Here we report the derivation and characterization of new human embryonic stem cell (hESC) lines, SNUhES1, SNUhES2, and SNUhES3. These cells, established from the inner cell mass using an STO feeder layer, satisfy the criteria that characterize pluripotent hESCs: The cell lines express high levels of alkaline phosphatase, cell surface markers (such as SSEA-3, SSEA-4, TRA-1-60, and TRA-1-81), transcription factor Oct-4, and telomerase. When grafted into severe combined immunodeficient mice after prolonged proliferation, these cells maintained the developmental potentials to form derivatives of all three embryonic germ layers. The cell lines have normal karyotypes and distinct identities, revealed from DNA fingerprinting. Interestingly, analysis by electron microscopy clearly shows the morphological difference between undifferentiated and differentiated hESCs. Undifferentiated hESCs have a high ratio of nucleus to cytoplasm, prominent nucleoli, indistinct cell membranes, free ribosomes, and small mitochondria with a few crista, whereas differentiated cells retain irregular nuclear morphology, desmosomes, extensive cytoplasmic membranes, tonofilaments, and highly developed cellular organelles such as Golgi complex with secretory vesicles, endoplasmic reticulum studded with ribosomes, and large mitochondria. Existence of desmosomes and tonofilaments indicates that these cells differentiated into epithelial cells. When in vitro differentiation potentials of these cell lines into cardiomyocytes were examined, SNUhES3 was found to differentiate into cardiomyocytes most effectively.
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http://dx.doi.org/10.1634/stemcells.2004-0122 | DOI Listing |
NPJ Precis Oncol
January 2025
Zentalis Pharmaceuticals, Inc., San Diego, CA, USA.
Upregulation of Cyclin E1 and subsequent activation of CDK2 accelerates cell cycle progression from G1 to S phase and is a common oncogenic driver in gynecological malignancies. WEE1 kinase counteracts the effects of Cyclin E1/CDK2 activation by regulating multiple cell cycle checkpoints. Here we characterized the relationship between Cyclin E1/CDK2 activation and sensitivity to the selective WEE1 inhibitor azenosertib.
View Article and Find Full Text PDFSci Rep
January 2025
School of Medicine, Nankai University, Tianjin, 300071, China.
Cholangiocarcinoma (CCA), a highly aggressive form of cancer, is known for its high mortality rate. A Disintegrin and Metalloprotease Domain-like Protein Decysin-1 (ADAMDEC1) can promote the development and metastasis in various tumors by degrading the extracellular matrix. However, its regulatory mechanism in CCA remains unclear.
View Article and Find Full Text PDFAnn Hematol
January 2025
Department of Hematology, Navy Medical Center of PLA, Naval Medical University, No. 338 West Huaihai Road, Changning District, Shanghai, 200052, China.
Multiple myeloma(MM) remains incurable with high relapse and chemoresistance rates. Differentially expressed genes(DEGs) between newly diagnosed myeloma and secondary plasma cell leukemia(sPCL) were subjected to a weighted gene co-expression network analysis(WGCNA). Drug resistant myeloma cell lines were established.
View Article and Find Full Text PDFSci Rep
January 2025
School of Physics, Engineering and Technology, University of York, Heslington, York, YO10 5DD, UK.
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
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