3 research outputs found
A Video Bioinformatics Method to Quantify Cell Spreading and Its Application to Cells Treated with Rho-Associated Protein Kinase and Blebbistatin
Commercial software is available for performing video bioinformatics analysis on cultured cells. Such software is convenient and can often be used to create suitable protocols for quantitative analysis of video
data with relatively little background in image processing. This chapter demonstrates that CL-Quant software, a commercial program produced by DRVision, can be used to automatically analyze cell spreading in time-lapse videos of human embryonic stem cells (hESC). Two cell spreading protocols were developed and tested. One was professionally created by engineers at DRVision and adapted to this project. The other was created by an undergraduate student with 1 month of experience using CL-Quant.
Both protocols successfully segmented small spreading colonies of hESC, and, in general, were in good agreement with the ground truth which was measured using ImageJ. Overall the professional protocol
performed better segmentation, while the user-generated protocol demonstrated that someone who had relatively little background with CL-Quant can successfully create protocols. The protocols were applied to
hESC that had been treated with ROCK inhibitors or blebbistatin, which tend to cause rapid attachment and spreading of hESC colonies. All treatments enabled hESC to attach rapidly. Cells treated with the
ROCK inhibitors or blebbistatin spread more than controls and often looked stressed. The use of the spreading analysis protocol can provide a very rapid method to evaluate the cytotoxicity of chemical treatment and reveal effects on the cytoskeleton of the cell. While hESC are presented in this chapter, other cell types could also be used in conjunction with the spreading protocol
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Video Bioinformatics: Human Embryonic Stem Cell Analysis With Machine Learning
Human Embryonic Stem Cell (hESC) have a great potential for regenerative medicine to provide treatments for Parkinson’s disease, Huntington’s disease, Type 1 diabetes mellitus, etc. Consequently, hESC are often used as a model in the biological assay to study the effects of chemical agents on the human body. Video analysis plays an important role for biological assays in the field of prenatal toxicology and stem cell differentiation. This thesis introduces machine learning techniques for detection, segmentation and classification for hESC analysis. For the detection, a bio-driven algorithm was used to detect cell regions in hESC images. Cell region detection is essential in stem cell focused analysis. It can prevent background information from contaminating the analysis and put more emphasis on processing the cell region. For the segmentation part, a bio-inspired method was proposed for bleb extraction and analysis over time. Bleb formation is a strong health indicator of the stem cell undergoing chemical reactions. Therefore, it is significant to biologist to analyze the formation process over time. For the classification, a deep learning structure was built with both labeled and unlabeled hESC data to classify the six common classes in stem cell images. The six classes are: 1). cell clusters, 2). debris, 3). unattached cells, 4). attached cells, 5). dynamically blebbing cells, and 6). apoptotically blebbing cells. Various results are provided on real video datasets collected using a phase contrast microscope and a Nikon Bio-station
Automated Human Embryonic Stem Cell Detection
Abstract — This paper proposes an automated detection method with simple algorithm for detecting human embryonic stem cell (hESC) regions in phase contrast images. The algorithm uses both the spatial information as well as the intensity distribution for cell region detection. The method is modeled as a mixture of two Gaussians; hESC and substrate regions. The paper validates the method with various videos acquired under different microscope objectives. Keywords-human embryonic stem cell (hESC); substrate; mixture of Gaussians I