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...

Supplementary data are available at Bioinformatics online.

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.

Exosomes are small extracellular vesicles (sEVs), playing a crucial role in the intercellular communication in physiological as well as pathological processes. Here, we aimed to study whether the melanoma-derived sEV-mediated communication could adapt to microenvironmental stresses. We compared B16F1 cell-derived sEVs released under normal and stress conditions, including cytostatic, heat and oxidative stress. The miRNome and proteome showed substantial differences across the sEV groups and bioinformatics analysis of the obtained data by the Ingenuity Pathway Analysis also revealed significant functional differences. The in silico predicted functional alterations of sEVs were validated by in vitro assays. For instance, melanoma-derived sEVs elicited by oxidative stress increased Ki-67 expression of mesenchymal stem cells (MSCs); cytostatic stress-resulted sEVs facilitated melanoma cell migration; all sEV groups supported microtissue generation of MSC-B16F1 co-cultures in a 3D tumour matrix model. Based on this study, we concluded that (i) molecular patterns of tumour-derived sEVs, dictated by the microenvironmental conditions, resulted in specific response patterns in the recipient cells; (ii) in silico analyses could be useful tools to predict different stress responses; (iii) alteration of the sEV-mediated communication of tumour cells might be a therapy-induced host response, with a potential influence on treatment efficacy.

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.

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Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.