scholarly journals An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 344 ◽  
Author(s):  
Chloe C. Lepage ◽  
Laura L. Thompson ◽  
Bradley Larson ◽  
Kirk J. McManus
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Quantitative Imaging ◽  
Chromosome Instability ◽  
Genotoxic Stress ◽  
Automated Image Analysis ◽  
Micronucleus Formation ◽  
Distinct Cell ◽  
Efficient Alternative

Micronuclei are small, extranuclear bodies that are distinct from the primary cell nucleus. Micronucleus formation is an aberrant event that suggests a history of genotoxic stress or chromosome mis-segregation events. Accordingly, assays evaluating micronucleus formation serve as useful tools within the fields of toxicology and oncology. Here, we describe a novel micronucleus formation assay that utilizes a high-throughput imaging platform and automated image analysis software for accurate detection and rapid quantification of micronuclei at the single cell level. We show that our image analysis parameters are capable of identifying dose-dependent increases in micronucleus formation within three distinct cell lines following treatment with two established genotoxic agents, etoposide or bleomycin. We further show that this assay detects micronuclei induced through silencing of the established chromosome instability gene, SMC1A. Thus, the micronucleus formation assay described here is a versatile and efficient alternative to more laborious cytological approaches, and greatly increases throughput, which will be particularly beneficial for large-scale chemical or genetic screens.

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 scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

2020 ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Regulation Of Gene Expression ◽  
R Package ◽  
Genomic Features ◽  
Distinct Cell ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
Differential Variability

AbstractHigh throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation of epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.

scholarly journals Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0245638
Author(s):  
Sripad Ram ◽  
Pamela Vizcarra ◽  
Pamela Whalen ◽  
Shibing Deng ◽  
C. L. Painter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Large Scale ◽  
Digital Image Analysis ◽  
Tumor Resection ◽  
Automated Image Analysis ◽  
Current Standard ◽  
Tissue Sections ◽  
Slide Image ◽  
Standard Of Practice

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

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.

Multidimensional single-cell analysis based on fluorescence microscopy and automated image analysis

2017 ◽  
Vol 409 (16) ◽  
pp. 4009-4019 ◽  
Author(s):  
Michael Sandmann ◽  
Martin Lippold ◽  
Franziska Saalfrank ◽  
Chimezie Progress Odika ◽  
Sascha Rohn
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Automated Image Analysis ◽  
Cell Analysis
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