ArticlePloS one2016
Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 31 citations in OpenAlex.
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- Menthol, a consumer product additive, adversely affects human embryonic stem cells via activation of TRPM8 and TRPA1 channels.Stem cells translational medicine · 2025Article
- Live Cell Imaging of Bone Cell and Organ Cultures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Iterative pseudo balancing for stem cell microscopy image classification.Scientific reports · 2024Article
- Disposable Puff Bar Electronic Cigarettes: Chemical Composition and Toxicity of E-liquids and a Synthetic Coolant.Chemical research in toxicology · 2022Article
- Generative Adversarial Networks for Morphological-Temporal Classification of Stem Cell Images.Sensors (Basel, Switzerland) · 2021Article
- A preview of selected articles-September 2021.Stem cells translational medicine · 2021Article
- Video bioinformatics analysis of human pluripotent stem cell morphology, quality, and cellular dynamics.Stem cells translational medicine · 2021Article
- Review
- DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.PloS one · 2019Article
- Optical-flow based non-invasive analysis of cardiomyocyte contractility.Scientific reports · 2017Article
- Adverse Health Effects of Thirdhand Smoke: From Cell to Animal Models.International journal of molecular sciences · 2017Review
- Thirdhand Smoke: New Evidence, Challenges, and Future Directions.Chemical research in toxicology · 2017Article
- Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.PloS one · 2017Article
- Distribution, quantification and toxicity of cinnamaldehyde in electronic cigarette refill fluids and aerosols.Tobacco control · 2016Article
- Noninvasive measurement of cell/colony motion using image analysis methods to evaluate the proliferative capacity of oral keratinocytes as a tool for quality control in regenerative medicine.Journal of tissue engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.