pseudomallei mouse monoclonal and a secondary anti-mouse/Alexa488 antibody. GDC-0973 concentration Scale bar: 90 μm. (B) Visual representation of the MNGC Image Analysis procedure. Each object (Nuclei) is pseudocolored with a unique color in the nucleus segmentation panel. Bacterial spots are pseudocolored in green in the spot segmentation panel. Nuclei clustering: Nuclei are clustered based on distance as described in Experimental procedures to generate the Cluster population. In the MNGC selection panel, image objects classified as MNGC are pseudocolored in green, and non-MNGC objects are pseudocolored in red. (C) Histograms representing the quantification of cellular attributes of the
cluster population as measured by the MNGC image analysis procedure described in Figure 1B. (D) Histograms showing the results of the quantification of cellular attributes related to bacterial spot formation. In C and D means +/- standard deviation (SD) are
shown for three independent B. pseudomallei macrophage infections performed on separate days and with six replicates/plate. n = 18 and > 500 nuclei were analyzed per well. **** p < 0.0001. As observed in the fluorescence microscopy images, Bp infection induced cell-to-cell fusion, clustering of the nuclei and cell body enlargement in a substantial fraction of infected macrophages when compared to mock infected samples (Figure 1A). These cellular objects click here fit the definition of MNGC. A large number of Bp bacterial spots were found to be
either internalized or in close proximity with the boundaries of infected cell bodies. In these experimental conditions not all the infected cells appear to be part of an MNGC object (Figure 1A). Hence, it was important to develop an HCI analysis that would recognize and distinguish MNGC objects from non-MNGC objects in a heterogeneous population of infected cells. To address this issue, we took advantage of the close proximity of the nuclei in MNGC’s to recognize and classify Astemizole MNGC clusters. Briefly, and as shown in Figure 1B, cell nuclei were first identified by using the Hoechst 33342 channel image, thus obtaining a population of objects that was named “Nuclei”. The cell body edges were identified by expanding the body of the nucleus detected in the previous step. The cell body borders were then detected by using the CellMask DeepRed channel image. Bp spots were identified using the Bp antibody channel image. Several cellular attributes were calculated for the Nuclei population, the most relevant being: number of objects, cell body area and number of bacterial spots per object. The next step in the image analysis consisted in recursively clustering distinct Nuclei objects together into a single “Cluster” object, provided that their nuclei were either touching or adjacent.