scholarly journals ImagePlane: An Automated Image Analysis Pipeline for High-Throughput Screens Using the Planarian Schmidtea mediterranea

2013 ◽  
Vol 20 (8) ◽  
pp. 583-592 ◽  
Author(s):  
Steven Flygare ◽  
Michael Campbell ◽  
Robert Mars Ross ◽  
Barry Moore ◽  
Mark Yandell
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Automated Image Analysis ◽  
Schmidtea Mediterranea ◽  
Analysis Pipeline ◽  
High Throughput Screens

scholarly journals Machine learning-based classification of mitochondrial morphology in primary neurons and brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Garrett M. Fogo ◽  
Anthony R. Anzell ◽  
Kathleen J. Maheras ◽  
Sarita Raghunayakula ◽  
Joseph M. Wider ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cortical Neurons ◽  
Mitochondrial Dynamics ◽  
Automated Image Analysis ◽  
Mitochondrial Morphology ◽  
Analysis Pipeline ◽  
Genetic Ablation ◽  
Block Face

AbstractThe mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

scholarly journals High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells

Lab on a Chip ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 3652-3663 ◽  
Author(s):  
Patricia M. Davidson ◽  
Gregory R. Fedorchak ◽  
Solenne Mondésert-Deveraux ◽  
Emily S. Bell ◽  
Philipp Isermann ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Time Scale ◽  
Viscoelastic Properties ◽  
Micropipette Aspiration ◽  
Automated Image Analysis ◽  
Cell Nuclei ◽  
Intact Cells ◽  
Nuclear Mechanics ◽  
Analysis Platform

We report the development, validation, and application of an easy-to-use microfluidic micropipette aspiration device and automated image analysis platform that enables high-throughput measurements of the viscoelastic properties of cell nuclei.

scholarly journals A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
M. Elena Garcia-Pardo ◽  
Jeremy C. Simpson ◽  
Niamh C. O’Sullivan
Keyword(s):  
Endoplasmic Reticulum ◽  
Image Analysis ◽  
Mammalian Cells ◽  
Large Scale ◽  
Morphological Changes ◽  
Methodological Approach ◽  
Automated Image Analysis ◽  
Adherent Cell ◽  
Analysis Pipeline ◽  
Cultured Human Cells

Abstract Background In mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as changes to the structure and organisation of the reticular network. Due to its complex morphology, image analysis methods to quantitatively describe this organelle, and importantly any changes to it, are lacking. Results In this work we detail a methodological approach that utilises automated high-content screening microscopy to capture images of cells fluorescently-labelled for various ER markers, followed by their quantitative analysis. We propose that two key metrics, namely the area of dense ER and the area of polygonal regions in between the reticular elements, together provide a basis for measuring the quantities of rough and smooth ER, respectively. We demonstrate that a number of different pharmacological perturbations to the ER can be quantitatively measured and compared in our automated image analysis pipeline. Furthermore, we show that this method can be implemented in both commercial and open-access image analysis software with comparable results. Conclusions We propose that this method has the potential to be applied in the context of large-scale genetic and chemical perturbations to assess the organisation of the ER in adherent cell cultures.

scholarly journals An automated, high-throughput image analysis pipeline enables genetic studies of shoot and root morphology in carrot (Daucus carota L.)

2018 ◽  
Author(s):  
Sarah D. Turner ◽  
Shelby L. Ellison ◽  
Douglas A. Senalik ◽  
Philipp W. Simon ◽  
Edgar P. Spalding ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Root Morphology ◽  
Agronomic Traits ◽  
Automated Analysis ◽  
Root Characteristics ◽  
Analysis Pipeline ◽  
Genetic Studies ◽  
Daucus Carota L ◽  
Size And Shape

AbstractCarrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

scholarly journals A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Morteza Shabannejad ◽  
Mohammad-Reza Bihamta ◽  
Eslam Majidi-Hervan ◽  
Hadi Alipour ◽  
Asa Ebrahimi
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cost Effective ◽  
Analysis Pipeline ◽  
Terminal Drought ◽  
Days To Maturity ◽  
Stress Ratios ◽  
Terminal Drought Stress

Abstract Background High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. Results The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. Conclusions This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.

scholarly journals HTPheno: An image analysis pipeline for high-throughput plant phenotyping

2011 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Anja Hartmann ◽  
Tobias Czauderna ◽  
Roberto Hoffmann ◽  
Nils Stein ◽  
Falk Schreiber
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Analysis Pipeline

scholarly journals Measuring Quantitative Virulence in the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis

2014 ◽  
Vol 104 (9) ◽  
pp. 985-992 ◽  
Author(s):  
Ethan L. Stewart ◽  
Bruce A. McDonald
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Continuous Distribution ◽  
Automated Image Analysis ◽  
Host Specialization ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Zymoseptoria Tritici

Zymoseptoria tritici, causal agent of Septoria tritici blotch on wheat, produces pycnidia in chlorotic and necrotic lesions on infected leaves. A high-throughput phenotyping method was developed based on automated digital image analysis that accurately measures the percentage of leaf area covered by lesions (PLACL) as well as pycnidia size and number. A seedling inoculation assay was conducted using 361 Z. tritici isolates originating from a controlled cross and two different winter wheat cultivars. Pycnidia size and density were found to be quantitative traits that showed a continuous distribution in the progeny. There was a weak correlation between pycnidia density and size (r = −0.27) and between pycnidia density and PLACL (r = 0.37). There were significant differences in PLACL and pycnidia density on resistant and susceptible cultivars. In all, >20% of the offspring exhibited significantly different pycnidia density on the two cultivars, consistent with host specialization. Automated image analysis provided greater accuracy and precision compared with traditional visual estimates of virulence. These results show that digital image analysis provides a powerful tool for measuring differences in quantitative virulence among strains of Z. tritici.

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