We describe an approach for using the flagella axoneme as the basis for biological self-assembling protein nanoarrays. The axoneme is the insoluble protein core of the eukaryotic flagellum or cilium. By attaching a protein of interest to particular axonemal proteins, it is possible to exploit the intraflagellar transport system to incorporate those proteins into the axoneme as it assembles. Using the axoneme as a protein array confers several advantages, such as high protein loading capacity compared to other bioparticle systems; genetically programmed self-assembly without the need for any linking steps; single-step purification of particles without the need for cell lysis, allowing retention and re-use of biomass; and choice of isolating the particle as a membrane enclosed vesicle or as an exposed protein array. Here we test several potential axonemal proteins as adaptor proteins, using green fluorescent protein as a test case. We find that FAP20 is an ideal scaffold protein for this purpose in that it shows high incorporation and uniform localization. We verify that FAP20-GFP constructs are stably associated with the axoneme during purification and storage, that the GFP moiety can be released by protease cleavage, and that the flagellar array can be easily encapsulated in aqueous-oil emulsion droplets for use in microfluidic assays.
Investigating the spatial information of cellular processes in tissues during mouse embryo development is one of the major technical challenges in development biology. Many imaging methods are still limited to the volumes of tissue due to tissue opacity, light scattering and the availability of advanced imaging tools. For analyzing the mitotic spindle angle distribution in developing mouse airway epithelium, we determined spindle angles in mitotic epithelial cells on serial sections of whole airway of mouse embryonic lungs. We then developed a computational image analysis to obtain spindle angle distribution in three dimensional airway reconstructed from the data obtained from all serial sections. From this study, we were able to understand how mitotic spindle angles are distributed in a whole airway tube. This analysis provides a potentially fast, simple and inexpensive alternative method to quantitatively analyze cellular process at subcellular resolution. Furthermore, this analysis is not limited to the size of tissues, which allows to obtain three dimensional and high resolution information of cellular processes in cell populations deeper inside intact organs.
An ongoing challenge in the field of cell biology is to how to quantify the size and shape of organelles within cells.
Automated image analysis methods often utilize thresholding for segmentation, but the calculated surface of objects
depends sensitively on the exact threshold value chosen, and this problem is generally worse at the upper and lower zboundaries
because of the anisotropy of the point spread function. We present here a threshold-independent method for
extracting the three-dimensional surface of vacuoles in budding yeast whose limiting membranes are labeled with a
fluorescent fusion protein. These organelles typically exist as a clustered set of 1-10 sphere-like compartments. Vacuole
compartments and center points are identified manually within z-stacks taken using a spinning disk confocal microscope.
A set of rays is defined originating from each center point and radiating outwards in random directions. Intensity profiles
are calculated at coordinates along these rays, and intensity maxima are taken as the points the rays cross the limiting
membrane of the vacuole. These points are then fit with a weighted sum of basis functions to define the surface of the
vacuole, and then parameters such as volume and surface area are calculated. This method is able to determine the
volume and surface area of spherical beads (0.96 to 2 micron diameter) with less than 10% error, and validation using
model convolution methods produce similar results. Thus, this method provides an accurate, automated method for
measuring the size and morphology of organelles and can be generalized to measure cells and other objects on
biologically relevant length-scales.
Several reports in the biological literature have indicated that when a living cell divides, the two daughter cells have
a tendency to be mirror images of each other in terms of their overall cell shape. This phenomenon would be consistent
with inheritance of spatial organization from mother cell to daughters. However the published data rely on a small
number of examples that were visually chosen, raising potential concerns about inadvertent selection bias. We propose
to revisit this issue using automated quantitative shape comparison methods which would have no contribution from the
observer and which would allow statistical testing of similarity in large numbers of cells. In this report we describe a
first order approach to the problem using rigid curve matching. Using test images, we compare a pointwise
correspondence based distance metric with a chamfer matching strategy and find that the latter provides better
correspondence and smaller distances between aligned curves, especially when we allow nonrigid deformation of the
outlines in addition to rotation.
In this paper we use kymographs and computational image processing to convert 3-D video microscopy data of
intracellular motion into 1-D time series data for further analysis. Because standard tools exist for time series analysis,
this method allows us to produce robust quantitative results from otherwise visual data. The kymograph-based approach
has an additional advantage over standard particle-tracking and flow-based image quantification algorithms in that we
can average out camera noise over the spatial axis of the kymograph. The method has the disadvantage that it removes
all spatial information. For this reason we see this method as a complement to rather than a replacement of standard
The standard problem we are trying to address in our work is how fluorescent proteins in one cellular compartment are
injected into another cellular compartment. The proteins travel at constant speed along a fixed spatial path, so a 2-D
kymograph produced from a trace along this fixed path will tell us about the injection history into this second
Our algorithm works by first taking a Radon transform of the input 2-D kymograph. We next make synthetic
kymographs by backprojection. The angle with the best correlation between the original kymograph and the backprojection
determines the dominant speed of the moving particles as well as the angle of the 1-D projected time series.
Time series are then analyzed with standard tools to determine the peak size distribution, the peak interval distribution,
the autocorrelation and the power spectrum.
Chromosomes are often arranged into specific configurations. One example is the metaphase plate of the Drosophila embryo in which chromosomes are arranged into a parallel bundle. How is this configuration established and maintained? Quantitative analysis of chromosomes motion in vivo should help answer this question by providing a measure of the relevant mechanical properties of the chromosomes themselves. In addition, motion analysis will allow us to study interactions of chromosomes with the mitotic spindle. In order to analyze moving mitotic chromosomes, we acquire time-lapse 3D images of chromosomes in living Drosophila embryos, and then interactively model the chromosome configuration at each time point. A model-based motion estimation algorithm is then applied. From the motion estimate, we can visualize trajectories of different regions on the chromosomes, such as centromeres and telomeres, during metaphase and during prometaphase congression. In addition, quantitative estimates of mechanical properties such as mobility and flexibility can be computed. In this preliminary report we describe computational tools for tracking and visualizing 3D chromosome motion, and for detecting oscillations in position along the mitotic spindle.
We present an algorithm for estimating nonrigid motion of chromosomes in 4D microscopic images. Chromosomes are represented by a graph and motion estimation is formulated as a graph matching problem. All chromosomes within the graph are located, and then simulated annealing is used to find the mapping of chromosomes at time t onto chromosomes at time t+1 that minimizes the integrated displacement along each chromosome. Results with actual 4D images indicate that this model-based approach is sufficiently robust to correctly track the motion of chromosomes under conditions of limited spatial and temporal resolution. Using the motion estimate, previously developed methods for the quantitative analysis of 3D structure are extended to four dimensions, allowing chromosome mobility, flexibility, and interactions to be measured. Application of these algorithms to 4D images of Drosophila metaphase chromosomes in vivo allows visualization of clearly defined domains of high chromosomal flexibility, as well as other regions of distinctly lower chromosomal mobility which may coincide with centrometers.
Four-dimensional (4D) microscopy, the acquisition of time-resolved 3D images of living biological samples, provides not only structural data but also contains information on the mechanics and forces inside a living cell. In order to extract, quantify, and visualize such information for chromosomes, we are exploring simple computational methods that will allow quantitative estimation of chromosome motion within 4D images of living nuclei. A correspondence-based approach is required due to limited temporal resolution. The chromosomes we are tracking are relatively textureless, but additional structural constraints can be imposed to discriminate against false matches.