Major histocompatibility complex (MHC) class I antigens in the plasma membranes of human T (HUT-102B2) and B (JY) lymphoma cells were probed by immunochemical reagents using fluorescence, transmission electron, and scanning force microscopies. Fluorescent labels were attached to monoclonal antibodies W6/32 or KE-2 directed against the heavy chain of HLA class I (A, B, C) and L368 or HB28 against the 82-microglobulin light chain. The topological distribution in the nanometer range was studied by photobleaching fluorescence resonance energy transfer (pbFRET) on single cells. A nonrandom codistribution pattern of MHC class I molecules was observed over distances of 2-10 nm. A second, nonrandom, and larger-scale topological organization of the MHC class I antigens was detected by indirect immunogold labeling and imaging by transmission electron microscopy (TEM) and scanning force microscopy (SFM). Although some differences in antigen distribution between the B-and T-cell lines were detected by pbFRET, both cell lines exhibited similar clustering patterns by TEM and SFM. Such defined molecular distributions on the surfaces of cells of the immune system may reflect an underlying specialization of membrane lipid domains and fulfill important functional roles in cell-cell contacts and signal transduction.The plasma membrane of lymphocytes accommodates many transmembrane proteins, receptors, and antigens, which have a limited mobility and/or occur in oligomeric assemblies (1). Multisubunit structures of key receptors involved in lymphocyte activation, like those of the T-cell receptor-CD3 and the interleukin 2 (IL-2) receptor systems, have been observed (2-5). It is likely that clustering of receptors in the membrane, either preexisting or induced by specific ligands, is important not only in transmembrane signaling but also in antigen presentation and cell-cell communication and contact. Seemingly unrelated integral membrane proteins exhibit a nonrandom pattern of codistribution, as detected by flow cytometric fluorescence energy transfer (FCET), microscope-based photobleaching resonance energy transfer (pbFRET), and lateral mobility measurements or biochemical crosslinking experiments (2,(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Biochemical crosslinking and quantitative FRET can probe the lateral distribution pattern of cell surface antigens and receptors over distances of 2-10 nm (20)(21)(22)(23)(24)(25)(26). Recently, a model based on normalized FCET measurements was proposed for the two-dimensional lateral organization of the intercellular adhesion molecule 1 (ICAM-1) molecule, the IL-2 receptor, and the class I and class II human leukocyte antigen (HLA) molecules (2).The long-range lateral distribution of labeled antigens in the plasma membrane can be detected by electron microscopy and scanning force microscopy (SFM). The latter technique provides a tool for investigation of the surface topography at the air-solid or liquid-solid interface, suitable for imaging with high spatial resolution livi...

We present recent advances in DNA specimen preparation technique for scanning force microscopy (SFM) based on spreading on mica in the presence of cationic and non-ionic detergents. Reproducible DNA imaging in air and in n-propsnol has been achieved in the presence of the non-ionic detergent 2,4,6-tris(dimethyhuninomethyl) phenol (DMP-30) or the cationic detergent cetylpyridinium chloride (CP) in a microdrop containing nanograms of DNA. In an alternative procedure, a microdrop of detergent is applied to the surface just prior to the DNA. Quantitative image analysis yields as the apparent molecular dimensions of the DNA a width of -7 nm and a height of -0.7 nm, and delineates the problems of DNA metrology by SFM.

A method is presented to determine the three-dimensional positions of immuno-labeled gold markers from tilted electron micrograph recordings by using image processing techniques. The method consists of three basic modules: localization of the markers in the recordings, estimation of the motion parameters, and matching corresponding markers between the views. Localization consists of a segmentation step based on edge detection and region growing. It also allows for the separation of (visually) aggregated markers. Initial estimates for the motion parameters are obtained from a small number of user-indicated correspondences. A matching algorithm based on simulated annealing is used to find corresponding markers. With the resulting mapping, the motion parameters are updated and used in a new matching step, etc. Once the parameters are stable, the marker depths are retrieved. The developed method has been applied to semithin resin sections of A431 cells labeled for DNA and detected by silver-enhanced ultrasmall gold particles. It represents a reliable method to analyze the three-dimensional distribution of gold markers in electron microscope samples.

In scanning probe microscopy of macromolecules, the specimen to be scanned is deposited on a flat surface such as mica or glass. Unfortunately, there is no guarantee that the orientation of this surface is normal to the scanning directions, or that it is atomically fiat. Phenomena such as capillary forces and noise can cause the surface to appear curved in the recording. Compensation for these background components are necessary before meaningful, quantitative measurements can be performed on the images. We describe a procedure to correct for both the shape of the recorded surface and the influence of noise on the background. The surface is modeled with a low-order polynomial, and the noise by a constant line offset for each scan line. These parameters are initially estimated using all recorded samples, and the result is subtracted from the recording. Then, in an iterative loop, the background is refined by first segmenting the image in back-and foreground pixels, followed by re-estimating the parameters using only the background pixels. For recordings of macromolecules, this procedure performs up to 70% better than the flattening procedure included in the microscope software, in terms of standard deviation of the background pixels as depicted by the final segmentation.

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