Photonic mode density effects on single-molecule fluorescence blinking

We investigated the influence of the photonic mode density (PMD) on the triplet dynamics of individual chromophores on a dielectric interface by comparing their response in the presence and absence of a nearby gold film. Lifetimes of the excited singlet state were evaluated in ordet to measure directly the PMD at the molecules position. Triplet state lifetimes were simultaneously determined by statistical analysis of the detection time of the fluorescence photons. The observed singlet decay rates are in agreement with the predicted PMD for molecules with different orientations. The triplet decay rate is modified in a fashion correlated to the singlet decay rate. These results show that PMD engineering can lead to an important suppression of the fluorescence, introducing a novel aspect of the physical mechanism to enhance fluorescence intensity in PMD-enhancing systems such as plasmonic devices

Nanoscale localization of proteins within focal adhesions indicates discrete functional assemblies with selective force‐dependence

Focal adhesions (FAs) are subcellular regions at the micrometer scale that link the cell to the surrounding microenvironment and control vital cell functions. However, the spatial architecture of FAs remains unclear at the nanometer scale. We used two‐color and three‐color super‐resolution stimulated emission depletion microscopy to determine the spatial distributions and co‐localization of endogenous FA components in fibroblasts. Our data indicate that adhesion proteins inside, but not outside, FAs are organized into nanometer size units of multi‐protein assemblies. The loss of contractile force reduced the nanoscale co‐localization between different types of proteins, while it increased this co‐localization between markers of the same type. This suggests that actomyosin‐dependent force exerts a nonrandom, specific, control of the localization of adhesion proteins within cell–matrix adhesions. These observations are consistent with the possibility that proteins in cell–matrix adhesions are assembled in nanoscale particles, and that force regulates the localization of the proteins therein in a protein‐specific manner. This detailed knowledge of how the organization of FA components at the nanometer scale is linked to the capacity of the cells to generate contractile forces expands our understanding of cell adhesion in health and disease

Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo

Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms.National Science Foundation (U.S.). Physics of Living Systems ProgramNational Institute of Mental Health (U.S.) (Award U01MH106011

Bright ligand-activatable fluorescent protein for high-quality multicolor live-cell super-resolution microscopy

We introduce UnaG as a green-to-dark photoswitching fluorescent protein capable of high-quality super-resolution imaging with photon numbers equivalent to the brightest photoswitchable red protein. UnaG only fluoresces upon binding of a fluorogenic metabolite, bilirubin, enabling UV-free reversible photoswitching with easily controllable kinetics and low background under Epi illumination. The on- and off-switching rates are controlled by the concentration of the ligand and the excitation light intensity, respectively, where the dissolved oxygen also promotes the off-switching. The photo-oxidation reaction mechanism of bilirubin in UnaG suggests that the lack of ligand-protein covalent bond allows the oxidized ligand to detach from the protein, emptying the binding cavity for rebinding to a fresh ligand molecule. We demonstrate super-resolution single-molecule localization imaging of various subcellular structures genetically encoded with UnaG, which enables facile labeling and simultaneous multicolor imaging of live cells. UnaG has the promise of becoming a default protein for high-performance super-resolution imaging. Photoconvertible proteins occupy two color channels thereby limiting multicolour localisation microscopy applications. Here the authors present UnaG, a new green-to-dark photoswitching fluorescent protein for super-resolution imaging, whose activation is based on a noncovalent binding with bilirubin

Catalytic Conversion of Lipophilic Substrates by Phase constrained Enzymes in the Aqueous or in the Membrane Phase

Both soluble and membrane-bound enzymes can catalyze the conversion of lipophilic substrates. The precise substrate access path, with regard to phase, has however, until now relied on conjecture from enzyme structural data only (certainly giving credible and valuable hypotheses). Alternative methods have been missing. To obtain the first experimental evidence directly determining the access paths (of lipophilic substrates) to phase constrained enzymes we here describe the application of a BODIPY-derived substrate (PS1). Using this tool, which is not accessible to cytosolic enzymes in the presence of detergent and, by contrast, not accessible to membrane embedded enzymes in the absence of detergent, we demonstrate that cytosolic and microsomal glutathione transferases (GSTs), both catalyzing the activation of PS1, do so only within their respective phases. This approach can serve as a guideline to experimentally validate substrate access paths, a fundamental property of phase restricted enzymes. Examples of other enzyme classes with members in both phases are xenobiotic-metabolizing sulphotransferases/UDP-glucuronosyl transferases or epoxide hydrolases. Since specific GSTs have been suggested to contribute to tumor drug resistance, PS1 can also be utilized as a tool to discriminate between phase constrained members of these enzymes by analyzing samples in the absence and presence of Triton X-100

Sequence-Dependent Fluorescence of Cyanine Dyes on Microarrays

Cy3 and Cy5 are among the most commonly used oligonucleotide labeling molecules. Studies of nucleic acid structure and dynamics use these dyes, and they are ubiquitous in microarray experiments. They are sensitive to their environment and have higher quantum yield when bound to DNA. The fluorescent intensity of terminal cyanine dyes is also known to be significantly dependent on the base sequence of the oligonucleotide. We have developed a very precise and high-throughput method to evaluate the sequence dependence of oligonucleotide labeling dyes using microarrays and have applied the method to Cy3 and Cy5. We used light-directed in-situ synthesis of terminally-labeled microarrays to determine the fluorescence intensity of each dye on all 1024 possible 5′-labeled 5-mers. Their intensity is sensitive to all five bases. Their fluorescence is higher with 5′ guanines, and adenines in subsequent positions. Cytosine suppresses fluorescence. Intensity falls by half over the range of all 5-mers for Cy3, and two-thirds for Cy5. Labeling with 5′-biotin-streptavidin-Cy3/-Cy5 gives a completely different sequence dependence and greatly reduces fluorescence compared with direct terminal labeling

SATB1, genomic instability and Gleason grading constitute a novel risk score for prostate cancer

Current prostate cancer risk classifications rely on clinicopathological parameters resulting in uncertainties for prognostication. To improve individual risk stratification, we examined the predictive value of selected proteins with respect to tumor heterogeneity and genomic instability. We assessed the degree of genomic instability in 50 radical prostatectomy specimens by DNA-Image-Cytometry and evaluated protein expression in related 199 tissue-microarray (TMA) cores. Immunohistochemical data of SATB1, SPIN1, TPM4, VIME and TBB5 were correlated with the degree of genomic instability, established clinical risk factors and overall survival. Genomic instability was associated with a GS >= 7 (p = 0.001) and worse overall survival (p = 0.008). A positive SATB1 expression was associated with a GS = 7 were identified as markers for poor prognosis. Their combination overcomes current clinical risk stratification regimes.Functional Genomics of Muscle, Nerve and Brain Disorder