Attachment of cancer urothelial cells to the bladder epithelium occurs on uroplakin-negative cells and is mediated by desmosomal and not by classical cadherins

Urinary bladder cancer is often multifocalhowever, the intraluminal dissemination of the urothelial cancer cells is poorly understood. The involvement of N-cadherin in the adhesion of the cancer urothelial cells to the urothelium had not previously been studied. Therefore, we herein explore the possibility of the intraluminal dissemination of the urothelial cancer cells by evaluating the role of classical cadherins in the adhesion of urothelial cancer cells to the urothelium. We used E-cadherin negative T24 cells and established a T24 Ncad$^{low}$ cell line with an additionally decreased expression of N-cadherin in the plasma membrane and a decreased secretion of proform of metalloproteinase 2. The labelled T24 and T24 Ncad$^{low}$ cells were seeded onto urothelial in vitro models. After 24 h in co-culture, unattached cancer cells were rinsed and urothelia with attached cancer urothelial cells were processed for fluorescence and electron microscopy. Both the T24 and T24 Ncad$^{low}$ cells attached to the urothelium, yet only to the uroplakin-negative urothelial cells. The ultrastructural analysis showed that T24 and T24 Ncad$^{low}$ cells adhere to poorly differentiated urothelial cells by desmosomes. To achieve this, they first disrupt tight junctions of superficial urothelial cells. This study indicates that the lack of E-cadherin expression and decreased expression of N-cadherin in the plasma membrane of T24 cells does not interfere with their adhesion to the urotheliumtherefore, our results suggest that intraluminal dissemination of cancer urothelial cells along the urothelium occurs on uroplakin-negative cells and is desmosome-mediated

sPlotOpen - An environmentally balanced, open-access, global dataset of vegetation plots

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called 'sPlot', compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01-40,000 m(2). Time period and grain 1888-2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot-level records. Software format Three main matrices (.csv), relationally linked

sPlotOpen:an environmentally balanced, open-access, global dataset of vegetation plots

Abstract Motivation: Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01–40,000 m². Time period and grain: 1888–2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records. Software format: Three main matrices (.csv), relationally linked