[
Oncogene,
1999]
1q21 is frequently involved in different types of translocation in many types of cancers. Jumping translocation (JT) is an unbalanced translocation that comprises amplified chromosomal segments jumping to various telomeres. In this study, we identified a novel gene human JTB (Jumping Translocation Breakpoint) at 1q21, which fused with the telomeric repeats of acceptor telomeres in a case of JT. hJTB (human JTB) encodes a trans-membrane protein that is highly conserved among divergent eukaryotic species. JT results in a hJTB truncation, which potentially produces an hJTB product devoid of the trans-membrane domain. hJTB is located in a gene-rich region at 1q21, called EDC (Epidermal Differentiation Complex). This is the first report identifying the gene involved in unbalanced translocations at 1q21.
[
J Neurophysiol,
2015]
Although the ability to detect humidity (i.e., hygrosensation) represents an important sensory attribute in many animal species (including humans), the neurophysiological and molecular bases of such sensory ability remain largely unknown in many animals. Recently, Russell and colleagues (Russell J, Vidal-Gadea AG, Makay A, Lanam C, Pierce-Shimomura JT. Proc Natl Acad Sci USA 111: 8269-8274, 2014) provided for the first time neuromolecular evidence for the sensory integration of thermal and mechanical sensory cues which underpin the hygrosensation strategy of an animal (i.e., the free-living roundworm Caenorhabditis elegans) that lacks specific sensory organs for humidity detection (i.e., hygroreceptors). Due to the remarkable similarities in the hygrosensation transduction mechanisms used by hygroreceptor-provided (e.g., insects) and hygroreceptor-lacking species (e.g., roundworms and humans), the findings of Russell et al. highlight potentially universal mechanisms for humidity detection that could be shared across a wide range of species, including humans.
[
Nat Biotechnol,
2021]
Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.