Automated high-throughput screening of carbon nanotube-based bio-nanocomposites for bone cement applications

2011 ◽  
Vol 83 (11) ◽  
pp. 2063-2069 ◽  
Author(s):  
Paula P. Gonçalves ◽  
Manoj K. Singh ◽  
Virgília S. Silva ◽  
Filipa Marques ◽  
Ana Marques ◽  
...  
Keyword(s):  
High Throughput ◽  
Cell Morphology ◽  
High Throughput Screening ◽  
Trypan Blue ◽  
Cellular Response ◽  
Mg63 Cell ◽  
Cell Diameter ◽  
Blue Dye ◽  
Bioactive Materials ◽  
Average Cell

In this work we demonstrate the potential of using an automated cell viability analyzer for developing high-throughput screening of orthopedic bioactive materials. We used a biomaterial of carbon nanotubes (CNTs)-based composite integrated with hydroxyapatite/polymethyl methacrylate (HA/PMMA) with controlled physical and chemical properties to evaluate the usefulness of morphometric analysis in conjunction with trypan blue dye exclusion assays in MG63 cell cultures. The MG63 cell line, derived from human bone osteo-sarcoma, is often used as a model for studying osteoblast-like cellular response to bioactive materials for orthopedic surgery. The viability analyzer, Vi-CELLTM XR, Beckman Coulter, was used with trypan blue dye exclusion method in cell suspensions obtained after trypsinization along with determining the distribution plots of cell diameter and circularity, which are critical cellular characteristics. In addition, the activity of alkaline phosphatase (ALP), a typical representation of osteogenic activity of osteoblasts, was also measured spectro-photometrically using p-nitrophenol phosphate as the substrate. Comparative analysis of the frequency histogram of average cell diameter and circularity allowed for the analyses of significant alterations in cell morphology not only over time in control cultures (spherical vs. a flat morphology) but also with respect to PMMA and HA nanocomposites. After cell exposure to HA/PMMA/CNTs, a shift toward loss of cell circularity was observed. The appearances of more differentiated morphologic features were well correlated with the increase of secreted ALP activity. In conclusion, the evaluation of material-induced changes of cell morphology could represent a valuable prescreening test for bioactive properties.

Rapid generation of Hybrid Biochemical/Mechanical Cues in Heterogeneous Droplets for High-Throughput Screening of Cellular Response

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Xing Zhao ◽  
Gaozhi Ou ◽  
Mengcheng Lei ◽  
Yang Zhang ◽  
Lina Li ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cellular Response ◽  
Wide Range ◽  
Rapid Generation ◽  
P Cells

Cells in native microenvironment are subjected to varying combinations of biochemical cues and mechanical cues in a wide range. Despite many signaling pathways have been found to be responsive for...

Microfabricated arrays for high-throughput screening of cellular response to cyclic substrate deformation

Lab on a Chip ◽  
2010 ◽  
Vol 10 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Christopher Moraes ◽  
Jan-Hung Chen ◽  
Yu Sun ◽  
Craig A. Simmons
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Cellular Response ◽  
Microfabricated Arrays

scholarly journals An image-based flow cytometric approach to the assessment of the nucleus-to-cytoplasm ratio

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253439
Author(s):  
Joseph A. Sebastian ◽  
Michael J. Moore ◽  
Elizabeth S. L. Berndl ◽  
Michael C. Kolios
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
High Throughput ◽  
Malignant Cell ◽  
Cell Diameter ◽  
Average Cell ◽  
Malignant Cell Line ◽  
Assessment Techniques ◽  
High Throughput Detection ◽  
Ht 29

The nucleus-to-cytoplasm ratio (N:C) can be used as one metric in histology for grading certain types of tumor malignancy. Current N:C assessment techniques are time-consuming and low throughput. Thus, in high-throughput clinical contexts, there is a need for a technique that can assess cell malignancy rapidly. In this study, we assess the N:C ratio of four different malignant cell lines (OCI-AML-5—blood cancer, CAKI-2—kidney cancer, HT-29—colon cancer, SK-BR-3—breast cancer) and a non-malignant cell line (MCF-10A –breast epithelium) using an imaging flow cytometer (IFC). Cells were stained with the DRAQ-5 nuclear dye to stain the cell nucleus. An Amnis ImageStreamX® IFC acquired brightfield/fluorescence images of cells and their nuclei, respectively. Masking and gating techniques were used to obtain the cell and nucleus diameters for 5284 OCI-AML-5 cells, 1096 CAKI-2 cells, 6302 HT-29 cells, 3159 SK-BR-3 cells, and 1109 MCF-10A cells. The N:C ratio was calculated as the ratio of the nucleus diameter to the total cell diameter. The average cell and nucleus diameters from IFC were 12.3 ± 1.2 μm and 9.0 ± 1.1 μm for OCI-AML5 cells, 24.5 ± 2.6 μm and 15.6 ± 2.1 μm for CAKI-2 cells, 16.2 ± 1.8 μm and 11.2 ± 1.3 μm for HT-29 cells, 18.0 ± 3.7 μm and 12.5 ± 2.1 μm for SK-BR-3 cells, and 19.4 ± 2.2 μm and 10.1 ± 1.8 μm for MCF-10A cells. Here we show a general N:C ratio of ~0.6–0.7 across varying malignant cell lines and a N:C ratio of ~0.5 for a non-malignant cell line. This study demonstrates the use of IFC to assess the N:C ratio of cancerous and non-cancerous cells, and the promise of its use in clinically relevant high-throughput detection scenarios to supplement current workflows used for cancer cell grading.

Approaches to Automating the Dereplication of Bioactive Natural Products—The Key Step in High Throughput Screening of Bioactive Materials From Natural Sources

1997 ◽  
Vol 2 (3) ◽  
pp. 145-152 ◽  
Author(s):  
Derek J. Hook ◽  
Edward J. Pack ◽  
Joseph J. Yacobucci ◽  
Jeffrey Guss
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
New Technologies ◽  
Critical Factor ◽  
Rapid Identification ◽  
Bioactive Materials ◽  
Screening Programs ◽  
Bioactive Natural Products ◽  
Compound Libraries ◽  
Automated Procedures

The rapid identification of the bioactive component(s) of natural product mixtures in high throughput screening programs has become a critical factor to ensure that this source of diverse chemotypes can compete effectively with chemical compound libraries and combinatorial synthetic efforts. The effective use of automated procedures and databases in the isolation, identification and biological profiling of bioactive compounds will be described. In addition, the potential of new technologies to enhance this process will be discussed as well as the possible reintroduction of TLC as a parallel dereplication method.

scholarly journals High-Content, High-Throughput Screening for the Identification of Cytotoxic Compounds Based on Cell Morphology and Cell Proliferation Markers

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88338 ◽  
Author(s):  
Heather L. Martin ◽  
Matthew Adams ◽  
Julie Higgins ◽  
Jacquelyn Bond ◽  
Ewan E. Morrison ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
High Throughput ◽  
Cell Morphology ◽  
High Throughput Screening ◽  
Proliferation Markers ◽  
Cytotoxic Compounds

scholarly journals A Novel Organelle Map Framework for High-Content Cell Morphology Analysis in High Throughput

2013 ◽  
Vol 19 (2) ◽  
pp. 317-324 ◽  
Author(s):  
Kristine Schauer ◽  
Jean-Philippe Grossier ◽  
Tarn Duong ◽  
Violaine Chapuis ◽  
Sébastien Degot ◽  
...  
Keyword(s):  
High Throughput ◽  
Cell Morphology ◽  
High Throughput Screening ◽  
Culture Conditions ◽  
Statistical Testing ◽  
Small Scale ◽  
Mapping Approach ◽  
Density Maps ◽  
Wide Range ◽  
Cell Shapes

A screening procedure was developed that takes advantage of the cellular normalization by micropatterning and a novel quantitative organelle mapping approach that allows unbiased and automated cell morphology comparison using black-box statistical testing. Micropatterns of extracellular matrix proteins force cells to adopt a reproducible shape and distribution of intracellular compartments avoiding strong cell-to-cell variation that is a major limitation of classical culture conditions. To detect changes in cell morphology induced by compound treatment, fluorescently labeled intracellular structures from several tens of micropatterned cells were transformed into probabilistic density maps. Then, the similarity or difference between two given density maps was quantified using statistical testing that evaluates differences directly from the data without additional analysis or any subjective decision. The versatility of this organelle mapping approach for different magnifications and its performance for different cell shapes has been assessed. Density-based analysis detected changes in cell morphology due to compound treatment in a small-scale proof-of-principle screen demonstrating its compatibility with high-throughput screening. This novel tool for high-content and high-throughput cellular phenotyping can potentially be used for a wide range of applications from drug screening to careful characterization of cellular processes.

scholarly journals Use of FLIPR Membrane Potential Dyes for Validation of High-Throughput Screening with the FLIPR and µARCS Technologies: Identification of Ion Channel Modulators Acting on the GABAA Receptor

2008 ◽  
Vol 13 (3) ◽  
pp. 218-228 ◽  
Author(s):  
Christoph Joesch ◽  
Emelie Guevarra ◽  
Serge P. Parel ◽  
Andreas Bergner ◽  
Peter Zbinden ◽  
...  
Keyword(s):  
Membrane Potential ◽  
High Throughput ◽  
High Throughput Screening ◽  
Constitutive Expression ◽  
Initial Assessment ◽  
Blue Dye ◽  
Imaging Plate ◽  
Allosteric Modulators ◽  
Positive Allosteric Modulators ◽  
Red Dye

Fluorometric imaging plate reader (FLIPR) membrane potential dyes (FMP-Red-Dye and FMP-Blue-Dye) were evaluated for the detection of compounds acting either as positive allosteric modulators or agonists on the GABAA receptor (GABAAR). A stable HEK293 cell line with constitutive expression of the rat GABA AR α1, β2, and γ2 genes was used to establish a functional high-throughput screening (HTS) assay based on measurement of the membrane potential change in living cells. The assay was validated with the FLIPR technology for identification of agonists and positive allosteric modulators using GABA and diazepam as model compounds. The FMP-Red-Dye showed better performance than the FMP-Blue-Dye, and the effects induced by GABA and diazepam were comparable to electrophysiology data. Subsequently, the assay was also validated with an ultra-HTS approach known as microarrayed compound screening (µARCS). The LOPAC library was used in a test screen for an initial assessment of the technology. Finally, the FLIPR and µARCS technologies were tested with a larger screening campaign. A focused library of 3520 putative positive modulators was tested with the FLIPR assay, and a diverse subset of 84,480 compounds was selected for screening with the µARCS technology. All hits were subjected to verification using the FLIPR technology, and confirmed hits were subsequently evaluated by EC50 determination. Finally, selected hits were further confirmed with electrophysiology testing. ( Journal of Biomolecular Screening 2008:218-228)

scholarly journals Deep2Full: Predictive model for complementing phenotypic outcomes in a deep mutational scan using protein sequence and structure information

2017 ◽  
Author(s):  
C. K. Sruthi ◽  
Meher K. Prakash
Keyword(s):  
Amino Acid ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cellular Response ◽  
Computational Models ◽  
Single Point ◽  
Drug Concentrations ◽  
Scan Data ◽  
The Impact ◽  
Bio Engineering

AbstractLarge scale mutagenesis experiments are becoming possible owing to the advancement in the sequencing technologies and high throughput screening. Deep mutational scans perform exhaustive single-point muta-tions on a protein and probe their phenotypic effects. Performing a full scan with site-directed mutations of all the amino acid residues in a protein may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In order to develop the realistic paradigm of assay context-aware predictive hybrid models, we combine minimal deep mutational studies with computational models and pre-dict the phenotypic outcomes quantitatively. Structural, sequence and co-evolutionary information along with partial deep mutational scan data was included to capture the phenotypic relevance of the mutations to the specific screening criterion. The model reliably predicts the fitness outcomes of hundreds of randomly selected amino acid mutations in β-lactamase, when the phenotypic fitness data from as few as 15% of the full mutation is available. Interestingly, the predictive capabilities are better with a random set of mutations rather than with a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH). The model can potentially be extended for predicting the phenotypic outcomes at other concentrations of the stressor by carefully analyzing the dose-response curves of a representative set of mutations.Author SummaryMutations are the minor changes in protein sequences, with incommensurately high consequences for their function. Many severe diseases can occur with simple single point mutations. An interesting way of studying these mutations is not to isolate the protein from its natural conditions, but rather study how the fitness of the cell improves or decreases in response to these mutations. Whether it is for understanding disease biology or for bio-engineering applications it is important to quantify the impact of mutations on the cellular fitness. An experimental paradigm has evolved which has improved the ability to sample several hundred thousands of mutation-fitness relations using high throughput screening. However, since these are very specialized experiments, the question is if the number of such experiments required can be minimized, by using computer models to complement the rest of the fitness predictions. In this work we introduce this new paradigm which uses computer model trained on a partial deep mutation scan data, to predict the fitness variations in a full mutations scan that could also be repeated under multiple experimental conditions like drug concentrations.

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