Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay

Guan, Benjamin X.; Bhanu, Bir; Talbot, Prue; Lin, Sabrina

doi:10.1109/tcbb.2014.2306836

Cited by 13 publications

(12 citation statements)

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“…Images were obtained every 2 h for 48 h, then analyzed using CL Quant software (DR Vision, Seattle, WA) to track dynamic cell processes, such as proliferation and death (Lin et al, 2010a, 2010b; Guan et al, 2014; Talbot et al, 2014). …”

Section: Methodsmentioning

confidence: 99%

Thirdhand smoke: Chemical dynamics, cytotoxicity, and genotoxicity in outdoor and indoor environments

Bahl

Shim

Jacob

et al. 2016

Toxicology in Vitro

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We tested the toxicity of thirdhand smoke (THS) using two controlled laboratory exposure scenarios and low levels of THS. One exposure modeled THS in a car parked outdoors, while the second modeled THS in a room without sunlight. The fabrics were exposed to cigarette smoke and then extracted in culture medium. Concentrations of nicotine, nicotine related alkaloids, and tobacco specific nitrosamines (TSNAs) were determined in fresh and aged extracts. The concentration of TSNAs increased with aging in the indoor experiment. THS extracts were used for cytotoxicity testing using mouse neural stem cells (mNSC), human dermal fibroblasts (hDF) and human palatal mesenchyme cells (hPM). Extracts from the car experiment inhibited mNSC proliferation in a live cell imaging assay and induced single strand DNA breaks in mNSC and hDF. In the indoor experiment, THS extracts made with medium containing serum proteins were significantly more toxic than extracts made with basal medium, and mNSC and hPM were more sensitive than hDF. These data indicate that: (1) aging of THS chemical differs on different fabrics and differs with and without sunlight; (2) very few cigarettes are sufficient to produce a toxic THS residue; and (3) protein enhances the efficiency of extraction of cytotoxic chemicals.

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Section: Methodsmentioning

confidence: 99%

Thirdhand smoke: Chemical dynamics, cytotoxicity, and genotoxicity in outdoor and indoor environments

Bahl

Shim

Jacob

et al. 2016

Toxicology in Vitro

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“…We detected and cropped stem cells from video frames of size 600 x 800 using a method developed by Guan et al . [24]. In the following we provide a brief description of the method.…”

Section: Methodsmentioning

confidence: 99%

“…The substrate becomes the background after the hESC are placed on its surface. Therefore, we model a hESC image with two regions of interest: foreground and background [24]. Fig 6 shows examples of the cell (foreground) and the substrate (background) and their intensity distributions.…”

Section: Methodsmentioning

confidence: 99%

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DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC

Theagarajan

Bhanu

2019

PLoS ONE

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Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson’s, Huntington’s, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting of two parts: (a) Generative Multi Adversarial Networks (GMAN) for generating synthetic images of hESC, (b) a hierarchical classification system consisting of Convolution Neural Networks (CNN) and Triplet CNNs to classify phase contrast hESC images into six different classes namely: Cell clusters , Debris , Unattached cells , Attached cells , Dynamically Blebbing cells and Apoptically Blebbing cells . The approach is totally non-invasive and does not require any chemical or staining of hESC. DeephESC 2.0 is able to classify hESC images with an accuracy of 93.23% out performing state-of-the-art approaches by at least 20%. Furthermore, DeephESC 2.0 is able to generate large number of synthetic images which can be used for augmenting the dataset. Experimental results show that training DeephESC 2.0 exclusively on a large amount of synthetic images helps to improve the performance of the classifier on original images from 93.23% to 94.46%. This paper also evaluates the quality of the generated synthetic images using the Structural SIMilarity (SSIM) index, Peak Signal to Noise ratio (PSNR) and statistical p -value metrics and compares them with state-of-the-art approaches for generating synthetic images. DeephESC 2.0 saves hundreds of hours of manual labor which would otherwise be spent on manually/semi-manually annotating more and more videos.

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“…A recent report used phase-contrast video segmentation to generate lineage trees of neural stem cells using cell location, shape, movement, and size [ 17 ]. We previously developed in-house video segmentation tools to analyze single hESC and small colonies [ 8 – 11 ]. In a toxicological application of video bioinformatics using CL-Quant software [ 18 ], cigarette smoke treatment altered hESC colony growth (area) and health [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics

Zahedi

Lin

et al. 2016

PLoS ONE

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There is a foundational need for quality control tools in stem cell laboratories engaged in basic research, regenerative therapies, and toxicological studies. These tools require automated methods for evaluating cell processes and quality during in vitro passaging, expansion, maintenance, and differentiation. In this paper, an unbiased, automated high-content profiling toolkit, StemCellQC, is presented that non-invasively extracts information on cell quality and cellular processes from time-lapse phase-contrast videos. Twenty four (24) morphological and dynamic features were analyzed in healthy, unhealthy, and dying human embryonic stem cell (hESC) colonies to identify those features that were affected in each group. Multiple features differed in the healthy versus unhealthy/dying groups, and these features were linked to growth, motility, and death. Biomarkers were discovered that predicted cell processes before they were detectable by manual observation. StemCellQC distinguished healthy and unhealthy/dying hESC colonies with 96% accuracy by non-invasively measuring and tracking dynamic and morphological features over 48 hours. Changes in cellular processes can be monitored by StemCellQC and predictions can be made about the quality of pluripotent stem cell colonies. This toolkit reduced the time and resources required to track multiple pluripotent stem cell colonies and eliminated handling errors and false classifications due to human bias. StemCellQC provided both user-specified and classifier-determined analysis in cases where the affected features are not intuitive or anticipated. Video analysis algorithms allowed assessment of biological phenomena using automatic detection analysis, which can aid facilities where maintaining stem cell quality and/or monitoring changes in cellular processes are essential. In the future StemCellQC can be expanded to include other features, cell types, treatments, and differentiating cells.

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Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay

Cited by 13 publications

References 13 publications

Thirdhand smoke: Chemical dynamics, cytotoxicity, and genotoxicity in outdoor and indoor environments

Thirdhand smoke: Chemical dynamics, cytotoxicity, and genotoxicity in outdoor and indoor environments

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC

Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics

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