Changing the data representation from the classical time delay histogram to the phasor representation provides a global view of the fluorescence decay at each pixel of an image. In the phasor representation we can easily recognize the presence of different molecular species in a pixel or the occurrence of fluorescence resonance energy transfer. The analysis of the fluorescence lifetime imaging microscopy (FLIM) data in the phasor space is done observing clustering of pixels values in specific regions of the phasor plot rather than by fitting the fluorescence decay using exponentials. The analysis is instantaneous since is not based on calculations or nonlinear fitting. The phasor approach has the potential to simplify the way data are analyzed in FLIM, paving the way for the analysis of large data sets and, in general, making the FLIM technique accessible to the nonexpert in spectroscopy and data analysis.

We describe a technique based on moment-analysis for the measurement of the average number of molecules and brightness in each pixel in fluorescence microscopy images. The average brightness of the particle is obtained from the ratio of the variance to the average intensity at each pixel. To obtain the average number of fluctuating particles, we divide the average intensity at one pixel by the brightness. This analysis can be used in a wide range of concentrations. In cells, the intensity at any given pixel may be due to bright immobile structures, dim fast diffusing particles, and to autofluorescence or scattering. The total variance is given by the variance of each of the above components in addition to the variance due to detector noise. Assuming that all sources of variance are independent, the total variance is the sum of the variances of the individual components. The variance due to the particles fluctuating in the observation volume is proportional to the square of the particle brightness while the variance of the immobile fraction, the autofluorescence, scattering, and that of the detector is proportional to the intensity of these components. Only the fluctuations that depend on the square of the brightness (the mobile particles) will have a ratio of the variance to the intensity >1. Furthermore, changing the fluorescence intensity by increasing the illumination power, distinguishes between these possible contributions. We show maps of molecular brightness and number of cell migration proteins obtained using a two-photon scanning microscope operating with a photon-counting detector. These brightness maps reveal binding dynamics at the focal adhesions with pixel resolution and provide a picture of the binding and unbinding process in which dim molecules attach to the adhesions or large molecular aggregates dissociate from adhesion.

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.phasor analysis | single cell metabolism T he hallmark of stem cells is their ability to produce a new stem cell by self-renewing, as they generate daughter cells that are committed to differentiation and form specialized tissues (1). The modulation of the balance between self-renewing divisions and differentiation is a central mechanism for stem cells during embryo development, adult tissue regeneration, and homeostasis. Stem-cell differentiation is a complex process mediated both by intrinsic molecular mechanisms and extrinsic signaling. Intrinsic cell polarity, subcellular localization mechanism, asymmetric centrosome and spindles, as well as cell-cycle regulators can establish self-renewing asymmetry during stem-cell division (2, 3). The influence of external chemical and physical stimuli, such as molecular gradients, extracellular matrix remodeling, and niche signaling is crucial for stem-cell plasticity and tissue development (4-6). Extrinsic signals can be propagated through intracellular signal-transduction pathways that converge to genetic networks that control pluripotency. Many different signaling and transcriptional pathways, which are important for development and cell-cycle regulation, converge on regulation of the redox state of stem cells (5,7,8). In turn, histone posttranscriptional modifications and epigenetic mechanisms, such as phosphorylation, acetylation, and methylation, sense cellular metabolism and the variation of metabolites levels (9). Hence, redox balance plays an important role in the maintenance and modulation of stem cell self-renewal and differentiation (10-12).In stem-cell research there is a high demand for noninvasive techniques to investigate self-renewal and differentiation. Methods, such as immunohistochemistry, metabolic assays, and PCR are currently...