Support

Reporting Bugs

When things happen, Argos Junior, Argos, and Panmo will generate a backtrace. Please email the backtrace to support@chi-square-works.com.

Documentation

The Argos user manual is available here.This manual is also for Argos Junior and is a subset of the Panmo user manual.

Posters

Simple graphical tools such as histograms, boxplots, and scatterplots are used in a dynamic graphics framework to achieve a synergistic effect, uncovering interesting patterns that would be for sure missed by HCS practitioners using traditional HCS data analysis tools.

Classification trees are used to conduct multiparameter 2-sample comparisons, rank parameters in terms of their importance, and get succinct characterizations of the conditions that drive a cellular phenomenon.

ABSTRACT: Labeling, detection, and quantification of cells in the S-phase of cell cycle progression are not only important in characterizing the basic biology but also in defining the cellular responses to drug treatments. Bromodeoxyuridine (BrdU) labeling of cells followed by antibody staining is the standard method for detecting cells in the S-phase that is amendable to high throughput screening. Antibody detection of BrdU involves harsh treatments or nuclease digestion to facilitate epitope access, which could interfere with multiplexing with other probes. We show a new method of labeling cells in the S-phase using ethynyl-deoxyuridine (EdU) and its subsequent fluorescent detection using a Cu(I) catalyzed [3+2] azidealkyne cycloaddition reaction performed in an aqueous buffer under mild conditions. We demonstrate multiplexing of EdU fluorescence detection with other chemical- and antibodybased fluorescent probes, and image acquisition/analysis using automated fluorescence microscopy.

ABSTRACT: Accurate characterization of the effect of pharmaceuticals or other biologically active reagents on DNA synthesis and cell cycle profession is of great importance not only in drug discovery but also in the study of basic cell biology. The traditional approach for DNA synthesis detection utilizes an antibody to detect the incorporation of BrdU into the newly synthesized DNA after pulse labeling. To facilitate the access by the antibody to the BrdU incorporated in the chromosomal DNA, the BrdU labeling and detection method involves harsh treatments such as nuclease digestion or acid treatment, leading to inevitable negative consequences when used in combination with other functional probes. With automated microscopy and image analysis, we compared the BrdU method with a new method of pulse-labeling cells with 5-ethynyl-2’- deoxyuridine (EdU) and the subsequent fluorescent detection via a Cu(I) catalyzed click reaction in a mild reaction buffer (pH 7.4). We evaluated these two approaches for their sensitivity, time requirement, ease of use, and compatibility with other antibody labeling. We found that while both methods showed excellent sensitivity with a 5-minute pulse labeling of cells with nucleosides, the BrdU method, due to its DNA denaturation requirements, gave poor performance in co-labeling with an anti-cyclin B1 antibody. On the other hand, the EdU method not only gave excellent cyclin B1 staining, its staining procedure is much simpler and with a shortened processing time (2 hours for EdU vs. at least 4 hours plus an overnight incubation for BrdU). Besides the detection of newly synthesized DNA and co-staining of a protein target of cell cycle importance (cyclin B1), we also demonstrate the profiling of DNA content in the same preparation of cells after drug treatments. This new click chemistry based EdU labeling method for detection of DNA synthesis, not only simplifies and expedites the analysis of DNA synthesis at the cellular level, but should also open the possibility of multiplexing with other functional probes to further enrich the information content of image based assays of cellular activities.

Visualizing the distribution of multiparameter HCS data with minimal spanning tree planing, comparing 2 sets of HCS data with multivariate Wald-Wolfowitz runs tests, and visualizing the difference between 2 sets of HCS data with multivariate P-P plots.

Similar to the previous poster, only it uses multivariate Kolmogorov-Smirnov tests, instead of Wald-Wolfowitz runs tests.

ABSTRACT: Reduced glutathione represents the majority of intracellular free thiols in mammalian cells. Among the many important physiological and pathological aspects involved, glutathione plays a central role in protecting mammalian cells against damage incurred by free radicals, oxidants, and electrophiles. Currently available fluorescent probes for detecting intracellular thiols in live cells share drawbacks ranging from low fluorescent intensity, species-dependence of labeling, to a requirement of UV excitation. We have developed a new fluorescent, cell-permeable, thiol reactive probe with the following properties: (1) Maximal excitation at 410 nm and maximal emission at 510 nm; (2) Labeled cells can be detected with standard fluorescence microscopy with a filter set for Hoechst 33342 (Ex/Em = 350 nm/461 nm) ; (3) Labeled cells can be detected flow or laser scanning cytometry with 405 nm laser excitation; (4) Signal intensity and cellular staining pattern are well retained after formaldehyde fixation and 0.5% (v/v) Triton® X-100 extraction. In formaldehyde fixed cells viewed with a fluorescence microscope with a standard filter set for Hoechst 33342, the new probe is estimated to be at least 10 times brighter than monochlorobimane (mBClglutathione, Ex/Em = 394 nm/490 nm) and at least 2 times brighter than 7-amino-4-chloromethylcourmarine (CMAC, Ex/Em = 353 nm/466 nm, not usable with 405 nm laser based instruments). By using automated microscopy and image analysis, responses of cultured human or murine cells to treatments with agents affecting the intracellular glutathione level, such as buthionine sulfoximine (BSO), diethyl maleate (DEM), various quinone toxicants, were readily quantifiable by labeling cells with this new probe.

Tutorials

Focusing and linking

Graphical query formulation

Creating plate views of high-content screening data.

Separating outliers in high-content screening data.

Exploring a high-content screening data set from an experiment to characterize a drug that affects cell cycle progression.

Exploring subpopulations in a high-content screening data set from an siRNA transfection experiment.