A region of interest (ROI) based quantitative FLIM-FRET image analysis is developed to
quantitate the autofluorescence signals of the essential amino acid tryptophan as a biomarker to
investigate the metabolism in prostate cancer cells.
NADH and FAD are important endogenous fluorescent coenzymes participating in key enzymatic reactions of cellular metabolism. While fluorescence intensities of NADH and FAD have been used to determine the redox state of cells and tissues, this simple approach breaks down in the case of deep-tissue intravital imaging due to depth- and wavelength-dependent light absorption and scattering. To circumvent this limitation, our research focuses on fluorescence lifetimes of two-photon excited NADH and FAD emission to study the metabolic state of live tissues. In our custom-built scanning microscope we combine tunable femtosecond Ti:sapphire laser (operating at 740 nm for NADH excitation and 890 nm for FAD excitation), two GaAsP hybrid detectors for registering individual fluorescence photons and two Becker and Hickl time correlator boards for high precision lifetime measurements. Together with our rigorous FLIM analysis approach (including image segmentation, multi-exponential decay fitting and detailed statistical analysis) we are able to detect metabolic changes in cancer xenografts (human pancreatic cancer MPanc96 cells injected subcutaneously into the ear of an immunodeficient nude mouse), relative to surrounding healthy tissue. Advantageously, with the same instrumentation we can also take high-resolution and high-contrast images of second harmonic signal (SHG) originating from collagen fibers of both the healthy skin and the growing tumor. The combination of metabolic measurements (NADH and FAD lifetime) and morphological information (collagen SHG) allows us to follow the tumor growth in live mouse model and the changes in tumor microenvironment.
Fluorescence Lifetime Imaging Microscopy (FLIM) can be used to identify changes in metabolic activity during cancer progression and upon anti-cancer drug treatment. Prostate cancer (PCa) is one of the leading cancers in men in the USA. This research focusses on understanding the lifetime changes of endogenous biomarkers: NAD(P)H, FAD and Trp in LNCaP cells upon treatment with doxorubicin using our 3-channel FLIM approach. The LNCaP cells were treated with doxorubicin for 24hr. Images using FLIM of LNCaP control and treated cells were acquired on Zeiss 780 multiphoton confocal microscope coupled with B and H TCSPC FLIM board. After FLIM data fitting and processing we observed increase in the mean fluorescence lifetime of Trp, NAD(P)H and FAD with doxorubicin treatment. Additionally, we saw reduction in the NAD(P)H/FAD redox ratio with doxorubicin treatment. Our results identify the changes in the lifetime of these endogenous biomarkers and in the cellular redox state as a metabolic response with doxorubicin treatment in prostate cancer cells.
Fluorescence lifetime imaging microscopy (FLIM) is one of the most sensitive techniques to measure metabolic activity in living cells, tissues and whole animals. We used two- and three-photon fluorescence excitation together with time-correlated single photon counting (TCSPC) to acquire FLIM signals from normal and prostate cancer cell lines. FLIM requires complex data fitting and analysis; we explored different ways to analyze the data to match diverse cellular morphologies. After non-linear least square fitting of the multi-photon TCSPC images by the SPCImage software (Becker & Hickl), all image data are exported and further processed in ImageJ. Photon images provide morphological, NAD(P)H signal-based autofluorescent features, for which regions of interest (ROIs) are created. Applying these ROIs to all image data parameters with a custom ImageJ macro, generates a discrete, ROI specific database. A custom Excel (Microsoft) macro further analyzes the data with charts and statistics. Applying this highly automated assay we compared normal and cancer prostate cell lines with respect to their glycolytic activity by analyzing the NAD(P)H-bound fraction (a2%), NADPH/NADH ratio and efficiency of energy transfer (E%) for Tryptophan (Trp). Our results show that this assay is able to differentiate the effects of glucose stimulation and Doxorubicin in these prostate cell lines by tracking the changes in a2% of NAD(P)H, NADPH/NADH ratio and the changes in Trp E%. The ability to isolate a large, ROI-based data set, reflecting the heterogeneous cellular environment and highlighting even subtle changes — rather than whole cell averages - makes this assay particularly valuable.
Fluorescence Lifetime Imaging (FLIM) can be used to understand the metabolic activity in cancer. Prostate cancer is one of the leading cancers in men in the USA. This research focuses on FLIM measurements of NAD(P)H and Tryptophan, used as biomarkers to understand the metabolic activity in prostate cancer cells. Two prostate cancers and one normal cell line were used for live-cell FLIM measurements on Zeiss780 2P confocal microscope with SPCM FLIM board. Glucose uptake and glycolysis proceeds about ten times faster in cancer than in non-cancerous tissues. Therefore, we assessed the glycolytic activity in the prostate cancer in comparison to the normal cells upon glucose stimulation by analyzing the NAD(P)H and Trp lifetime distribution and efficiency of energy transfer (E%). Furthermore, we treated the prostate cancer cells with 1μM Doxorubicin, a commonly used anti-cancer chemotherapeutic. Increase in NADH a2%, an indicator of increased glycolysis and increased E% between Trp and NAD(P)H were seen upon glucose stimulation for 30min. The magnitude of shift to the right for NAD(P)H a2% and E% distribution was higher in prostate cancer versus the normal cells. Upon treatment with Doxorubicin decrease in cellular metabolism was seen at 15 and 30 minutes. The histogram for NAD(P)H a2% post-treatment for prostate cancer cells showed a left shift compared to the untreated control suggesting decrease in glycolysis and metabolic activity opposite to what was observed after glucose stimulation. Hence, NAD(P)H and Trp lifetimes can be used biomarkers to understand metabolic activity in prostate cancer and upon chemotherapeutic interventions.
KEYWORDS: Fluorescence resonance energy transfer, Microscopy, Energy efficiency, Image processing, Energy transfer, Venus, Electroluminescence, Imaging systems, Image analysis, Data processing
Average lifetime between the usually bi-exponential double-label specimen and a mono-exponential single donor sample serves as a basis for the calculation of the average energy transfer efficiency (E). This semi-quantitative approach however does not fully explore cellular functions, such as endosomal pH differences, specific morphological features, examining sub-populations and the like. We applied a different, quantitative Region-of-Interest (ROI)-based method in 2 live-cell assays by TCSPC FLIM-FRET microscopy: a 5 amino-acid linked FRET standard and mouse pituitary cells expressing a dimerized C/EBPα-bZip transcription factor in the nucleus, both tagged with Cerulean (C) and Venus (V). ROIs with different selection thresholds were generated and compared. Average lifetimes are similar, but ratios between them and other subtle differences are revealed by comprehensive distribution information. Following published references, we also explored 3 different methods to calculate FLIM-FRET energy transfer efficiencies for the Cerulean- Venus constructs, producing differences and supporting the long-held notion that E is called 'apparent' efficiency. FRET's greatest contribution continues to be exploring changes taking place at the cellular level and quantifying differences in relative terms between control and variables.
Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise Ratio (SNR), Signal to Background Ratio (SBR), circular average of the power spectral density and the S3 sharpness index. The results show that SR-SIM and Bayesian estimation combine illumination patterned images more effectively and provide better lateral resolution in exchange for more complex image processing. SR-SIM requires one to precisely shift the separated spectral components to their proper positions in reciprocal space. High noise levels in the raw data can cause inaccuracies in the shifts of the spectral components which degrade the super-resolved image. Bayesian estimation has proven to be more robust to changes in noise level and illumination pattern frequency.
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