The potential of a spheroid tumor model composed of cells in different proliferative and metabolic states for the development of new anticancer strategies has been amply demonstrated. However, there is little or no information in the literature on the problems of reproducibility of data originating from experiments using 3D models. Our analyses, carried out using a novel open source software capable of performing an automatic image analysis of 3D tumor colonies, showed that a number of morphology parameters affect the response of large spheroids to treatment. In particular, we found that both spheroid volume and shape may be a source of variability. We also compared some commercially available viability assays specifically designed for 3D models. In conclusion, our data indicate the need for a pre-selection of tumor spheroids of homogeneous volume and shape to reduce data variability to a minimum before use in a cytotoxicity test. In addition, we identified and validated a cytotoxicity test capable of providing meaningful data on the damage induced in large tumor spheroids of up to diameter in 650 μm by different kinds of treatments.

(2015). CIDRE: an illumination-correction method for optical microscopy. Nature Methods, 12(5) Uneven illumination affects every image acquired by a microscope. It is often overlooked, but it can introduce considerable [AU: Use of "significant" is reserved for the statistical sense; instances in the paper have been changed to "considerable" or "substantial."] bias to image measurements. The most reliable correction methods require special reference images, and retrospective alternatives do not fully model the correction process. Our approach overcomes these issues for most optical microscopy applications without the need for howNo optical system is ideal. Inhomogeneous illumination is present in every image acquired by a microscope. Many factors, including misaligned optics, dust, nonuniform light sources and vignetting, contribute to uneven illumination 1 .It is increasingly common for light microscopes to be used as quantitative instruments even though seemingly minor shifts in illumination can corrupt measurements and invalidate subsequent analyses. For example, we found that uneven illumination increased the false detections and missed detections by CellProfiler 2 on images of yeast cells by 35% when illumination correction was neglected ( Supplementary Fig. 1c-f). Other routine measurements can be affected as well. Uneven illumination substantially reduced the measurements of the mean intensity and mean area of GFP-stained HeLa cells in the corner of the image relative to the center ( Supplementary Fig. 1g-l).The consequences of ignoring uneven illumination are often underestimated, as reflected in our survey of microscope users (Supplementary Note 1). The magnitude of intensity loss attributed to vignetting, that is, falloff of intensity from the center of the image, is often substantially stronger than assumed. Data from 11 ordinary microscope setups revealed that between 10% and 40% less light is typically recorded at the dimmest region of the image (Supplementary Note 2). Intensity loss is even more severe for cameras with large sensor areas or wide apertures, such as scientific complementary metal-oxide semiconductor (sCMOS) devices, which can experience a falloff greater than 50% (Supplementary Note 2).The most common approach for correcting uneven illumination reverses the image formation process, attempting to recover the true image, I, from the image observed by the sensor, I 0 . Distortions to the observed image are modeled by a linear intensity gain function v and an additive term z; I 0 (x) = I(x)v(x) + z(x), where I 0 (x) is the intensity observed at location x. The intensity gain models attenuations to the signal (Fig. 1a). An additive or zero-light term models contributions present even if no light is incident on the sensor, mainly camera offset and fixed- Although simple at first glance, in practice v and z cannot be known exactly, which has prompted the development of a variety of correction methods (Supplementary Note 3). Prospective methods estimate the correction surfaces from special r...

Motivation Microscopy images of stained cells and tissues play a central role in most biomedical experiments and routine histopathology. Storing colour histological images digitally opens the possibility to process numerically colour distribution and intensity to extract quantitative data. Among those numerical procedures is colour deconvolution, which enables decomposing an RGB image into channels representing the optical absorbance and transmittance of the dyes when their RGB representation is known. Consequently, a range of new applications become possible for morphological and histochemical segmentation, automated marker localisation and image enhancement. Availability and implementation Colour deconvolution is presented here in two open-source forms: a MATLAB program/function and an ImageJ plugin written in Java. Both versions run in Windows, Macintosh, and UNIX-based systems under the respective platforms. Source code and further documentation are available at: https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/ Supplementary information Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.