Registering Drosophila embryos at cellular resolution to build a quantitative 3D atlas of gene expression patterns and morphology

Abstract

The Berkeley Drosophila Transcription Network Project is developing a suite of methods to convert volumetric data generated by confocal fluorescence microscopy into numerical, three dimensional representations of gene expression at cellular resolution. One key difficulty is that fluorescence microscopy can only capture expression levels for a few gene products in a given animal. We report on a method for registering 3D expression data from different Drosophila embryos stained for overlapping subsets of gene products in order to build a composite atlas, ultimately containing coexpression information for thousands of genes. Our techniques have also allowed the discovery of a complex pattern of cell density across the blastula that changes over time and may play a role in gastrulation.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

“…To allow relationships between multiple transcription factors and their target genes to be compared in a common coordinate framework, PointClouds are registered into a Virtual Embryo using both morphology and a common reference gene to determine cell correspondences [6], [7]. Because the spatial patterns of the genes change rapidly during stage 5, we stage the embryos based on invagination of cell membranes and group the PointClouds into six temporal cohorts [2].…”

Visual Exploration of Three-Dimensional Gene Expression Using Physical Views and Linked Abstract Views

“…A Single PointCloud file contains information about the x , y , z location of each nucleus in an embryo, the nuclear and cytoplasmic volumes, and the relative concentrations of gene products (mRNA or protein) associated with each nucleus and surrounding cytoplasm [2], [3], [5]. To generate this data, embryos are labeled typically with two fluorophores to detect two gene products and with a third one to detect the nuclei.…”

“…The great wealth of existing knowledge about the regulatory interactions and pattern formation of the Drosophila blastoderm make it an ideal model for analyzing genomic regulation of complex patterns. This project has developed a suite of methods (Section 2) for extracting quantitative measurements of spatial gene expression at cellular resolution from imaging data, providing information about the locations of all blastoderm nuclei and associated expression levels of a select set of genes [2], [3]. This results in a compact computationally amenable representation of gene expression patterns, called PointClouds, which require efficient means to visually explore these data.…”

Interactive processing and visualization of image data for biomedical and life science applications

“…We generally refer to the measured fluorescent intensities as gene expression levels, assuming that the two are closely correlated, see Luengo et al [7]. A Single PointCloud file that results from this step contains information about the spatial location of each nucleus in an embryo, the nuclear and cellular volumes, and the relative concentrations of gene products (mRNA or protein) associated with each nucleus and cell [7,9,10]. As part of this project, IDAV researchers are developing methods to visualize all types of data encountered in the project ranging from raw image data collected by confocal microscopy imaging to derived PointCloud data.…”