scholarly journals Laocoön: a tool for high-throughput automated cell counting

2019 ◽  
Author(s):  
Kaitlin Lim ◽  
Mikaela Louie ◽  
Anne La Torre ◽  
Corinne Fairchild ◽  
Ian Korf
Keyword(s):  
Operating System ◽  
Cell Cycle ◽  
High Throughput ◽  
Region Of Interest ◽  
Cell Counting ◽  
User Input ◽  
Link Type ◽  
Automated Cell Counting ◽  
Python Package ◽  
Number Of Cells

STRUCTURED ABSTRACTMotivationThere are current programs and plugins that automatically count the number of cells in a given image. However, many of these processes are not entirely automatic, as they require user input to specify a region of interest and are also frequently inaccurate.ResultsThis project presents laocoön, a Python package specifically designed to automatically and efficiently count the number of fluorescently-labelled cells in images. This package not only allows for reliable cell counting, but returns the proportion of cells in each cell cycle relative to all the cells in the DAPI channel, which is currently used for research purposes, but could ultimately be utilized for clinical purposes.Availability and ImplementationThis package, its corresponding execution instructions, and further information about the underlying algorithms, are currently available in the GitHub repository https://github.com/edukait/laocoon under the MIT license and can be run on the command terminal of any operating system. Alternatively, laocoön is available in the Python Package Index (PyPi), so the user can use the pip command to immediately download the [email protected]

scholarly journals A cell-based high-throughput screening assay for radiation susceptibility using automated cell counting

2015 ◽  
Vol 10 (1) ◽  
pp. 55 ◽  
Author(s):  
Jasmina Hodzic ◽  
Ilse Dingjan ◽  
Mariëlle Maas ◽  
Ida H van der Meulen-Muileman ◽  
Renee X de Menezes ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Cell Counting ◽  
Screening Assay ◽  
Automated Cell Counting ◽  
High Throughput Screening Assay ◽  
A Cell

scholarly journals AGEpy: a Python package for computational biology

2018 ◽  
Author(s):  
Franziska Metge ◽  
Robert Sehlke ◽  
Jorge Boucas
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
High Throughput ◽  
Biological Data ◽  
Command Line ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Link Type ◽  
Biological Meaning ◽  
Python Package

AbstractSummary:AGEpy is a Python package focused on the transformation of interpretable data into biological meaning. It is designed to support high-throughput analysis of pre-processed biological data using either local Python based processing or Python based API calls to local or remote servers. In this application note we describe its different Python modules as well as its command line accessible toolsaDiff,abed,blasto,david, andobo2tsv.Availability:The open source AGEpy Python package is freely available at:https://github.com/mpg-age-bioinformatics/AGEpy.Contact:[email protected]

scholarly journals Anti-proliferative mechanism of Huaier extract in human thyroid carcinoma cells

2020 ◽  
Author(s):  
Jingni He ◽  
Ying Zhang ◽  
Lidong Wang ◽  
Yifang Yu ◽  
Baiyu Yao ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Flow Cytometry ◽  
Thyroid Cancer ◽  
Cancer Cells ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Treated Group ◽  
G1 Phase ◽  
Thyroid Cancer Cells ◽  
Number Of Cells

Abstract BackgroundThyroid cancer is the most common endocrine tumor and typically has a good prognosis; however, some patients still present with local or distant metastases. Huaier is a traditional Chinese medicine reported as effective in treating certain types of tumor, but the effect of Huaier on thyroid cancer has not yet been reported. MethodsThe thyroid cancer cell lines, B-CPAP and C643, were treated with increasing concentrations of Huaier extract and the therapeutic effect was measured using a cell counting kit 8 (CCK-8) and flow cytometry. High-throughput sequencing was further performed to identify differentially expressed genes (DEGs) in Huaier-treated B-CPAP cells. Moreover, quantitative real-time PCR (RT-qPCR) was carried out to verify the selected RNAs. Finally, the dual luciferase detection kit was used to detect gene activity.ResultsProliferation of B-CPAP and C643 cells was significantly suppressed by treatment with Huaier extract in a concentration- and time-dependent manner. Huaier extract also induced cell cycle arrest and apoptosis according to flow cytometry (p < 0.05).High-throughput sequencing observed 7,979 significantly altered transcripts. Gene Ontology (GO) analysis showed that 270 genes were enriched in upregulated terms, while 171 genes were enriched in downregulated terms (p < 0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that there were 47 enriched pathways associated with DEGs (p < 0.05). The expression levels of chosen lncRNAs (SNHG7, MIR181A2HG, ILF3-AS1, and CTA-29F11.1) and their corresponding mRNAs (BBC3, CTSL, GADD45A, and DDIT3) were verified to be overexpressed in Huaier-treated B-CPAP cells by RT-qPCR (p < 0.05).Following transduction, the CCK-8 results showed that the proliferative capacity was increased in the shRNA group as compared with that in the Ctrl and Scr groups. According to flow cytometry, the number of cells in the G0/G1 phase was decreased in the shRNA group (p < 0.01) and the apoptosis rate was lower (p < 0.05). The shRNA-treated group had significantly reduced Huaier-induced apoptosis as compared with the Scr-treated group (p < 0.05). Moreover, the number of cells in the G0/G1 phase in the shRNA-treated group was significantly lower than that in the Scr-treated group (p < 0.05). The results of the dual luciferase reporter gene experiment showed that the activity in the GADD45A WT + miR-301a-3p(+) group was significantly reduced as compared with that in the GADD45A WT + miR-301a-3p(+) NC group (p < 0.01). Further, the activity in the ILF3-AS1 WT + miR-301a-3p(+) group was significantly lower than that in the ILF3-AS1 WT + miR-301a-3p(+) NC group (p < 0.05).ConclusionsThe present study demonstrates that Huaier extract inhibits the proliferation of thyroid cancer cells via changes in the expression levels of a multitude of genes. In particular, a decrease in GADD45A expression enhances the proliferative ability of thyroid cancer cells, the levels of which can be increased by Huaier treatment, thus regulating the cell cycle and apoptosis. Huaier can inhibit the proliferation of thyroid cancer cells through the ILF3-AS1/hsa-miR-301a-3p/GADD45A ceRNA axis.

scholarly journals dms2dfe: Comprehensive Workflow for Analysis of Deep Mutational Scanning Data

2016 ◽  
Author(s):  
Rohan Dandage ◽  
Kausik Chakraborty
Keyword(s):  
Noise Reduction ◽  
High Throughput ◽  
Critical Issue ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Selection Pressures ◽  
Link Type ◽  
Supplementary Material ◽  
End To End ◽  
Python Package

SummaryHigh throughput genotype to phenotype (G2P) data is increasingly being generated by widely applicable Deep Mutational Scanning (DMS) method. dms2dfe is a comprehensive end-to-end workflow that addresses critical issue with noise reduction and offers variety of crucial downstream analyses. Noise reduction is carried out by normalizing counts of mutants by depth of sequencing and subsequent dispersion shrinkage at the level of calculation of preferential enrichments. In downstream analyses, dms2dfe workflow provides identification of relative selection pressures, potential molecular constraints and generation of data-rich visualizations.Availabilitydms2dfe is implemented as a python package and it is available at https://kc-lab.github.io/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Hypercluster: a python package and SnakeMake pipeline for flexible, parallelized unsupervised clustering optimization

2020 ◽  
Author(s):  
Lili Blumenberg ◽  
Kelly V. Ruggles
Keyword(s):  
Big Data ◽  
Single Cell ◽  
High Throughput ◽  
Unsupervised Clustering ◽  
Multiple Models ◽  
Rna Seq ◽  
Link Type ◽  
Hyperparameter Selection ◽  
Clustering Optimization ◽  
Python Package

AbstractUnsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow. To streamline this process, we present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Hypercluster is available on bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster.Author summaryUnsupervised clustering is a technique for grouping similar samples within a dataset. It is extremely common when analyzing big data from patient samples, or high throughput techniques like single cell RNA-seq. When researchers use unsupervised clustering, they have to select parameters that affect the final result—for instance, how many groups they expect to find or what the smallest group is allowed to be. Some methods require setting even less intuitive parameters. For most applications, it is extremely challenging to guess what the values of these parameters should be; therefore to prevent introducing bias into the final results, researchers should test many different parameters and methods to find the best groups. This process is cumbersome, slow and challenging to perform in a reproducible way. We developed hypercluster, a tool that automates this process, make it much faster, and presenting the results in a reproducible and helpful manner.

High density CMOS electrode array for high-throughput and automated cell counting

2013 ◽  
Vol 181 ◽  
pp. 842-849 ◽  
Author(s):  
Tze Sian Pui ◽  
Yu Chen ◽  
Chee Chung Wong ◽  
Revanth Nadipalli ◽  
Roshan Weerasekera ◽  
...  
Keyword(s):  
High Throughput ◽  
Electrode Array ◽  
High Density ◽  
Cell Counting ◽  
Automated Cell Counting

scholarly journals Hypercluster: a flexible tool for parallelized unsupervised clustering optimization

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Lili Blumenberg ◽  
Kelly V. Ruggles
Keyword(s):  
High Throughput ◽  
Unsupervised Clustering ◽  
Ease Of Use ◽  
Multiple Models ◽  
Optimal Model ◽  
Flexible Tool ◽  
Link Type ◽  
Hyperparameter Selection ◽  
Clustering Optimization ◽  
Python Package

Abstract Background Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow. Results We present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model. Conclusions Hypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster.

Comparison between optical microscopy and automation for cytometric analysis of pericardial fluids in a cohort of adult subjects undergoing cardiac surgery

2019 ◽  
Vol 72 (7) ◽  
pp. 493-500 ◽  
Author(s):  
Sabrina Buoro ◽  
Michela Seghezzi ◽  
Maria del Carmen Baigorria Vaca ◽  
Barbara Manenti ◽  
Valentina Moioli ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Limit Of Detection ◽  
Pericardial Fluid ◽  
Cell Counting ◽  
Current Evidence ◽  
Limited Information ◽  
Fluid Analysis ◽  
Automated Cell Counting ◽  
Limit Of Quantitation ◽  
Number Of Cells

AimsLimited information is available on number and type of cells present in the pericardial fluid (PF). Current evidence and has been garnered with inaccurate application of guidelines for analysis of body fluids. This study was aimed at investigating the performance of automate cytometric analysis of PF in adult subjects.MethodsSeventy-four consecutive PF samples were analysed with Sysmex XN with a module for body fluid analysis (XN-BF) and optical microscopy (OM). The study also encompassed the assessment of limit of blank, limit of detection and limit of quantitation (LoQ), imprecision, carryover and linearity of XN-BF module.ResultsXN-BF parameters were compared with OM for the following cell classes: total cells (TC), leucocytes (white blood cell [WBC]), polymorphonuclear (PMN) and mononuclear (MN) cells. The relative bias were −4.5%, 71.2%, 108.2% and −47.7%, respectively. Passing and Bablok regression yielded slope comprised between 0.06 for MN and 5.8 for PMN, and intercept between 0.7 for PMN and 220.3 for MN. LoQ was comprised between 3.8×106 and 6.0×106 cells/L for WBC and PMN. Linearity was acceptable and carryover negligible.ConclusionsPF has a specific cellular composition. Overall, automated cell counting can only be suggested for total number of cells, whereas OM seems still the most reliable option for cell differentiation.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

OSI-027 alleviates oxaliplatin chemoresistance in gastric cancer cells by suppressing P-gp induction

2020 ◽  
Vol 20 ◽  
Author(s):  
En Xu ◽  
Hao Zhu ◽  
Feng Wang ◽  
Ji Miao ◽  
Shangce Du ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cell Cycle ◽  
Cancer Cells ◽  
Mammalian Target Of Rapamycin ◽  
Cell Counting ◽  
Gastric Cancer Cells ◽  
Ags Cells ◽  
Dual Inhibitor ◽  
Induced Apoptosis

: Gastric cancer is one of the most common malignancies worldwide and the third leading cause of cancer-related death. In the present study, we investigated the potential activity of OSI-027, a potent and selective mammalian target of rapamycin complex 1/2 (mTOR1/2) dual inhibitor, alone or in combination with oxaliplatin against gastric cancer cells in vitro. Cell counting kit-8 assays and EdU staining were performed to examine the proliferation of cancer cells. Cell cycle and apoptosis were detected by flow cytometry. Western blot was used to detect the elements of the mTOR pathway and Pgp in gastric cancer cell lines. OSI-027 inhibited the proliferation of MKN-45 and AGS cells by arresting the cell cycle in the G0/G1 phase. At the molecular level, OSI-027 simultaneously blocked mTORC1 and mTORC2 activation, and resulted in the downregulation of phosphor-Akt, phpspho-p70S6k, phosphor-4EBP1, cyclin D1, and cyclin-dependent kinase4 (CDK4). Additionally, OSI-027 also downregulated P-gp, which enhanced oxaliplatin-induced apoptosis and suppressed multidrug resistance. Moreover, OSI-027 exhibited synergistic cytotoxic effects with oxaliplatin in vitro, while a P-gp siRNA knockdown significantly inhibited the synergistic effect. In summary, our results suggest that dual mTORC1/mTORC2 inhibitors (e.g., OSI-027) should be further investigated as a potential valuable treatment for gastric cancer.

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