Automated Assessment of β-Cell Area and Density per Islet and Patient Using TMEM27 and BACE2 Immunofluorescence Staining in Human Pancreatic β-Cells

Abstract

In this study we aimed to establish an unbiased automatic quantification pipeline to assess islet specific features such as β-cell area and density per islet based on immunofluorescence stainings. To determine these parameters, the in vivo protein expression levels of TMEM27 and BACE2 in pancreatic islets of 32 patients with type 2 diabetes (T2D) and in 28 non-diabetic individuals (ND) were used as input for the automated pipeline. The output of the automated pipeline was first compared to a previously developed manual area scoring system which takes into account the intensity of the staining as well as the percentage of cells which are stained within an islet. The median TMEM27 and BACE2 area scores of all islets investigated per patient correlated significantly with the manual scoring and with the median area score of insulin. Furthermore, the median area scores of TMEM27, BACE2 and insulin calculated from all T2D were significantly lower compared to the one of all ND. TMEM27, BACE2, and insulin area scores correlated as well in each individual tissue specimen. Moreover, islet size determined by costaining of glucagon and either TMEM27 or BACE2 and β-cell density based either on TMEM27 or BACE2 positive cells correlated significantly. Finally, the TMEM27 area score showed a positive correlation with BMI in ND and an inverse pattern in T2D. In summary, automated quantification outperforms manual scoring by reducing time and individual bias. The simultaneous changes of TMEM27, BACE2, and insulin in the majority of the β–cells suggest that these proteins reflect the total number of functional insulin producing β–cells. Additionally, β–cell subpopulations may be identified which are positive for TMEM27, BACE2 or insulin only. Thus, the cumulative assessment of all three markers may provide further information about the real β–cell number per islet.

Anthropogenic Matrices Favor Homogenization of Tree Reproductive Functions in a Highly Fragmented Landscape

“…One of the major threats to tropical biodiversity is the fragmentation process, which can cause changes in the structure, composition and functions of communities over time [ 1 ]. In plant communities, species homogenization and floristic differentiation are two possible consequences of this process [ 2 , 3 ]. Species homogenization is when sensitive species disappear locally and fragments are dominated by tolerant species [ 4 ].…”

“…Some studies have shown flora homogenization in fragments [ 2 ], while others have indicated that this phenomenon is not always predominant, and that small fragments located in modified human landscapes can retain high levels of biodiversity [ 3 , 5 ]. A possible explanation for these different results is that, over the time, landscape composition (matrix type and forest cover) determines the changes in species of a plant assemblage in fragments that occur in human modified landscapes [ 2 , 3 , 4 , 5 ].…”

“…Thus, landscapes with low forest cover commonly present in anthropogenic matrices are more likely to be experiencing a floristic homogenization process. The few studies about floristic homogenization have shown that the process is dependent on landscape composition [ 2 , 3 ]. However, the homogenization process has two other components, genetic and functional, which have not been investigated.…”

“…We predicted that landscapes with a greater proportion of pasture and sugarcane matrices (inhospitable matrices) would show a reduction in both functional diversity and abundance of individuals within more sensitive RFTs, such as those involving plant-animal interactions (animal pollination and dispersal, and self incompatible reproductive systems) [ 22 ]. On the other hand, we envisaged that the abundance within sensitive functional groups would increase in landscapes with a greater percentage of forest cover or coffee matrix (a shrubby, perennial matrix that is more similar to the fragments in terms of structure [ 3 ]. This information is highly relevant in the context of fragmentation, since the management of forest fragments in human modified landscapes requires understanding how land use changes affect remnant communities.…”

A supervised learning framework for pancreatic islet segmentation with multi‐scale color‐texture features and rolling guidance filters

“…The pancreatic islet can be segmented by constructing a graph to represent the high cell density. This approach has been applied to automated assessment of β-cell areas and density per islet based on immunofluorescence staining (5). However, this method is still limited by its assumption that there is only one islet in each image, and that the islet usually has high density cells (4).…”