Scaling success: Linking public breeding with private enterprise

<p>The known Downstream Promoter Element and Initiator site motifs are shown in boldface.</p

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines

A method for quantification of noise non-uniformity in computed tomography images: A computational study

In computed tomography (CT), the noise is sometimes non-uniform, i.e. the noise magnitude may vary with the gradient level within the image. The purpose of this study was to quantify the noise non-uniformity in CT images using appropriate 1D and 2D computational phantoms, and to validate the effectiveness of the proposed concept in images filtered by the bilateral filter (BF), as an example of a non-linear filter. We first developed 1D and 2D computational phantoms, and Gaussian noises with several noise levels were then added to the phantoms. In addition, to simulate the real form of noise from images obtained in a real CT scanner, a homogeneous water phantom image was used. These noise levels were referred to as ground truth noise (σG). The phantoms were then filtered by the bilateral filter with various pixel value spreads (σ) to produce non-uniform noise. The original gradient phantoms (G) were subtracted from both the noisy phantoms (IN) and the filtered noisy phantoms (IBF), and the magnitudes of the resulting noise for each gradient were computed. The noise-gradient dependency (NGD) curve was used to display the dependency of noise magnitude on image gradient in the non-uniform noise. It is found that for uniform noise, the magnitude of noise was constant for all gradients. However, for non-uniform noise, the measured noise was dependent on the gradient levels and on the strength of the BF for every ground truth noise (σG). It was found that the noise magnitude was large for the large gradients and decreased with the magnitude of the image gradient

The Pathway Coexpression Network: Revealing pathway relationships.

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/

CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses

CELLPEDIA is a repository database for current knowledge about human cells. It contains various types of information, such as cell morphologies, gene expression and literature references. The major role of CELLPEDIA is to provide a digital dictionary of human cells for the biomedical field, including support for the characterization of artificially generated cells in regenerative medicine. CELLPEDIA features (i) its own cell classification scheme, in which whole human cells are classified by their physical locations in addition to conventional taxonomy; and (ii) cell differentiation pathways compiled from biomedical textbooks and journal papers. Currently, human differentiated cells and stem cells are classified into 2260 and 66 cell taxonomy keys, respectively, from which 934 parent–child relationships reported in cell differentiation or transdifferentiation pathways are retrievable. As far as we know, this is the first attempt to develop a digital cell bank to function as a public resource for the accumulation of current knowledge about human cells. The CELLPEDIA homepage is freely accessible except for the data submission pages that require authentication (please send a password request to [email protected])

Mechanistic elucidation of human pancreatic acinar development using single-cell transcriptome analysis on a human iPSC differentiation model.

Few effective treatments have been developed for intractable pancreatic exocrine disorders due to the lack of suitable disease models using human cells. Pancreatic acinar cells differentiated from human induced pluripotent stem cells (hiPSCs) have the potential to solve this issue. In this study, we aimed to elucidate the developmental mechanisms of pancreatic exocrine acinar lineages to establish a directed differentiation method for pancreatic acinar cells from hiPSCs. hiPSC-derived pancreatic endoderm cells were spontaneously differentiated into both pancreatic exocrine and endocrine tissues by implantation into the renal subcapsular space of NOD/SCID mice. Single-cell RNA-seq analysis of the retrieved grafts confirmed the differentiation of pancreatic acinar lineage cells and identified REG4 as a candidate marker for pancreatic acinar progenitor cells. Furthermore, differential gene expression analysis revealed upregulated pathways, including cAMP-related signals, involved in the differentiation of hiPSC-derived pancreatic acinar lineage cells in vivo, and we found that a cAMP activator, forskolin, facilitates the differentiation from hiPSC-derived pancreatic endoderm into pancreatic acinar progenitor cells in our in vitro differentiation culture. Therefore, this platform contributes to our understanding of the developmental mechanisms of pancreatic acinar lineage cells and the establishment of differentiation methods for acinar cells from hiPSCs

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

[Background] Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. [Results] This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. [Conclusions] eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations

NCBI GEO: mining millions of expression profiles—database and tools

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30 000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo

Multiacquisition Variable-Resonance Image Combination Selective Can Improve Image Quality and Reproducibility for Metallic Implants in the Lumbar Spine

The aim of this study is to evaluate how metallic artifacts in the lumbar spine can affect images obtained from magnetic resonance (MR) sequences. We performed a phantom experiment by scanning an agar containing an orthopedic metallic implant using 64-channel multidetector row computed tomography (CT) and a 3-tesla MR unit. We compared the reproducibility in each measurement, enlargement or reduction ratio of the CT and MR measurements, and signal deviation in each voxel from the control. The reproducibility on CT and multiacquisition variable-resonance image combination selective (MAVRIC SL) was good, but that on the other MR sequences showed either fixed bias or proportional bias. The reduction ratios of the distance between the nails were significantly smaller in MAVRIC SL than in the other MR sequences after CT measurements (p<0.001, respectively). MAVRIC SL was able to reduce the metallic artifact, permitting observation of the tissue surrounding the metal with good reproducibility