Projects

  • Tracking and Segmentation for Cell Lineage Reconstruction

    Research Area: A.2-Light Microscopy
     

    Our final goal is to reconstruct full lineage trees for each individual cell with morphology information in entire developing complex model organisms, such as Drosophila and zebrafish. Such information would open new doors to quantitative analyses of cellular dynamics such as comprehensive mapping of gene expression dynamics or automated cellular phenotyping and biophysical analyses of cell shape changes and cellular forces, to mention a few.

  • Olfactory input by transsynaptic tracing

    Research Area: A.2-Light Microscopy
     

    The functions of mammalian brains are based on the activity patterns of large numbers of interconnected neurons that form information processing circuits. Neural circuits consist of local connections— where pre- and post-synaptic partners reside within the same brain area and long-distance connections, which link different areas (Fig. 1). Local connections can be predicted by axon and dendrite reconstructions, and confirmed by physiological recording and stimulation methods. Long-distance connections are more difficult to map, as commonly used methods can only trace bulk projections with a coarse resolution. Most methods cannot distinguish axons in passing from those that form synapses, or pinpoint the neuronal types to which connections are made. Trans-synaptic tracers can potentially overcome these limitations. Here we combine a retrograde rabies-virus-dependent mono-trans-synaptic labeling technique with genetic control of the location, number and cell type of ‘starter’ neurons to trace their presynaptic partners. We systematically mapped long-distance connections between the first olfactory processing centre, the olfactory bulb, and its postsynaptic targets in the olfactory cortex including the anterior olfactory nucleus (AON), piriform cortex and amygdala.

  • Subtomogram averaging

    Research Area: A.1-Electron Microscopy
     

    Subtomogram averaging is a registration algorithm in order to obtain higher resolution structures by averaging thousands of aligned subvolumes containing the same structural unit (Fig. 1). For example, we can obtain multiple tomograms of bacteria and select subvolumes containing flagellar motors from each tomogram. If the hypthesis that flagellar motor structure is the same in all the samples, we can register all those subvolumes and average them in order to obtain a higher resolution structure than from a single tomogram. The basic idea is very similar to single particle approaches: by combining two similar images that have been blurred in different directions by the missing wedge, we can obtain a better reconstruction of the original image. However it has the advantage that we can apply to all sorts of tomographic samples and not only to purified structures. As of 2011, resolutions close to 20 ̊A have been reported using subtomogram averaging, and continuing progress in the field makes higher resolutions expected. The end goal is to achieve near-atomic resolution of biological complexes close to their native state. A review by Bartesaghi and Subramaniam contains an excellent review on this topic.

  • Indexing of fetal anatomies from 3D ultrasound images

    Research Area: A.4-Medical Imaging
     

    The use of 3D ultrasound data has several advantages over 2D ultrasound for fetal biometric measurements, such as considerable decrease in the examination time, possibility of post-exam data processing by experts and the ability to produce 2D views of the fetal anatomies in orienta- tions that cannot be seen in common 2D ultrasound exams. However, the search for standardized planes and the precise localization of fetal anatomies in ultrasound volumes are hard and time consuming processes even for expert physicians and sonographers. The relative low resolution in ultrasound volumes, small size of fetus anatomies and inter- volume position, orientation and size variability make this localization problem even more challenging. In order to make the plane search and fetal anatomy localization prob- lems completely automatic, we introduce a novel principled probabilistic model that combines discriminative and generative classifiers with contextual information and sequential sampling. We implement a system based on this model, where the user queries consist of semantic keywords that represent anatomical structures of interest. After queried, the system automatically displays standardized planes and produces biometric measurements of the fetal anatomies. Experimental results on a held-out test set show that the automatic measurements are within the interuser variability of expert users. It resolves for position, orientation and size of three different anatomies in less than 10 seconds in a dual-core computer running at 1.7 GHz (Fig. 1).

  • Automatic electron tomography alignment: RAPTOR

    Research Area: A.1-Electron Microscopy
     

    The first tomograms were obtained from fixed samples at room temperature, and opened a dramatic new window into the ultrastructure of cells [1]. They made it abundantly clear that the high­resolution, 3­D information present in tomograms would be essential for understanding the complex spatial relationships of organelles, microtubules, vesicles, ribosomes, and other large structures within cells. Efforts were then launched to perform “serial­section, montage” tomography, where beam and image shifts are used to record a tiled montage of large (a few microns square) areas of a series of sequential sections, and then all the resultant tomograms are stitched together to produce a 3­D reconstruction of a substantial fraction of a cellular volume [3]. This will eventually make it possible to reconstruct representative human cells in their entirety by serial section montage tomography, in both healthy and diseased conditions, at nanoscopic scale. Real high-­throughput technology is crucial to achieve this goal since each single cell montage requires thousands of tomograms to be acquired and processed.

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