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      Segmented cell analyses to measure redox states of autofluorescent NAD(P)H, FAD & Trp in cancer cells by FLIM

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          Abstract

          Multiphoton FLIM microscopy offers many opportunities to investigate processes in live cells, tissue and animal model systems. For redox measurements, FLIM data is mostly published by cell mean values and intensity-based redox ratios. Our method is based entirely on FLIM parameters generated by 3-detector time domain microscopy capturing autofluorescent signals of NAD(P)H, FAD and novel FLIM-FRET application of Tryptophan and NAD(P)H-a2%/FAD-a1% redox ratio. Furthermore, image data is analyzed in segmented cells thresholded by 2 × 2 pixel Regions of Interest (ROIs) to separate mitochondrial oxidative phosphorylation from cytosolic glycolysis in a prostate cancer cell line. Hundreds of data points allow demonstration of heterogeneity in response to intervention, identity of cell responders to treatment, creating thereby different sub-populations. Histograms and bar charts visualize differences between cells, analyzing whole cell versus mitochondrial morphology data, all based on discrete ROIs. This assay method allows to detect subtle differences in cellular and tissue responses, suggesting an advancement over means-based analyses.

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          On the origin of cancer cells.

          O WARBURG (1956)
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            Imaging protein molecules using FRET and FLIM microscopy.

            Förster (or fluorescence) resonance energy transfer (FRET) and fluorescence lifetime imaging (FLIM) have moved center stage and are increasingly forming part of multifaceted imaging approaches. They are complementary methodologies that can be applied to advanced quantitative analyses. The widening application of FRET and FLIM has been driven by the availability of suitable fluorophores, increasingly sophisticated microscopy systems, methodologies to correct spectral bleed-through, and the ease with which FRET can be combined with other techniques. FRET and FLIM have recently found use in several applications: in the analysis of protein-protein interactions with high spatial and temporal specificity (e.g. clustering), in the study of conformational changes, in the analysis of binding sequences, and in applications such as high-throughput screening.
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              Phasor approach to fluorescence lifetime microscopy distinguishes different metabolic states of germ cells in a live tissue.

              We describe a label-free imaging method to monitor stem-cell metabolism that discriminates different states of stem cells as they differentiate in living tissues. In this method we use intrinsic fluorescence biomarkers and the phasor approach to fluorescence lifetime imaging microscopy in conjunction with image segmentation, which we use to introduce the concept of the cell phasor. In live tissues we are able to identify intrinsic fluorophores, such as collagen, retinol, retinoic acid, porphyrin, flavins, and free and bound NADH. We have exploited the cell phasor approach to detect a trend in metabolite concentrations along the main axis of the Caenorhabditis elegans germ line. This trend is consistent with known changes in metabolic states during differentiation. The cell phasor approach to lifetime imaging provides a label-free, fit-free, and sensitive method to identify different metabolic states of cells during differentiation, to sense small changes in the redox state of cells, and may identify symmetric and asymmetric divisions and predict cell fate. Our method is a promising noninvasive optical tool for monitoring metabolic pathways during differentiation or disease progression, and for cell sorting in unlabeled tissues.
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                Author and article information

                Contributors
                ap3t@virginia.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 January 2018
                8 January 2018
                2018
                : 8
                : 79
                Affiliations
                [1 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, The W.M. Keck Center for Cellular Imaging, , University of Virginia, ; Charlottesville, VA USA
                [2 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, Departments of Biology, , University of Virginia, ; Charlottesville, VA USA
                [3 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, Biomedical Engineering, , University of Virginia, ; Charlottesville, VA USA
                [4 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, Advanced Research Computing Services, , University of Virginia, ; Charlottesville, VA USA
                [5 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, Microbiology, Immunology & Cancer Biology, , University of Virginia, ; Charlottesville, VA USA
                Author information
                http://orcid.org/0000-0001-8007-0612
                Article
                18634
                10.1038/s41598-017-18634-x
                5758727
                29311591
                0076e09b-37af-494e-bfc7-ad1ca54e3c75
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 September 2017
                : 13 December 2017
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