Targeted gene sanger sequencing should remain the first-tier genetic test for children suspected to have the five common X-linked inborn errors of immunity

DATA AVAILABILITY STATEMENT : The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.To address inborn errors of immunity (IEI) which were underdiagnosed in resource-limited regions, our centre developed and offered free genetic testing for the most common IEI by Sanger sequencing (SS) since 2001. With the establishment of The Asian Primary Immunodeficiency (APID) Network in 2009, the awareness and definitive diagnosis of IEI were further improved with collaboration among centres caring for IEI patients from East and Southeast Asia. We also started to use whole exome sequencing (WES) for undiagnosed cases and further extended our collaboration with centres from South Asia and Africa. With the increased use of Next Generation Sequencing (NGS), we have shifted our diagnostic practice from SS to WES. However, SS was still one of the key diagnostic tools for IEI for the past two decades. Our centre has performed 2,024 IEI SS genetic tests, with in-house protocol designed specifically for 84 genes, in 1,376 patients with 744 identified to have disease-causing mutations (54.1%). The high diagnostic rate after just one round of targeted gene SS for each of the 5 common IEI (X-linked agammaglobulinemia (XLA) 77.4%, Wiskott–Aldrich syndrome (WAS) 69.2%, X-linked chronic granulomatous disease (XCGD) 59.5%, X-linked severe combined immunodeficiency (XSCID) 51.1%, and X-linked hyper-IgM syndrome (HIGM1) 58.1%) demonstrated targeted gene SS should remain the first-tier genetic test for the 5 common X-linked IEI.The Hong Kong Society for Relief of Disabled Children and Jeffrey Modell Foundation.http://www.frontiersin.org/Immunologyam2023Paediatrics and Child Healt

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

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