• MeSH
• Cell Biology economics   history   organization & administration   trends   MeSH
• Cytological Techniques history   methods   trends   MeSH
• History, 21st Century MeSH
• Drug Industry organization & administration   trends   MeSH
• Fund Raising organization & administration   trends   MeSH
• Congresses as Topic * history   organization & administration   trends   MeSH
• Humans MeSH
• Small Business economics   methods   organization & administration   trends   MeSH
• Microfluidic Analytical Techniques instrumentation   methods   trends   MeSH
• Image Cytometry * history   methods   trends   MeSH
• Flow Cytometry * history   methods   trends   MeSH
• Societies, Scientific economics   history   organization & administration   trends   MeSH
• Education history   organization & administration   trends   MeSH
• Inventions * economics   trends   MeSH
• Check Tag
• History, 21st Century MeSH
• Humans MeSH
• Publication type
• Letter MeSH
• Historical Article MeSH
• Geographicals
• Czech Republic MeSH
• Canada MeSH

In mass cytometry, the isolation of pure lymphocytes is very important to obtain reproducible results and to shorten the time spent on data acquisition. To prepare highly purified cell suspensions of peripheral blood lymphocytes for further analysis on mass cytometer, we used the new CD81+ immune affinity chromatography cell isolation approach. Using 21 metal conjugated antibodies in a single tube we were able to identify all basic cell subsets and compare their relative abundance in final products obtained by density gradient (Ficoll-Paque) and immune affinity chromatography (CD81+ T-catch™) isolation approach. We show that T-catch isolation approach results in purer final product than Ficoll-Paque (P values 0.0156), with fewer platelets bound to target cells. As a result acquisition time of 105 nucleated cells was 3.5 shorter. We then applied unsupervised high dimensional analysis viSNE algorithm to compare the two isolation protocols, which allowed us to evaluate the contribution of unsupervised analysis over supervised manual gating. ViSNE algorithm effectively characterized almost all supervised cell subsets. Moreover, viSNE uncovered previously overseen cell subsets and showed inaccuracies in Maxpar™ Human peripheral blood phenotyping panel kit recommended gating strategy. These findings emphasize the use of unsupervised analysis tools in parallel with conventional gating strategy to mine the complete information from a set of samples. They also stress the importance of the impurity removal to sensitively detect rare cell populations in unsupervised analysis. © 2016 International Society for Advancement of Cytometry.

• MeSH
• Tetraspanin 28 chemistry   metabolism   MeSH
• Ficoll chemistry   MeSH
• Leukocytes, Mononuclear cytology   MeSH
• Humans MeSH
• Lymphocytes cytology   immunology   MeSH
• Image Cytometry methods   MeSH
• Antibodies chemistry   immunology   MeSH
• Cell Separation methods   MeSH
• Cell Survival immunology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

• MeSH
• Algorithms MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Image Cytometry methods   MeSH
• Pattern Recognition, Automated methods   MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

• MeSH
• Cell Biology MeSH
• Cytogenetics MeSH
• Immunophenotyping MeSH
• Image Cytometry methods   MeSH
• Flow Cytometry * methods   MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Buněčná biologie. Cytologie
• NML Fields
• cytologie, klinická cytologie

Automated image analysis scoring of micronuclei (MN) in cells can facilitate the objective and rapid measurement of genetic damage in mammalian and human cells. This approach was repeatedly developed and tested over the past two decades but none of the systems were sufficiently robust for routine analysis of MN until recently. New methodological, hardware and software developments have now allowed more advanced systems to become available. This mini-review presents the current stage of development and validation of the Metasystems Metafer MNScore system for automated image analysis scoring of MN in cytokinesis-blocked binucleated lymphocytes, which is the best-established method for studying MN formation in humans. The results and experience of users of this system from 2004 until today are reviewed in this paper. Significant achievements in the application of this method in research related to mutagen sensitivity phenotype in cancer risk, radiation biodosimetry and biomonitoring studies of air pollution (enriched by new data) are described. Advantages as well as limitations of automated image analysis in comparison with traditional visual analysis are discussed. The current increased use of the Metasystems Metafer MNScore system in various studies and the growing number of publications based on automated image analysis scoring of MN is promising for the ongoing and future application of this approach.

• MeSH
• Child MeSH
• Adult MeSH
• Infant MeSH
• Middle Aged MeSH
• Humans MeSH
• Lymphocytes drug effects   ultrastructure   MeSH
• Micronucleus Tests instrumentation   MeSH
• Micronuclei, Chromosome-Defective MeSH
• Adolescent MeSH
• Young Adult MeSH
• Mutagens toxicity   MeSH
• Infant, Newborn MeSH
• Image Cytometry methods   MeSH
• Lymphocyte Count methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Child, Preschool MeSH
• Aged MeSH
• Dose-Response Relationship, Drug MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Infant MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Infant, Newborn MeSH
• Child, Preschool MeSH
• Aged MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

Image cytometry still faces the problem of the quality of cell image analysis results. Degradations caused by cell preparation, optics, and electronics considerably affect most 2D and 3D cell image data acquired using optical microscopy. That is why image processing algorithms applied to these data typically offer imprecise and unreliable results. As the ground truth for given image data is not available in most experiments, the outputs of different image analysis methods can be neither verified nor compared to each other. Some papers solve this problem partially with estimates of ground truth by experts in the field (biologists or physicians). However, in many cases, such a ground truth estimate is very subjective and strongly varies between different experts. To overcome these difficulties, we have created a toolbox that can generate 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The user can then apply image analysis methods to such simulated image data. The analysis results (such as segmentation or measurement results) can be compared with ground truth derived from input object digital phantoms (or measurements on them). In this way, image analysis methods can be compared with each other and their quality (based on the difference from ground truth) can be computed. We have also evaluated the plausibility of the synthetic images, measured by their similarity to real image data. We have tested several similarity criteria such as visual comparison, intensity histograms, central moments, frequency analysis, entropy, and 3D Haralick features. The results indicate a high degree of similarity between real and simulated image data.

• MeSH
• Algorithms MeSH
• Cell Nucleolus ultrastructure   MeSH
• Cell Nucleus MeSH
• Phantoms, Imaging MeSH
• Microscopy, Fluorescence methods   MeSH
• Granulocytes cytology   MeSH
• HL-60 Cells MeSH
• Humans MeSH
• Microspheres MeSH
• Image Cytometry methods   MeSH
• Imaging, Three-Dimensional methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Education, Distance methods   MeSH
• Genetic Techniques trends   MeSH
• Genetic Research MeSH
• Medical Oncology methods   trends   MeSH
• Humans MeSH
• Microarray Analysis methods   utilization   MeSH
• Microscopy methods   utilization   MeSH
• Neoplasms diagnosis   genetics   MeSH
• Image Cytometry methods   utilization   MeSH
• Statistics as Topic MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH

• MeSH
• Research Support as Topic MeSH
• Image Interpretation, Computer-Assisted methods   instrumentation   MeSH
• Microscopy methods   instrumentation   utilization   MeSH
• Image Cytometry methods   utilization   MeSH
• Image Processing, Computer-Assisted methods   instrumentation   MeSH
• Software trends   MeSH
• Data Display utilization   MeSH

• MeSH
• Research Support as Topic MeSH
• Microscopy, Fluorescence MeSH
• Image Cytometry methods   MeSH
• Sperm Count methods   MeSH
• Fishes physiology   MeSH
• Spermatocytes cytology   physiology   MeSH
• Cell Survival physiology   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Comparative Study MeSH

• MeSH
• Algorithms MeSH
• Cell Physiological Phenomena MeSH
• Microscopy, Confocal instrumentation   MeSH
• Image Cytometry methods   instrumentation   MeSH
• Image Processing, Computer-Assisted MeSH
• Software MeSH