Splice variants in apoptotic pathway

Elimination of superfluous or mutated somatic cells is provided by various mechanisms including apoptosis, and deregulation of apoptotic signaling pathways contributes to oncogenesis. 40 years have passed since the term “apoptosis” was introduced by Kerr et al. in 1972; among the programmed cell death, a variety of therapeutic strategies especially targeting apoptotic pathways have been investigated. Alternative precursor messenger RNA splicing, by which the process the exons of pre-mRNA are spliced in different arrangements to produce structurally and functionally distinct mRNA and proteins, is another field in progress, and it has been recognized as one of the most important mechanisms that maintains genomic and functional diversity. A variety of apoptotic genes are regulated through alternative pre-mRNA splicing as well, some of which have important functions as pro-apoptotic and anti-apoptotic factors. In this article we summarized splice variants of some of the apoptotic genes including BCL2L1, BIRC5, CFLAR, and MADD, as well as the regulatory mechanisms of alternative splicing of these genes. If the information of the apoptosis and aberrant splicing in each of malignancies is integrated, it will become possible to target proper variants for apoptosis, and the trans-elements themselves can become specific targets of cancer therapy as well. This article is part of a Special Issue entitled “Apoptosis: Four Decades Later”

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

Scaling success: Linking public breeding with private enterprise

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

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])

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

Integrated Collection of Stem Cell Bank Data, a Data Portal for Standardized Stem Cell Information

世界中で樹立されたiPS細胞の数や疾患の種類が明らかに. 京都大学プレスリリース. 2021-03-19.The past decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed the Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16, 000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1, 000 diseases, including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals

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

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

Background: Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise.Objective: This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms.Material and Methods: This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms.Results: The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise. Conclusion: The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise