integration-animal-behavioral-assessment-convolutional-neural-network

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an ...

Hong Kong Baptist University

This review describes important advances that have been made during the past decade for genome-wide human promoter recognition. Interest in promoter recognition algorithms on a genome-wide scale is worldwide and touches on a number of practical systems that are important in analysis of gene regulation and in genome annotation without experimental support of ESTs, cDNAs or mRNAs. The main focus of this review is on feature extraction and model selection for accurate human promoter recognition, with descriptions of what they are, what has been accomplished, and what remains to be done.

Towards accurate human promoter recognition: a review of currently used sequence features and classification methods.

DNA rigidity is an important physical property originating from the DNA three-dimensional structure. Although the general DNA rigidity patterns in human promoters have been investigated, their distinct roles in transcription are largely unknown. In this paper, we discover four highly distinct human promoter groups based on similarity of their rigidity profiles. First, we find that all promoter groups conserve relatively rigid DNAs at the canonical TATA box [a consensus TATA(A/T)A(A/T) sequence] position, which are important physical signals in binding transcription factors. Second, we find that the genes activated by each group of promoters share significant biological functions based on their gene ontology annotations. Finally, we find that these human promoter groups correlate with the tissue-specific gene expression.

Finding human promoter groups based on DNA physical properties.

Coordination of cell division timing is crucial for proper cell fate specification and tissue growth. However, the differential regulation of cell division timing across or within cell types during metazoan development remains poorly understood. To elucidate the systems-level genetic architecture coordinating division timing, we performed a high-content screening for genes whose depletion produced a significant reduction in the asynchrony of division between sister cells (ADS) compared to that of wild-type during Caenorhabditis elegans embryogenesis. We quantified division timing using 3D time-lapse imaging followed by computer-aided lineage analysis. A total of 822 genes were selected for perturbation based on their conservation and known roles in development. Surprisingly, we find that cell fate determinants are not only essential for establishing fate asymmetry, but also are imperative for setting the ADS regardless of cellular context, indicating a common genetic architecture used by both cellular processes. The fate determinants demonstrate either coupled or separate regulation between the two processes. The temporal coordination appears to facilitate cell migration during fate specification or tissue growth. Our quantitative dataset with cellular resolution provides a resource for future analyses of the genetic control of spatial and temporal coordination during metazoan development. 

Systems-level quantification of division timing reveals a common genetic architecture controlling asynchrony and fate asymmetry.

In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at .

An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model.

Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout Caenorhabditis elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels.

Inference of cellular level signaling networks using single-cell gene expression data in Caenorhabditis elegans reveals mechanisms of cell fate specification.

Intercellular signaling interactions play a key role in breaking fate symmetry during animal development. Identification of signaling interactions at cellular resolution is technically challenging, especially in a developing embryo. Here, we develop a platform that allows automated inference and validation of signaling interactions for every cell cycle of  embryogenesis. This is achieved by the generation of a systems-level cell contact map, which consists of 1114 highly confident intercellular contacts, by modeling analysis and is validated through cell membrane labeling coupled with cell lineage analysis. We apply the map to identify cell pairs between which a Notch signaling interaction takes place. By generating expression patterns for two ligands and two receptors of the Notch signaling pathway with cellular resolution using the automated expression profiling technique, we are able to refine existing and identify novel Notch interactions during  embryogenesis. Targeted cell ablation followed by cell lineage analysis demonstrates the roles of signaling interactions during cell division in breaking fate symmetry. Finally, we describe the development of a website that allows online access to the cell-cell contact map for mapping of other signaling interactions by the community. The platform can be adapted to establish cellular interactions from any other signaling pathway.

Establishment of Signaling Interactions with Cellular Resolution for Every Cell Cycle of Embryogenesis.

Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.

Molecular subtyping of cancer: current status and moving toward clinical applications.

Understanding the cellular architecture is a fundamental problem in various biological studies. C. elegans is widely used as a model organism in these studies because of its unique fate determinations. In recent years, researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. Although various algorithms have been proposed to analyze nucleus, cell shape features are not yet well recorded. This paper proposes a method to systematically analyze three-dimensional morphological cellular features.

3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo.

The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell morphology during development has not yet been systematically characterized in any metazoan, including C. elegans. This knowledge gap substantially hampers many studies in both developmental and cell biology. Here we report an automatic pipeline, CShaper, which combines automated segmentation of fluorescently labeled membranes with automated cell lineage tracing. We apply this pipeline to quantify morphological parameters of densely packed cells in 17 developing C. elegans embryos. Consequently, we generate a time-lapse 3D atlas of cell morphology for the C. elegans embryo from the 4- to 350-cell stages, including cell shape, volume, surface area, migration, nucleus position and cell-cell contact with resolved cell identities. We anticipate that CShaper and the morphological atlas will stimulate and enhance further studies in the fields of developmental biology, cell biology and biomechanics.

Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.

Early embryonic cell cycles usually alternate between S and M phases without any gap phase. When the gap phases are developmentally introduced in various cell types remains poorly defined especially during embryogenesis. To establish the cell-specific introduction of gap phases in embryo, we generate multiple fluorescence ubiquitin cell cycle indicators (FUCCI) in . Time-lapse 3D imaging followed by lineal expression profiling reveals sharp and differential accumulation of the FUCCI reporters, allowing the systematic demarcation of cell cycle phases throughout embryogenesis. Accumulation of the reporters reliably identifies both G1 and G2 phases only in two embryonic cells with an extended cell cycle length, suggesting that the remaining cells divide either without a G1 phase, or with a brief G1 phase that is too short to be picked up by our reporters. In summary, we provide an initial picture of gap phase introduction in a metazoan embryo. The newly developed FUCCI reporters pave the way for further characterization of developmental control of cell cycle progression.

Hong Yan

State Key Laboratory of Environmental and Biological Analysis,
Department of Chemistry,
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry

Hong Kong Baptist University

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Hong Yan

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}State Key Laboratory of Environmental and Biological Analysis,Department of Chemistry,State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry

Hong Kong Baptist University

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

State Key Laboratory of Environmental and Biological Analysis,
Department of Chemistry,
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry