In this study we aimed to establish an unbiased automatic quantification pipeline to assess islet specific features such as beta-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 beta-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 beta-cells suggest that these proteins reflect the total number of functional insulin producing beta-cells. Additionally, beta-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 beta-cell number per islet.
Automated Assessment of β-Cell Area and Density per Islet and Patient Using TMEM27 and BACE2 Immunofluorescence Staining in Human Pancreatic β-Cells
Lorella Marselli;Piero Marchetti;
2014-01-01
Abstract
In this study we aimed to establish an unbiased automatic quantification pipeline to assess islet specific features such as beta-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 beta-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 beta-cells suggest that these proteins reflect the total number of functional insulin producing beta-cells. Additionally, beta-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 beta-cell number per islet.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
