Spatial organization of fibroblast nuclear chromocenters: component tree analysis.

The nuclei of mouse connective tissue fibroblasts contain chromocenters which are well-defined zones of heterochromatin that can be used as positional landmarks to examine nuclear remodeling in response to a mechanical perturbation. This study used component tree analysis, an image segmentation algorithm that detects high intensity voxels that are topologically connected, to quantify the spatial organization of chromocenters in fibroblasts within whole mouse connective tissue fixed and stained with 4',6-diamidino-2-phenylindole (DAPI). The component tree analysis method was applied to confocal microscopy images of whole mouse areolar connective tissue incubated for 30 min ex vivo with or without static stretch. In stretched tissue, the mean distance between chromocenters within fibroblast nuclei was significantly greater (vs. non-stretched, P < 0.001), corresponding to an average of a 500-nm increase in chromocenter separation (~10% strain). There was no significant difference in chromocenter number or average size between stretch and no stretch. Average chromocenter distance was positively correlated with nuclear cross-sectional area (r = 0.78, P < 0.0001), and nuclear volume (r = 0.42, P < 0.0001), and negatively correlated with nuclear aspect ratio (r = -0.65, P < 0.0001) and nuclear concavity index (r = -0.44, P < 0.0001). These results demonstrate that component trees can be successfully applied to the morphometric analysis of nuclear chromocenters in fibroblasts within whole connective tissue. Static stretching of mouse areolar connective tissue for 30 min resulted in substantially increased separation of nuclear chromocenters in connective tissue fibroblasts. This interior remodeling of the nucleus induced by tissue stretch may impact transcriptionally active euchromatin within the inter-chromocenter space.

Projection-based volume alignment.

When heterogeneous samples of macromolecular assemblies are being examined by 3D electron microscopy (3DEM), often multiple reconstructions are obtained. For example, subtomograms of individual particles can be acquired from tomography, or volumes of multiple 2D classes can be obtained by random conical tilt reconstruction. Of these, similar volumes can be averaged to achieve higher resolution. Volume alignment is an essential step before 3D classification and averaging. Here we present a projection-based volume alignment (PBVA) algorithm. We select a set of projections to represent the reference volume and align them to a second volume. Projection alignment is achieved by maximizing the cross-correlation function with respect to rotation and translation parameters. If data are missing, the cross-correlation functions are normalized accordingly. Accurate alignments are obtained by averaging and quadratic interpolation of the cross-correlation maximum. Comparisons of the computation time between PBVA and traditional 3D cross-correlation methods demonstrate that PBVA outperforms the traditional methods. Performance tests were carried out with different signal-to-noise ratios using modeled noise and with different percentages of missing data using a cryo-EM dataset. All tests show that the algorithm is robust and highly accurate. PBVA was applied to align the reconstructions of a subcomplex of the NADH: ubiquinone oxidoreductase (Complex I) from the yeast Yarrowia lipolytica, followed by classification and averaging.

Probabilistic principal component analysis with expectation maximization (PPCA-EM) facilitates volume classification and estimates the missing data.

We have developed a new method for classifying 3D reconstructions with missing data obtained by electron microscopy techniques. The method is based on principal component analysis (PCA) combined with expectation maximization. The missing data, together with the principal components, are treated as hidden variables that are estimated by maximizing a likelihood function. PCA in 3D is similar to PCA for 2D image analysis. A lower dimensional subspace of significant features is selected, into which the data are projected, and if desired, subsequently classified. In addition, our new algorithm estimates the missing data for each individual volume within the lower dimensional subspace. Application to both a large model data set and cryo-electron microscopy experimental data demonstrates the good performance of the algorithm and illustrates its potential for studying macromolecular assemblies with continuous conformational variations.

Tissue stretch induces nuclear remodeling in connective tissue fibroblasts.

Studies in cultured cells have shown that nuclear shape is an important factor influencing nuclear function, and that mechanical forces applied to the cell can directly affect nuclear shape. In a previous study, we demonstrated that stretching of whole mouse subcutaneous tissue causes dynamic cytoskeletal remodeling with perinuclear redistribution of alpha-actin in fibroblasts within the tissue. We have further shown that the nuclei of these fibroblasts have deep invaginations containing alpha-actin. In the current study, we hypothesized that tissue stretch would cause nuclear remodeling with a reduced amount of nuclear invagination, measurable as a change in nuclear concavity. Subcutaneous areolar connective tissue samples were excised from 28 mice and randomized to either tissue stretch or no stretch for 30 min, then examined with histochemistry and confocal microscopy. In stretched tissue (vs. non-stretched), fibroblast nuclei had a larger cross-sectional area (P < 0.001), smaller thickness (P < 0.03) in the plane of the tissue, and smaller relative concavity (P < 0.005) indicating an increase in nuclear convexity. The stretch-induced loss of invaginations may have important influences on gene expression, RNA trafficking and/or cell differentiation.