R406 br Materials and methods br Statistical

Materials and methods
Statistical analyses Results were expressed as mean±SEM (standard error of mean). Multiple group comparisons were performed by one-way analyses of variance (ANOVA) followed by the Bonferroni procedure for comparison of means. Comparisons between two groups were performed using the unpaired Student\'s t-test. Data were considered statistically significant at a value of p≤0.05. Analysis of mortality was performed by the Kaplan–Meier-method.
Funding
Conflict of interest
Acknowledgments
Introduction Human stem R406 have potential to be used for treatment of multiple diseases and trauma (Nelson et al., 2010; Anderson et al., 2008). To better understand the properties of stem cells and the ability of these to repair human conditions, many laboratories are routinely culturing and differentiating stem cells in vitro. Accordingly, the numbers of samples processed for fate analysis are increasing exponentially, and accurate automated cell fate analysis would be an enormous improvement in this field. In vitro cultured stem cells are commonly classified and quantified from 2D or 3D images, and different image acquisition platform types used to collect the data have their advantages. Confocal microscopy is a common tool for image acquisition of z-stacks of optical slices of cells or tissues after immunohistochemistry, and it has the advantage of allowing for numerous adjustments, e.g. aperture size, image dimension, and scan speed that affect image quality, the rate of image capture, and file size. These parameters define not just the quality of 3D images, but also the time needed for image capture and the size of data files, which matters especially when dealing with the large number of samples common when testing effects of variable culture conditions such as small molecules and drugs on stem cell differentiation. Depending on the number of optical slices, acquisition time is often shorter for 2D images than z-stacks, especially when using conventional laser scanning confocal microscopes. On the other hand, stem cells typically show heterogenous cell morphology, high number of overlapping cell processes, and small distances between individual cells, which make object recognition in 2D challenging. Identification becomes even more difficult when more than one cell fate marker is used at once; in particular when the proteins of interest are located in the cytoplasm and co-expressed in a proportion of the cells undergoing fate selection (Walton et al., 2006; Rieske et al., 2007). There are several image analysis software tools available that can analyze 2D images, however the major disadvantage is their inability to distinguish partly overlapping objects due to lack of z-axis data (Hamilton, 2009). 2D images are also regularly used for human based manual image analysis to classify and quantify stem cell progeny. However, manual cell counting methods are often an inconsistent and error prone process in which the objects are subjectively scored either positive or negative, and can result in variability of data within experiments between researchers as high as 20%. Conversely, software based image analysis can produce repeatable measurements of not just intensity, but also volume and textures that would not be detectable by a human observer (Hamilton, 2009; Huang and Murphy, 2004). 3D data analysis in z-stacks of optical slices could overcome the majority of classification errors associated with x and y, but requires advanced software and high volume data management. Volocity® (PerkinElmer Inc.) is one of several commercially available advanced high performance 3D-4D image analysis software packages that can measure all volumetric pixels (i.e. voxels in total volume of z-stacks of optical sections) and therefore gain better signal overlap detection in all x, y and z axes improving compiling of cell structures as well as fate classification (Rueden and Eliceiri, 2007). In this report, we have A) created 2D and 3D cell classification protocols for the high performance 3D-4D image analysis software (Volocity®, Perkin Elmer Inc.) to quantify cytoplasmic and nuclear markers in human neural stem cells (hNSCs) after in vitro differentiation. Both protocols can be modified to suit a specific user\'s needs. They allow for semi-automated, more time efficient and accurate stem cell fate classification and quantification, although data verification by operator is still highly recommended. We also show how to B) optimize laser scanning confocal microscope (Fluoview® FV10i; Olympus America Inc.) image acquisition settings to collect 3D information for Volocity® quantification analysis in a more time efficient and accurate manner. C) Finally, we assessed Volocity® based operator validated cell fate analysis in experimental conditions, and compared the time efficiency and validity between software based and human based manual image analysis.