@article {3839, title = {Simultaneous recognition and segmentation of cells: application in C. elegans.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 Aug 17}, pages = {2895-902}, abstract = {

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7\% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving {\textpm}90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1\%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr480}, author = {Qu, Lei and Long, Fuhui and Liu, Xiao and Kim, Stuart and Myers, Eugene and Peng, Hanchuan} } @article {2149, title = {A 3D digital atlas of C. elegans and its application to single-cell analyses.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Sep}, pages = {667-72}, abstract = {

We built a digital nuclear atlas of the newly hatched, first larval stage (L1) of the wild-type hermaphrodite of Caenorhabditis elegans at single-cell resolution from confocal image stacks of 15 individual worms. The atlas quantifies the stereotypy of nuclear locations and provides other statistics on the spatial patterns of the 357 nuclei that could be faithfully segmented and annotated out of the 558 present at this developmental stage. We then developed an automated approach to assign cell names to each nucleus in a three-dimensional image of an L1 worm. We achieved 86\% accuracy in identifying the 357 nuclei automatically. This computational method will allow high-throughput single-cell analyses of the post-embryonic worm, such as gene expression analysis, or ablation or stimulation of cells under computer control in a high-throughput functional screen.

}, keywords = {Animals, Automatic Data Processing, Biological, Caenorhabditis elegans, Cell Lineage, Cell Nucleus, Models}, issn = {1548-7105}, doi = {10.1007/s12021-010-9090-x}, author = {Long, Fuhui and Peng, Hanchuan and Liu, Xiao and Kim, Stuart K and Myers, Eugene} } @article {2697, title = {Analysis of cell fate from single-cell gene expression profiles in C. elegans.}, journal = {Cell}, volume = {139}, year = {2009}, month = {2009 Oct 30}, pages = {623-33}, abstract = {

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

}, keywords = {Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Cell Differentiation, Cell Lineage, Gene Expression Profiling}, issn = {1097-4172}, doi = {10.1016/j.cell.2009.08.044}, author = {Liu, Xiao and Long, Fuhui and Peng, Hanchuan and Aerni, Sarah J and Jiang, Min and S{\'a}nchez-Blanco, Adolfo and Murray, John I and Preston, Elicia and Mericle, Barbara and Batzoglou, Serafim and Myers, Eugene W and Kim, Stuart K} } @article {2695, title = {Straightening caenorhabditis elegans images.}, journal = {Bioinformatics}, volume = {24}, year = {2008}, month = {2008 Jan 15}, pages = {234-42}, abstract = {

MOTIVATION: Caenorhabditis elegans, a roundworm found in soil, is a widely studied model organism with about 1000 cells in the adult. Producing high-resolution fluorescence images of C.elegans to reveal biological insights is becoming routine, motivating the development of advanced computational tools for analyzing the resulting image stacks. For example, worm bodies usually curve significantly in images. Thus one must {\textquoteright}straighten{\textquoteright} the worms if they are to be compared under a canonical coordinate system. RESULTS: We develop a worm straightening algorithm (WSA) that restacks cutting planes orthogonal to a {\textquoteright}backbone{\textquoteright} that models the anterior-posterior axis of the worm. We formulate the backbone as a parametric cubic spline defined by a series of control points. We develop two methods for automatically determining the locations of the control points. Our experimental methods show that our approaches effectively straighten both 2D and 3D worm images.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Computer-Assisted, Fluorescence, Image Enhancement, Image Interpretation, Imaging, Microscopy, Three-Dimensional}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm569}, author = {Peng, Hanchuan and Long, Fuhui and Liu, Xiao and Kim, Stuart K and Myers, Eugene W} }