17,088 research outputs found
Quantification of Nematic Cell Polarity in Three-dimensional Tissues
How epithelial cells coordinate their polarity to form functional tissues is
an open question in cell biology. Here, we characterize a unique type of
polarity found in liver tissue, nematic cell polarity, which is different from
vectorial cell polarity in simple, sheet-like epithelia. We propose a
conceptual and algorithmic framework to characterize complex patterns of
polarity proteins on the surface of a cell in terms of a multipole expansion.
To rigorously quantify previously observed tissue-level patterns of nematic
cell polarity (Morales-Navarette et al., eLife 8:e44860, 2019), we introduce
the concept of co-orientational order parameters, which generalize the known
biaxial order parameters of the theory of liquid crystals. Applying these
concepts to three-dimensional reconstructions of single cells from
high-resolution imaging data of mouse liver tissue, we show that the axes of
nematic cell polarity of hepatocytes exhibit local coordination and are aligned
with the biaxially anisotropic sinusoidal network for blood transport. Our
study characterizes liver tissue as a biological example of a biaxial liquid
crystal. The general methodology developed here could be applied to other
tissues or in-vitro organoids.Comment: 27 pages, 9 color figure
Irregular S-cone mosaics in felid retinas: spatial interaction with axonless horizontal revealed by cross-correlation
In most mammals short-wavelength-sensitive (S) cones are arranged in irregular patterns with widely variable intercell distances. Consequently, mosaics of connected interneurons either may show some type of correlation to photoreceptor placement or may establish an independent lattice with compensatory dendritic organization. Since axonless horizontal cells (A-HC’s) are supposed to direct all dendrites to overlying cones, we studied their spatial interaction with chromatic cone subclasses. In the cheetah, the bobcat, and the leopard, anti-S-opsin antibodies have consistently colabeled the A-HC’s in addition to the S cones. We investigated the interaction between the two cell mosaics, using autocorrelation and cross-correlation procedures, including a Voronoi-based density probe. Comparisons with simulations of random mosaics show significantly lower densities of S cones above the cell bodies and primary dendrites of A-HC’s. The pattern results in different long-wavelength-sensitive-L- and S-cone ratios in the central versus the peripheral zones of A-HC dendritic fields. The existence of a related pattern at the synaptic level and its potential significance for color processing may be investigated in further studies
Query processing of spatial objects: Complexity versus Redundancy
The management of complex spatial objects in applications, such as geography and cartography,
imposes stringent new requirements on spatial database systems, in particular on efficient
query processing. As shown before, the performance of spatial query processing can be improved
by decomposing complex spatial objects into simple components. Up to now, only decomposition
techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have
been considered. In this paper, we will investigate the natural trade-off between the complexity of
the components and the redundancy, i.e. the number of components, with respect to its effect on
efficient query processing. In particular, we present two new decomposition methods generating
a better balance between the complexity and the number of components than previously known
techniques. We compare these new decomposition methods to the traditional undecomposed representation
as well as to the well-known decomposition into convex polygons with respect to their
performance in spatial query processing. This comparison points out that for a wide range of query
selectivity the new decomposition techniques clearly outperform both the undecomposed representation
and the convex decomposition method. More important than the absolute gain in performance
by a factor of up to an order of magnitude is the robust performance of our new decomposition
techniques over the whole range of query selectivity
3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries
Recent advances in electron microscopy have enabled the imaging of single
cells in 3D at nanometer length scale resolutions. An uncharted frontier for in
silico biology is the ability to simulate cellular processes using these
observed geometries. Enabling such simulations requires watertight meshing of
electron micrograph images into 3D volume meshes, which can then form the basis
of computer simulations of such processes using numerical techniques such as
the Finite Element Method. In this paper, we describe the use of our recently
rewritten mesh processing software, GAMer 2, to bridge the gap between poorly
conditioned meshes generated from segmented micrographs and boundary marked
tetrahedral meshes which are compatible with simulation. We demonstrate the
application of a workflow using GAMer 2 to a series of electron micrographs of
neuronal dendrite morphology explored at three different length scales and show
that the resulting meshes are suitable for finite element simulations. This
work is an important step towards making physical simulations of biological
processes in realistic geometries routine. Innovations in algorithms to
reconstruct and simulate cellular length scale phenomena based on emerging
structural data will enable realistic physical models and advance discovery at
the interface of geometry and cellular processes. We posit that a new frontier
at the intersection of computational technologies and single cell biology is
now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies
available upon reques
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