Cytokine and chemokine profiling is central to understanding inflammatory processes and the mechanisms driving diverse diseases. We introduce InCytokine, an open-source tool for semiquantitative analysis of cytokine and chemokine data generated by protein array technologies. InCytokine features robust and modular image-processing workflows, including automated spot detection, template alignment, normalization, quality control measures, and quantitative intensity summarization to deliver consistent and reliable readouts from profiling assays. We evaluated InCytokine by profiling wild-type microglia, TREM2 knockout, and Alzheimer's disease-associated TREM2 R47H variant cells in response to lipopolysaccharide and sulfatide exposure. Differential expression analysis revealed unique sulfatide-specific and genotype-specific cytokine signatures in TREM2 variants. We also report an intriguing modulation of DPP4 and a divergent expression pattern of ENA-78 in TREM2 variants in response to lipopolysaccharide and sulfatide treatment. Such distinct expression signatures raise the possibility that TREM2 variants may play a role in modulating inflammatory signaling relevant to cardio-metabolic and Alzheimer's disease. These signatures were corroborated using transcriptional profiling of the same microglia cells, revealing also a good concordance between protein array and RNA sequencing technologies. Taken together, InCytokine is an interactive, user-friendly web application for rapid, reproducible, and scalable analysis of protein array data, proven to generate meaningful insights for drug and biomarker discovery campaigns in pharmaceutical settings.

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ( 50

Tissue clearing and Light sheet fluorescence microscopy (LSFM) provide spatial information at a subcellular resolution in intact organs and tumors which is a significant advance over tools that limit imaging to a few representative tissue sections. The spatial distribution of drugs, targets, and biomarkers can help inform relationships between exposure at the site of action, efficacy, and safety during drug discovery. We demonstrate the use of LSFM to investigate distribution of an oncolytic virus (OV) and vasculature in xenograft tumors, as well as brain Aβ pathology in an Alzheimer’s disease (AD) mouse model. Machine learning-based image analysis tools developed to segment vasculature in tumors showed that random forest and deep learning methods provided superior segmentation accuracy vs intensity-based thresholding. Sub-cellular resolution enabled detection of punctate and diffuse intracellular OV distribution profiles. LSFM investigation in the brain in a TgCRND8 AD mouse model at 6.5 months of age enabled evaluation of Aβ plaque density in different brain regions. The utility of LSFM data to support quantitative systems pharmacology (QSP) and physiology-based pharmacokinetics (PBPK) modeling to inform drug development are also discussed. In summary, we showcase how LSFM can expand our understanding of macromolecular drug and biomarker distribution to advance drug discovery and development.

We introduce a new strategy for image analysis of inline microscopy monitoring estimate particle size distri-bution using deep learning. The proposed method consists of two major components: First, a novel way to generate training image-label pairs with a high-level of credibility via a Cycle-consistent Generative Adversarial Network (CycleGAN), and second, a Mask-RCNN model trained with the generated data for the particle detection task. The proposed methodology eliminates the need for manual labeling in the training phase which is a labor-intensive step and can result in labeling errors given the fuzziness of these images. We studied the application of this strategy to images acquired with a particle vision and measurement (PVM) probe. The proposed methodology was applied to images of two particle morphologies with different sizes and concentrations. Our results showed that the proposed methodology could be inexpensively used to determine qualitative trends between crystal size distributions. This trend information is a very important aspect of crys-tallization process monitoring and is often enough to determine what is controlling the crystallization. Therefore, we see our approach as a step in the right direction to provide insights into the particularly challenging PVM inline microscopy monitoring process without the need for offline sampling.

Journal Article High-Resolution Ex Vivo Tissue Clearing, Lightsheet Imaging, and Data Analysis to Support Macromolecular Drug and Biomarker Distribution in Whole Organs and Tumors Get access Niyanta Kumar, Niyanta Kumar ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Corresponding author: niyanta.kumar@merck.com Search for other works by this author on: Oxford Academic Google Scholar Petr Hrobař, Petr Hrobař Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Martin Vagenknecht, Martin Vagenknecht Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Jindrich Soukup, Jindrich Soukup Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Peter Bloomingdale, Peter Bloomingdale Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc., Boston, MA, United States Search for other works by this author on: Oxford Academic Google Scholar Tomoko Freshwater, Tomoko Freshwater Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc., Rahway, NJ, United States Search for other works by this author on: Oxford Academic Google Scholar Sophia Bardehle, Sophia Bardehle Neuroscience, Merck & Co. Inc., Boston, MA, United States Search for other works by this author on: Oxford Academic Google Scholar Roman Peter, Roman Peter Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Nadia Patterson, Nadia Patterson ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Ruban Mangadu, Ruban Mangadu Immuno-Oncology, Merck & Co. Inc., San Francisco, CA, United States Search for other works by this author on: Oxford Academic Google Scholar ... Show more Cinthia Pastuskovas, Cinthia Pastuskovas ADME & Discovery Toxicology, Merck & Co. Inc., San Francisco, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Mark Cancilla Mark Cancilla ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 1436–1437, https://doi.org/10.1017/S1431927622005840 Published: 01 August 2022

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward for in-silico BGC identification.

Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells.

We present a new method for segmentation of phase-contrast microscopic images of cells. The algorithm is based on the variational formulation of the level set method, i.e. minimizing of a functional, which describes the level set function. The functional is minimized by a gradient flow described by an evolutionary partial differential equation. The most significant new ideas are initialization using thresholding and the introduction of a new term based on local variance that speeds up convergence and achieves more accurate results. The proposed algorithm is applied on real data and compared with another algorithm. Our method yields an average gain in accuracy of 2 %.

We propose a general data acquisition model with volatile random displacement of measured samples. Discrepancies between recorded and true positions of the original data is due to the nature of measured data or the acquisition device itself. A reconstruction method based on the Variational Bayesian inference is proposed, which estimates the original data from samples acquired with the acquisition model, and its relation to Jensen's inequality is discussed. A model variant of 2D image reconstruction is analyzed in detail. Further, we outline a relation between the proposed method and the classic deconvolution problem, and illustrate superiority of the Variational Bayesian approach in the case of small number of samples.

M3art database contains data about colours behaviour in VIS and NIR spectral bands. The database is open, publicly available and should serve as the knowledge base for further study of optical properties of pigments, drawing materials and canvases. The content of database consists of FORS and digital camera data collected in range 400-1050nm. Measurements were made on up to three layer samples composed of canvas, underdrawing and colour layers. The colorants were selected to represent historical painting techniques in Gothic and Renaissance According to underdrawing acquisition ability four material categories were established.

Acknowledgment The authors acknowledge the support of the GAUK, grant No. 914813/2013, GA JU 134/2013/Z, project CENAKVA (CZ.1.05/2.1.00/01.0024) and grant GACR No. 13-29225S. The authors would also like to thank the staff of Working place of tissue culture certified laboratory at Nove Hrady, namely Monika Homolkova and Sarka Beranova for their assistance with the manual segmentation of the cells. The results of the project LO1205 were obtained with a financial support from the MEYS under the NPU I program, CENAKVA CZ.1.05/2.1.00/ 01.0024. Original image Blurring Otsu thresholding

The paper describes an innovative method of determining cytotoxicity rate for non- transparent materials. Cytotoxicity is a very important property of materials in terms of their use in health care. It means that all materials with a targeted use in a field of interaction with tissue have to be tested for cytotoxicity. Therefore it is necessary to have a quick and reliable method of determination available. Innovative method for testing cytotoxicity of non-transparent materials is based on monitoring cell-material interaction directly on its surface. Important non-transparent materials used in medicine are titanium alloys. The method is using MG63 cell line which is inoculated on the surface of a material Titan Grade 2 (pure titanium), TiGr5 (Ti6Al4V) and TNT (Ti36Nb4Ta) and cultivated there for approximately three successive generations. The cultivated cells are fluorescently stained so that they are visible in the incident light. Afterwards an analysis of surface coverage of material by cells is accomplished by the means of semi-automatic segmentation software. Image recording ready to be applicable for further analysis is an advantage of this method. The innovative aspect of the method is in utilization of fluorescence for capturing images of cell colonization, of the surface and subsequent semiautomatic determination of the colonization surface which guarantees objective and reproducible results. The determined percentage of tolerance was 68.88 % for TiGr2, 74.01 % for TiGr5 and 77,46 % for TNT.

We present the method for determination of phycobilisomes diffusivity (diffusion coefficient D) on thylakoid membrane from fluorescence recovery after photobleaching (FRAP) experiments. This was usually done by analytical models consisting mainly of a simple curve fitting procedure. However, analytical models need some unrealistic conditions to be supposed. Our method, based on finite difference approximation of the process governed by the Fickian diffusion equation and on the minimization of an objective function representing the disparity between the measured and simulated time-varying fluorescent particles concentration profiles, naturally accounts for experimentally measured time-varying Dirichlet boundary conditions and can include a reaction term as well. The result we get is the overall (time averaged) diffusion coefficient D and the sequence of diffusivities D-j based on two successive fluorescence profiles in j-th time interval. Due to the ill-posedness of our inverse problem, regularization algorithms are implemented. On the synthetic example, we illustrate the behaviour of solution depending on regularization parameter for different signal to noise ratio.

Phase contrast is a noninvasive microscopy imaging technique that is widely used in time-lapse imaging of cells. Resulting images however contain some optical artifacts, which makes automated processing by computer difficult.We developed a novel algorithm for cell segmentation. It is based on processing of time differences between images and combination of thresholding, blurring and morphological operations. We tested the algorithm on four different cell types acquired by two different microscopes. We evaluated the precision of segmentation against the manual segmentation by human operator and compared also with other methods. Our algorithm is simple, fast and shows accuracy that is comparable to manual segmentation. In addition it can correctly separate the dead from living cells.

In the paper we propose an alternative approach to the multispectral data acquisition of the cultural heritage artifacts. The demonstrated solution is mobile, affordable, and consists only of commercial off-the-shelf products. It could be used for the data acquisition in-situ without limitations. It was designed for multispectral scanning of cultural heritage artifacts for their first analysis, for multimedia presentations dedicated to public, and, of course, for art conservation studies. The presented solution contains next to the hardware part as well the description of pre-analysis step - two alternative ways of the photometric calibration - to ensure the anticipated precision. The applicability of the framework was demonstrated on the case study, the preliminary spectral analysis. The proposed methodology is successfully used in the art restoration practice.

The cybernetics was defined as a description of control and communication in living organisms and machines, by Norbert Wiener in 1948. Unfortunately, the part of living organisms is often underestimated. Recently, the initiative „A New Biology for the 21st Century“ of the US National Research Council of the National Academies, announced a goal of re-integration of the many subdiscipline of biology, and the integration into biology of physicists, chemists, computer scientists, engineers, and mathematicians to permit deeper understanding of biological systems. The similar aspect is expected to be a part of the next European framework Horizon 2020. Contemporary situation has to deal with two complementary issues: 1) The system theory and the artificial intelligence already produced plenty of theories, methods, and algorithms for processing and analysis of the digital (sampled and quantizied) signals, including images, to perform generous amount of possible results for given tasks. Various methods were gradually conditioned properly in specific or general way. 2) On the other hand, biology (biochemistry, biophysics, systems biology) is able to generate troubling problems, which are mathematically analogous to the problems already solved in the other scientific fields. Thus, the interdisciplinary collaboration has a possibility to increase an impact of the joint solution. Introduction Institute of complex systems is a part of the Faculty of Fisheries and Protection of Waters, University of South Bohemia. The institute consist of laboratories of Tissue cultures; Laser, Microscopy, Condensed phase and Material Engineering; Macromolecular Structure and Dynamics; and Applied systems biology. In the year 2014 will be created new laboratory for signal processing and analysis. The laboratory developed many software solutions in Matlab environment to help biologists with analysis of microscopical images, and other signals.