Baretton, G. | Search bioRxiv

bioRxiv2022

Identification of a unique transcriptomic signature associated with islet amyloidosis

AimsThis cross-sectional study aims to identify potential transcriptomic changes conveyed by presence of amyloid deposits in islets from pancreatic tissue obtained from metabolically profiled living donors. MethodsAfter establishing Thioflavin S as the most sensitive approach to detect islet amyloid plaques, we utilized RNA sequencing data obtained from laser capture microdissected islets to define transcriptomic effects of this pathological entity. The RNA sequencing data was used to identify differentially expressed genes by linear modeling. Further analyses included functional enrichment analysis of KEGG and Hallmark gene sets as well as a weighted gene correlation network analysis. ResultsEleven differentially expressed genes were identified in islets affected by amyloidosis. Enrichment analyses pointed to signatures related to protein aggregation diseases, energy metabolism and inflammatory response. A gene co-expression module was identified that correlated to islet amyloidosis. ConclusionAlthough the influence of underlying Type 2 diabetes could not be entirely excluded, this study presents a valuable insight into the biology of islet amyloidosis, particularly providing hints into the potential relationship between islet amyloid deposition and structural and functional proteins involved in insulin secretion. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIIslet amyloidosis is the only histological marker of Type 2 diabetes in the pancreas C_LIO_LIIndividuals not suffering from Type 2 diabetes can also be affected by islet amyloidosis C_LIO_LIThe clinicopathological significance of this phenomenon is still unclear C_LI What is the key question?O_LIDoes the islet transcriptome of individuals with islet amyloidosis provide explanations for the onset of this phenomenon and its pathophysiological value? C_LI What are the new findings?O_LIIslet transcriptomes of affected subjects exhibit only limited transcriptomic differences compared to unaffected ones. C_LIO_LIStructural and functional proteins involved in insulin secretion machinery may be involved in the pathophysiological sequence of amyloid formation C_LI

systems biology↗

bioRxiv2018

Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues

The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and FISH images on a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.

pathology↗