Generation of induced pluripotent stem cells from a patient with Best Dystrophy carrying 11q12.3 (BEST1 (VMD2)) mutation

Hsu, Chih Chien; Lu, Huai En; Chuang, Jen Hua; Ko, Yu Ling; Tsai, Yi Ching; Tai, Hung-Chi; Yarmishyn, Aliaksandr A.; Hwang, De‐Kuang; Wang, Mong‐Lien; Yang, Yi; Chen, Shih‐Jen; Peng, Chi Hsien; Chiou, Shih Hwa; Lin, Tzu‐Chin

doi:10.1016/j.scr.2018.03.019

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Induced pluripotent stem cell (iPSC) technologies have been shown to hold great promise for personalized therapy, translation medicine, and regeneration medicine [1]. Owing to the potential of iPSCs to be generated from somatic cells via the transduction of specific transcription factors [2], scientists reprogrammed patient-derived somatic cells to generate patient-specific iPSCs [3][4][5]. Remarkably, silencing the expression of human leukocyte antigen (HLA) class I allows to establish the universal human leukocyte antigen 2 of 18 (HLA) iPSCs characterized by low immunogenicity [6].…”

Section: Introductionmentioning

confidence: 99%

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Lien

Chen

Tsai

et al. 2023

Cells

Self Cite

View full text Add to dashboard Cite

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.

show abstract

Section: Introductionmentioning

confidence: 99%

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Lien

Chen

Tsai

et al. 2023

Cells

Self Cite

View full text Add to dashboard Cite

show abstract

Altered transcriptome and disease-related phenotype emerge only after fibroblasts harvested from patients with age-related macular degeneration are differentiated into retinal pigment epithelium

Cai

Gong

Noggle

et al. 2021

Experimental Eye Research

View full text Add to dashboard Cite

Human iPSC banking: barriers and opportunities

et al. 2019

View full text Add to dashboard Cite

The introduction of induced pluripotent stem cells (iPSCs) has opened up the potential for personalized cell therapies and ushered in new opportunities for regenerative medicine, disease modeling, iPSC-based drug discovery and toxicity assessment. Over the past 10 years, several initiatives have been established that aim to collect and generate a large amount of human iPSCs for scientific research purposes. In this review, we compare the construction and operation strategy of some iPSC banks as well as their ongoing development. We also introduce the technical challenges and offer future perspectives pertaining to the establishment and management of iPSC banks.

show abstract

Generation of induced pluripotent stem cells from a patient with Best Dystrophy carrying 11q12.3 (BEST1 (VMD2)) mutation

Cited by 3 publications

References 2 publications

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Altered transcriptome and disease-related phenotype emerge only after fibroblasts harvested from patients with age-related macular degeneration are differentiated into retinal pigment epithelium

Human iPSC banking: barriers and opportunities

Contact Info

Product

Resources

About