scholarly journals An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)

2018 ◽  
Vol 9 ◽  
Author(s):  
Sarah D. Turner ◽  
Shelby L. Ellison ◽  
Douglas A. Senalik ◽  
Philipp W. Simon ◽  
Edgar P. Spalding ◽  
...  
Keyword(s):  
Image Analysis ◽  
Daucus Carota ◽  
Root Morphology ◽  
Automated Image Analysis ◽  
Analysis Pipeline ◽  
Genetic Studies ◽  
Daucus Carota L

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 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 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 Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

2017 ◽  
Author(s):  
Jonathan A. Atkinson ◽  
Guillaume Lobet ◽  
Manuel Noll ◽  
Patrick E. Meyer ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Root Systems ◽  
Machine Learning Algorithms ◽  
Automated Image Analysis ◽  
Genetic Studies ◽  
Image Descriptors ◽  
Architectural Traits

AbstractBackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).FindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.ConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

scholarly journals 3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets

2020 ◽  
Author(s):  
E. C. Kugler ◽  
J. Frost ◽  
V. Silva ◽  
K. Plant ◽  
K. Chhabria ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Light Sheet ◽  
Relevant Information ◽  
Automated Image Analysis ◽  
Analysis Pipeline ◽  
Biologically Relevant ◽  
Branching Points ◽  
Light Sheet Fluorescence Microscopy

AbstractZebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into vascular development in vivo and 3D. However, robust quantification of the zebrafish cerebral vasculature in 3D remains a challenge, and would be essential to describe the vascular architecture. Here, we report an image analysis pipeline that allows 3D quantification of the total or regional zebrafish brain vasculature. This is achieved by landmark- or object-based inter-sample registration and extraction of quantitative parameters including vascular volume, surface area, density, branching points, length, radius, and complexity. Application of our analysis pipeline to a range of sixteen genetic or pharmacological manipulations shows that our quantification approach is robust, allows extraction of biologically relevant information, and provides novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, we present the first 3D quantification approach to assess the whole 3D cerebrovascular architecture in zebrafish.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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