By developing a wide-field scheme for spectral measurement and implementing photoswitching, we synchronously obtained the fluorescence spectra and positions of ∼10(6) single molecules in labeled cells in minutes, which consequently enabled spectrally resolved, 'true-color' super-resolution microscopy. The method, called spectrally resolved stochastic optical reconstruction microscopy (SR-STORM), achieved cross-talk-free three-dimensional (3D) imaging for four dyes 10 nm apart in emission spectrum. Excellent resolution was obtained for every channel, and 3D localizations of all molecules were automatically aligned within one imaging path.

We report on the fabrication and measurement of devices designed to study the electrochemical behavior of individual monolayer graphene sheets as electrodes. We have examined both mechanically exfoliated and chemical vapor deposited (CVD) graphene. The effective device areas, determined from cyclic voltammetric measurements, show good agreement with the geometric area of the graphene sheets, indicating that the redox reactions occur on clean graphene surfaces. The electron transfer rates of ferrocenemethanol at both types of graphene electrodes were found to be more than 10-fold faster than at the basal plane of bulk graphite, which we ascribe to corrugations in the graphene sheets. We further describe an electrochemical investigation of adsorptive phenomena on graphene surfaces. Our results show that electrochemistry can provide a powerful means of investigating the interactions between molecules and the surfaces of graphene sheets as electrodes.

The application of electron microscopy to hydrated biological samples has been limited by high-vacuum operating conditions. Traditional methods utilize harsh and laborious sample dehydration procedures, often leading to structural artefacts and creating difficulties for correlating results with high-resolution fluorescence microscopy. Here, we utilize graphene, a single-atom-thick carbon meshwork, as the thinnest possible impermeable and conductive membrane to protect animal cells from vacuum, thus enabling high-resolution electron microscopy of wet and untreated whole cells with exceptional ease. Our approach further allows for facile correlative super-resolution and electron microscopy of wet cells directly on the culturing substrate. In particular, individual cytoskeletal actin filaments are resolved in hydrated samples through electron microscopy and well correlated with super-resolution results.

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The discovery and rise of graphene were historically enabled by its ∼10% optical contrast on specialized substrates like oxide-capped silicon. However, substantially lower contrast is obtained on transparent substrates. Moreover, it remains difficult to visualize nanoscale defects in graphene, including voids, cracks, wrinkles, and multilayers, on most device substrates. We report the use of interference reflection microscopy (IRM), a facile, label-free optical microscopy method originated in cell biology, to directly visualize graphene on transparent inorganic and polymer substrates at 30-40% image contrast per graphene layer. Our noninvasive approach overcomes typical challenges associated with transparent substrates, including insulating and rough surfaces, enables unambiguous identification of local graphene layer numbers and reveals nanoscale structures and defects with outstanding contrast and throughput. We thus demonstrate in situ monitoring of nanoscale defects in graphene, including the generation of nanocracks under uniaxial strain, at up to 4× video rate.

Recent studies have sparked debate over whether catalytic reactions enhance the diffusion coefficients D of enzymes. Through high statistics of the transient (600 μs) displacements of unhindered single molecules freely diffusing in common buffers, we here quantify D for four enzymes under catalytic turnovers. We thus formulate how ∼ ±1% precisions may be achieved for D, and show no changes in diffusivity for catalase, urease, aldolase, and alkaline phosphatase under the application of wide concentration ranges of substrates. Our single-molecule approach thus overcomes potential limitations and artifacts underscored by recent studies to show no enhanced diffusion in enzymatic reactions.

The use of patterned substrates to impose geometrical restriction on the lateral mobility of molecules in supported lipid membranes has found widespread utility in studies of cell membranes. Here, we template-pattern supported lipid membranes with nanopatterned graphene. We utilize focused ion beam milling to pattern graphene on its growth substrate, then transfer the patterned graphene to fresh glass substrates for subsequent supported membrane formation. We observe that graphene functions as an excellent lateral diffusion barrier for supported lipid bilayers. Additionally, the observed diffusion dynamics of lipids in nanoscale graphene channels reveal extremely low boundary effects, a common problem with other materials. We suggest this is attributable to the ultimate thinness of graphene.

Using single-molecule displacement/diffusivity mapping (SMdM), an emerging super-resolution microscopy method, here we quantify, at nanoscale resolution, the diffusion of a typical fluorescent protein (FP) in the endoplasmic reticulum (ER) and mitochondrion of living mammalian cells. We thus show that the diffusion coefficients D in both organelles are ∼40% of that in the cytoplasm, with the latter exhibiting higher spatial inhomogeneities. Moreover, we unveil that diffusions in the ER lumen and the mitochondrial matrix are markedly impeded when the FP is given positive, but not negative, net charges. Calculation shows most intraorganellar proteins as negatively charged, hence a mechanism to impede the diffusion of positively charged proteins. However, we further identify the ER protein PPIB as an exception with a positive net charge and experimentally show that the removal of this positive charge elevates its intra-ER diffusivity. We thus unveil a signasymmetric protein charge effect on the nanoscale intraorganellar diffusion.