laid on the imaging stage. The temporal measurements at excitation wavelength (λex=690nm) were obtained over a range of 800ps at 40ps interval using 300ps gate-width (MCP voltage=420V; integration time =50ms). After applying our AWFI, the three optimized patterns demonstrated identical spatial distribution. The laser power and mean photon counts versus different iteration numbers is shown in Fig. 3. These parameters converged after 4 iterations for the uniform pattern, 6 iterations for the gradient pattern and 7 iterations for the random pattern.
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AWFI was applied to fluorescence imaging in multi-well settings. Cardiogreen dye (I2633, Sigma-Aldrich) was solved in 50% ethanol at different concentrations, which were 5nM, 15nM, 45nM, 125nM, 350nM, and 1000nM. Fluorescence signals were acquired using a band-pass filter (FF01-832/37-25, Semrock) over a range of 2.8ns at 40ps interval using 300ps gate-width (MCP voltage=590V; integration time =400ms). The optimized pattern to capture optimal fluorescence signal over the sample was obtained after 4 iterations. The wells with 5nM, 15nM and 45nM were not resolved when using non-optimized pattern (homogenous widefield illumination) as shown in the original image (Fig. 4(a)). After AWFI, all the wells in the FOV were resolved with photon counts up to 3600 as shown in Fig. 4(b). Lifetime estimation was performed on each individual well
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before and after optimization. As expected, fluorescence decay curve fitting was not accurate when the fluorescence signals were too weak (such as in 5nM to 45nM wells prior to optimization), but the lifetimes were in excellent agreement with the ground truth after optimization as shown in Fig. 4(c). The ground truth lifetime was obtained by optimizing and acquiring fluorescence signal on each individual well, similarly to dynamic raster scanning techniques.
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Fig. 4. Fluorescence intensity image at the maximum of decay curve without optimization procedure (a) and with optimization procedure (b). (b) Comparison of average lifetime form (a) and (b) versus different concentrations marked in yellow in (a).
Next, we applied the approach to a cell-based FRET assay. In lifetime FRET assay, the fluorescence brightness is determined by the concentration of the donor and quenching. In fixed cell assays, the concentration of the donor undergoing FRET is proportional to the number of receptors/ligand interactions (intramolecular FRET). Here, we applied AWFI to visualize a transferrin labeled NIR FRET pair (Alexa Fluor 700- Alexa Fluor) in Madin-Darby canine kidney (MDCK) cells and human mammary epithelial (HME) cells with different donor-acceptor (AD) ratios in vitro. Quenched donor fraction was estimated using bi-exponential fitting model. A consistent linear relationship can be obtained with optimization for different cell type and concentrations over a broad range of AD ratios after active illumination. The overall time of acquisition and optimization was 1 minute. 4. Conclusion We have developed a new approach to perform accurate wide-field fluorescence imaging over heterogeneous sample with accuracy. We applied this new approach, AWFI, to lifetime sensing and validated the method stability experimentally. We have demonstrated that AWFI can improve the accuracy of fluorescence decay curve fitting in multi-well plate configuration with fluorophore concentrations ranging >2 orders of magnitude. Since AWFI is able to obtain homogenous fluorescence intensity distribution, it can be applied to both time domain measurement and continues wave measurement. AWFI have the potential to be implemented in automated TRFIM multi-well plate reader for high content analysis and high-throughput screening by providing high acquisition speed, high SNR, high precision of fluorescence lifetime fitting. We expect also this approach to be benefit for in vivo FLIM applications by overcoming the dynamical range associated with heterogeneous background [10] and for lifetime-based FMT in which lifetime is an a priori information [11]. 5. Acknowledgements This work was partly funded by the National Institutes of Health grant R21 CA161782-01 and National Science Foundation CAREER AWARD CBET-1149407. 6. References [1] W. Becker, "Fluorescence lifetime imaging--techniques and applications," J Microsc 247, 119-136 (2012). [2] J. W. Borst and A. J. W. G. Visser, "Fluorescence lifetime imaging microscopy in life sciences," Meas Sci Technol 21, 102002 (2010). [3] L. L. Zhao, D. N. Chen, J. Qi, and J. L. Qu, "Multispectral autofluorescence lifetime imaging of RPE cells using two-photon excitation," Proc SPIE 7569, 75692B (2010). [4] A. Pliss, L. L. Zhao, T. Y. Ohulchanskyy, J. L. Qu, and P. N. Prasad, "Fluorescence Lifetime of Fluorescent Proteins as an Intracellular Environment Probe Sensing the Cell Cycle Progression," Acs Chem Biol 7, 1385-1392 (2012). [5] V. Venugopal, J. Chen, M. Barroso, and X. Intes, "Quantitative tomographic imaging of intermolecular FRET in small animals," Biomed Opt Express 3, 3161-3175 (2012). [6] V. Venugopal, J. Chen, and X. Intes, "Development of an optical imaging platform for functional imaging of small animals using wide-field excitation," Biomedical Optics Express 1, 143-156 (2010). [7] V Venugopal, J Chen, F Lesage and X Intes, “Full-field time-resolved fluorescence tomography of small animals,” Optics Letters 35, 31893191 (2010). [8] J Chen and X Intes, “Time-gated perturbation Monte Carlo for whole body functional imaging in small animals,” Optics Express 17, 19566– 19579 (2009). [9] J Chen, V Venugopal and X Intes, “A Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomedical Optics Express 2, 871-886 (2011) [10] V Venugopal and X Intes, “Adaptive full-field imaging for fluorescence optical tomography,” Journal of Biomedical Optics, in press (2013). [11] J Chen and X Intes, “Comparison of Monte Carlo Methods for Fluorescence Molecular Tomography - Computational Efficiency,” Medical Physics 38, 5788-5798 (2011).
