Thirdhand smoke (THS) is a mixture of chemicals that remain on indoor surfaces after smoking has ceased. These chemicals can be inhaled, ingested, or absorbed dermally, and thus could impact human health. We evaluated the cytotoxicity and mode of action of fresh and aged THS, the toxicity of volatile organic chemicals (VOCs) in THS, and the molecular targets of acrolein, a VOC in THS. Experiments were done using mouse neural stem cells (mNSC), human pulmonary fibroblasts (hPF), and lung A549 epithelial cells. THS-exposed cotton cloth was extracted in Dulbecco's Eagle Medium and caused cytotoxicity in the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. THS extracts induced blebbing, immotility, vacuolization, cell fragmentation, severing of microfilaments and depolymerization of microtubules in mNSC. Cytotoxicity was inversely related to headspace volume in the extraction container and was lost upon aging, suggesting that VOCs in THS were cytotoxic. Phenol, 2',5'-dimethyl furan and acrolein were identified as the most cytotoxic VOCs in THS, and in combination, their cytotoxicity increased. Acrolein inhibited proliferation of mNSC and hPF and altered expression of cell cycle regulatory genes. Twenty-four hours of treatment with acrolein decreased expression of transcription factor Dp-1, a factor needed for the G1 to S transition in the cell cycle. At 48 h, WEE1 expression increased, while ANACP1 expression decreased consistent with blocking entry into and completion of the M phase of the cell cycle. This study identified acrolein as a highly cytotoxic VOC in THS which killed cells at high doses and inhibited cell proliferation at low doses.
Significance: Automated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.Aim: This paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope. Approach:The paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1) cell clusters, (2) debris, (3) unattached cells, (4) attached cells, (5) dynamically blebbing cells, and ( 6) apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data. Results:The proposed approach achieves a classification accuracy of 97.23 AE 0.94% and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor. Conclusions:RandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.
This study characterizes dynamic and apoptotic blebbing in human embryonic stem cells (hESC), identifies dynamic blebbing as a bottleneck to successful cell attachment during passaging, and demonstrates that dynamic blebbing can be rapidly stopped by plating cells on recombinant human laminin. In freshly plated hESC, dynamic and apoptotic blebbing differed in time of occurrence, bleb retraction rate, mitochondrial membrane potential, and caspase 3&7 activation. While dynamic blebbing can be controlled with drugs that inhibit myosin II, these methods have off-target effects and are not suitable for clinical applications. Recombinant human laminin-521 or addition of laminin-111 to Matrigel provided a safe method to drastically decrease dynamic blebbing and improve cell attachment with proteins normally found in the inner cell mass. Inhibition of focal adhesion kinase, which is activated by binding of integrins to laminin, prolonged dynamic blebbing and inhibited attachment. These data show that hESC bind rapidly to laminins through an integrin, which activates focal adhesion kinase that in turn downregulates dynamic blebbing. Laminins enabled hESC to rapidly attach during passaging, improved plating efficiency, enabled passaging of single pluripotent stem cells, and avoided use of inhibitors that have non-specific off-target effects. These data provide a strategy for improving hESC culture using biologically safe recombinant human proteins.
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.
Determining the meaningful texture feat embryonic stem cells (hESC) is im development of online hESC classificatio paper proposes the use of novel support vec bio-inspired one-against-all (OAA) multi and statistical Gabor descriptors for hESC investigates the statistical histogram info different orientations and two different win Gabor filter. It demonstrates that statistica are more accurate and reliable than histogram based features.
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