4.7 Article

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Related references

Note: Only part of the references are listed.
Review Medicine, Research & Experimental

Commercialization and regulation of regenerative medicine products: Promises, advances and challenges

Nima Beheshtizadeh et al.

Summary: The article discusses the commercialization process, regulatory concerns, and immunological considerations in the field of regenerative medicine. It reviews commercially available engineered tissues and cell/gene therapeutic products. Furthermore, the article presents clinical applications and future perspectives for improving the regenerative medicine field.

BIOMEDICINE & PHARMACOTHERAPY (2022)

Article Cell & Tissue Engineering

Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning

Claudia Coronnello et al.

Summary: The emergence of iPSC technology offers the potential for therapeutic cell and organ production on demand, but there is a growing need for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Faster and more accurate methods for cell identity and function validation at different stages of the iPSC manufacturing process are in demand.

STEM CELL REVIEWS AND REPORTS (2022)

Article Reproductive Biology

Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network

Yajun Liu et al.

Summary: Utilizing stem cell differentiation models with deep learning technology accelerates research on early molecular events in human pregnancy. Automatic identification of trophoblast cells and stem cells can be achieved through transfer learning techniques and convolutional neural networks.

REPRODUCTIVE TOXICOLOGY (2021)

Article Clinical Neurology

Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells

Keiko Imamura et al.

Summary: This study developed an ALS prediction model using artificial intelligence and iPSC technology, achieving high accuracy in analyzing images of spinal motor neurons, which may support the diagnosis and potential treatment of ALS through future prospective research.

ANNALS OF NEUROLOGY (2021)

Article Biology

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status

Tobias Piotrowski et al.

Summary: This paper presents a method for fully automating cell state recognition using phase contrast microscopy and deep learning, for in-process control during automated hiPSC cultivation. The algorithm is capable of accurately segmenting important parameters of hiPSC colony formation and discriminating between different classes of cells. It provides localized information about the cell state and enables well-based treatment of the cell culture in automated processes.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Biochemical Research Methods

Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells

Zhengyun Zhang et al.

Summary: A deep-learning-based processing pipeline is proposed for accurately locating and characterizing cell nuclei without the need for invasive staining or ultraviolet radiation. Detailed information on the design, construction, and validation of the deep-learning pipeline is provided, making it easy to adapt to different cell types.

BIOMEDICAL OPTICS EXPRESS (2021)

Review Biochemistry & Molecular Biology

Role of the constitutive androstane receptor (CAR) in human liver cancer

Sarah Da Won Bae et al.

Summary: CAR is a receptor that is predominantly expressed in the liver and interacts with key signaling pathways related to drug, energy, and bilirubin metabolism. While studies in animal models suggest a potential role of CAR in tumorigenesis, recent research has shown species differences and a possible tumor-suppressive role of CAR in liver cancer in humans. This review highlights the need for further exploration of CAR's role in human diseases, particularly cancers, with a focus on its emerging functions in liver cancer.

BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER (2021)

Article Biochemical Research Methods

Human embryonic stem cell classification: random network with autoencoded feature extractor

Benjamin X. Guan et al.

Summary: This study developed an effective model using deep learning methods for hESC video classification, achieving an accuracy of 97.23% and outperforming existing methods. The approach has low training cost and can save significant manual annotation time.

JOURNAL OF BIOMEDICAL OPTICS (2021)

Article Cell & Tissue Engineering

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

David A. Joy et al.

Summary: Through the use of neural networks to track individual hiPSC cells, it was found that while individual cell parameters are not strongly affected by pluripotency maintenance conditions or morphogenic cues, regional changes in cell behavior can predict cell fate and colony organization. This tracking pipeline enables a detailed understanding of morphogenesis by elucidating the role of regional behavior in early tissue formation.

STEM CELL REPORTS (2021)

Article Biology

Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes

Francis Grafton et al.

Summary: Drug-induced cardiotoxicity and hepatotoxicity are leading causes of drug attrition. This study utilized high-content image analysis and iPSC-CMs to rapidly detect patterns of cardiotoxicity, including DNA intercalators, ion channel blockers, and multi-kinase inhibitors. The combination of deep learning and iPSC technology can effectively reduce the risk of early-stage drug discovery.

ELIFE (2021)

Article Cell & Tissue Engineering

Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking

Takuya Hirose et al.

Summary: DeepACT is a deep learning-based automated cell tracking technology for quality control and identification of cultured human stem cells. It can analyze cell motion and spatial information to evaluate keratinocyte dynamics accurately.

STEM CELLS (2021)

Article Biochemistry & Molecular Biology

Characterization of Human Induced Pluripotent Stem Cell-Derived Hepatocytes with Mature Features and Potential for Modeling Metabolic Diseases

Gustav Holmgren et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

Article Pharmacology & Pharmacy

Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method

Mahnaz Maddah et al.

JOURNAL OF PHARMACOLOGICAL AND TOXICOLOGICAL METHODS (2020)

Article Cell & Tissue Engineering

Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation

Ariel Waisman et al.

STEM CELL REPORTS (2019)

Article Pharmacology & Pharmacy

Deep learning-based quality control of cultured human-induced pluripotent stem cell-derived cardiomyocytes

Ken Orita et al.

JOURNAL OF PHARMACOLOGICAL SCIENCES (2019)

Review Biochemical Research Methods

Deep learning for cellular image analysis

Erick Moen et al.

NATURE METHODS (2019)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Multidisciplinary Sciences

Tissue-based map of the human proteome

Mathias Uhlen et al.

SCIENCE (2015)