Variants in the pancreatic CUB and zona pellucida-like domains 1 (CUZD1) gene in early-onset chronic pancreatitis - A possible new susceptibility gene

Rygiel, Agnieszka Magdalena; Unger, Lara Sophie; Sörgel, Franziska Lena; Masson, Emmanuelle; Matsumoto, Ryotaro; Ewers, Maren; Chen, Jianmin; Bugert, Peter; Buscail, Louis; Gambin, Tomasz; Oracz, Grzegorz; Winiewska-Szajewska, Maria; Mianowska, A.; Poznański, Jarosław; Kosińska, Joanna; Stawiński, Piotr; Płoski, Rafał; Kozieł, Dorota; Głuszek, Stanisław; Laumen, Helmut; Lindgren, Fredrik; Löhr, Matthias; Orekhova, Anna; Rebours, Vinciane; Rosendahl, Jonas; Párniczky, Andrea; Hegyi, Péter; Sasaki, Akira; Kataoka, Fumiya; Tanaka, Yu; Hamada, Shin; Sahin–Tóth, Miklós; Hegyi, E; Férec, Claude; Masamune, Atsushi; Witt, Heiko

doi:10.1016/j.pan.2022.04.015

Cited by 4 publications

(2 citation statements)

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“…FindClusters was used to identify clusters of cells by a shared nearest neighbours modularity optimisation based on original Louvain clustering algorithms with a resolution of .2. Cell clusters annotated with marker genes are acinar cell ( CUZD1 25 ), acinar to ductal metaplasia (ADM) cell ( ONECUT1 26 ), ductal cell ( KRT19 11,15 ), endocrine cell ( PAX6 27 ), endothelial cell ( TEK 28 ), fibroblast ( COL1A2 29 ), myeloid cell ( CD163 , 12 CSF2RA 30 ) and T cell ( IL7R 11 ) (Table S1). After annotation, an extended list of marker genes was predicted for each cell type against all other cell types through FindAllMarkers with ‘wilcox’ test and logfc.threshold as .25 (Table S1).…”

Section: Methodsmentioning

confidence: 99%

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Analysis of single nuclear chromatin accessibility reveals unique myeloid populations in human pancreatic ductal adenocarcinoma

Pratt,

Ma,

Dziadowicz

et al. 2024

Clinical & Translational Med

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BackgroundA better understanding of the pancreatic ductal adenocarcinoma (PDAC) immune microenvironment is critical to developing new treatments and improving outcomes. Myeloid cells are of particular importance for PDAC progression; however, the presence of heterogenous subsets with different ontogeny and impact, along with some fluidity between them, (infiltrating monocytes vs. tissue‐resident macrophages; M1 vs. M2) makes characterisation of myeloid populations challenging. Recent advances in single cell sequencing technology provide tools for characterisation of immune cell infiltrates, and open chromatin provides source and function data for myeloid cells to assist in more comprehensive characterisation. Thus, we explore single nuclear assay for transposase accessible chromatin (ATAC) sequencing (snATAC‐Seq), a method to analyse open gene promoters and transcription factor binding, as an important means for discerning the myeloid composition in human PDAC tumours.MethodsFrozen pancreatic tissues (benign or PDAC) were prepared for snATAC‐Seq using 10× Chromium technology. Signac was used for preliminary analysis, clustering and differentially accessible chromatin region identification. The genes annotated in promoter regions were used for Gene Ontology (GO) enrichment and cell type annotation. Gene signatures were used for survival analysis with The Cancer Genome Atlas (TCGA)‐pancreatic adenocarcinoma (PAAD) dataset.ResultsMyeloid cell transcription factor activities were higher in tumour than benign pancreatic samples, enabling us to further stratify tumour myeloid populations. Subcluster analysis revealed eight distinct myeloid populations. GO enrichment demonstrated unique functions for myeloid populations, including interleukin‐1b signalling (recruited monocytes) and intracellular protein transport (dendritic cells). The identified gene signature for dendritic cells influenced survival (hazard ratio = .63, p = .03) in the TCGA‐PAAD dataset, which was unique to PDAC.ConclusionsThese data suggest snATAC‐Seq as a method for analysis of frozen human pancreatic tissues to distinguish myeloid populations. An improved understanding of myeloid cell heterogeneity and function is important for developing new treatment targets in PDAC.

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Section: Methodsmentioning

confidence: 99%

“…FindAllMarkers was used to identify the differentially accessible regions for each cluster by against all other clusters, by setting 'LR' as the test used, peak_region_fragments as the test variable, and logfc.threshold (fold change in log2 scale) as . 25.…”

Section: Snatac-seq Data Pre-processing and Clustering Analysismentioning

confidence: 99%