The HUDSEN Atlas: a three-dimensional (3D) spatial framework for studying gene expression in the developing human brain.

We are developing a three-dimensional (3D) atlas of the human embryonic brain using anatomical landmarks and gene expression data to define major subdivisions through 12 stages of development [Carnegie Stages (CS) 12-23; approximately 26-56 days post conception (dpc)]. Virtual 3D anatomical models are generated from intact specimens using optical projection tomography (OPT). Using MAPAINT software, selected gene expression data, gathered using standard methods of in situ hybridization and immunohistochemistry, are mapped to a representative 3D model for each chosen Carnegie stage. In these models, anatomical domains, defined on the basis of morphological landmarks and comparative knowledge of expression patterns in vertebrates, are linked to a developmental neuroanatomic ontology. Human gene expression patterns for genes with characteristic expression in different vertebrates (e.g. PAX6, GAD65 and OLIG2) are being used to confirm and/or refine the human anatomical domain boundaries. We have also developed interpolation software that digitally generates a full domain from partial data. Currently, the 3D models and a preliminary set of anatomical domains and ontology are available on the atlas pages along with gene expression data from approximately 100 genes in the HUDSEN Human Spatial Gene Expression Database (http://www.hudsen.org). The aim is that full 3D data will be generated from expression data used to define a more detailed set of anatomical domains linked to a more advanced anatomy ontology and all of these will be available online, contributing to the long-term goal of the atlas, which is to help maximize the effective use and dissemination of data wherever it is generated.

From spatial-data to 3D models of the developing human brain.

Visualisation and interpretation of gene expression data have been crucial to advances in our understanding of mechanisms underlying early brain development. As most developmental processes involve complex changes in size, shape and structure, spatial-data can most readily provide information at multiple levels (cell type, cell location in relation to tissue organisation or body axes, etc.), that can be related to these complex changes. Although three-dimensional (3D) spatial-data are ideal, the restricted availability of suitable tissues makes it difficult to generate these for genes expressed at early human fetal stages. Mapping gene expression data to representative 3D models facilitates combinatorial analysis of multiple expression patterns but does not overcome the problems of sparsely sampled data in time and space. Here we describe software that allows 3D domains to be reconstructed by interpolating between sparse 2D gene expression patterns that have been mapped to 3D representative models of corresponding human developmental stages. A set of procedures are proposed to infer expression domains in these gaps. The procedures, which are connected in a serial way, include components clustering, components tracking, shape matching and points interpolation. Each procedure consists of a graphical user interface and a set of algorithms. Results on exemplar gene data are provided.

Experiments with repeating weighted boosting search for optimization in signal processing applications.

Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations, and optimization methods that require no gradient and can achieve a global optimal solution are highly desired to tackle these difficult problems. The paper proposes a guided global search optimization technique, referred to as the repeated weighted boosting search. The proposed optimization algorithm is extremely simple and easy to implement, involving a minimum programming effort. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used guided global search techniques, known as the genetic algorithm and adaptive simulated annealing, in terms of the requirements for algorithmic parameter tuning. The effectiveness of the proposed algorithm as a global optimizer are investigated through several application examples.