2021
DOI: 10.1101/2021.07.05.450633
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Reticular Dysgenesis-associated Adenylate Kinase 2 deficiency causes failure of myelopoiesis through disordered purine metabolism

Abstract: Reticular Dysgenesis is a particularly grave from of severe combined immunodeficiency (SCID) that presents with severe congenital neutropenia and a maturation arrest of most cells of the lymphoid lineage. The disease is caused by biallelic loss of function mutations in the mitochondrial enzyme Adenylate Kinase 2 (AK2). AK2 mediates the phosphorylation of adenosine monophosphate (AMP) to adenosine diphosphate (ADP) as substrate for adenosine triphosphate (ATP) synthesis in the mitochondria. Accordingly, it has … Show more

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“…We initially categorized the major cell groups (immune, epithelium, and stromal cells) based on the expression levels of conventional cell markers (immune: PTPRC/CD45 for pan-immune cells; CD3E/G for T cells; CD79A for B cells; CD68 for myeloid cells; EPCAM, KRT8/18/19 for epithelia cells; and PECAM, ACTA2, MYL9 and MYLK for stromal cells). For immune cell annotations, we employed machine learning support vector machines (SVM) 39,40 , we integrated, well-annotated single-cell datasets from various publications: (GSE179346 45 ; E-MTAB-8884, E-MTAB-9139 (https://www.ebi.ac.uk/arrayexpress/), GSE175604 46 , GSE120221 47 , GSE193138 48 , GSE159929 49 , GSE181989 50 , GSE159624 51 , GSE130430 52 , GSE139369 53 , GSE165645 54 , GSE128639 55 , GSE185381 56 , GSE166895 57 , GSE133181 58 , GSE135194 59 , resulting in a reference dataset comprising over 670,000 immune cells and encompassing more than 40 cell types and cellular states ( Table S2 ). Following cell type predictions in our dataset using the trained references, we conducted manual validation of classical cell marker expression 60 .…”
Section: Star Methodsmentioning
confidence: 99%
“…We initially categorized the major cell groups (immune, epithelium, and stromal cells) based on the expression levels of conventional cell markers (immune: PTPRC/CD45 for pan-immune cells; CD3E/G for T cells; CD79A for B cells; CD68 for myeloid cells; EPCAM, KRT8/18/19 for epithelia cells; and PECAM, ACTA2, MYL9 and MYLK for stromal cells). For immune cell annotations, we employed machine learning support vector machines (SVM) 39,40 , we integrated, well-annotated single-cell datasets from various publications: (GSE179346 45 ; E-MTAB-8884, E-MTAB-9139 (https://www.ebi.ac.uk/arrayexpress/), GSE175604 46 , GSE120221 47 , GSE193138 48 , GSE159929 49 , GSE181989 50 , GSE159624 51 , GSE130430 52 , GSE139369 53 , GSE165645 54 , GSE128639 55 , GSE185381 56 , GSE166895 57 , GSE133181 58 , GSE135194 59 , resulting in a reference dataset comprising over 670,000 immune cells and encompassing more than 40 cell types and cellular states ( Table S2 ). Following cell type predictions in our dataset using the trained references, we conducted manual validation of classical cell marker expression 60 .…”
Section: Star Methodsmentioning
confidence: 99%