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Bottcher, A.

Publications and source records attributed to Bottcher, A..

2 recordsLinked to original sources

Infrared Laser Sampling of Low Volumes Reveals Marker Lipids in Palatine Tonsil Carcinoma via Shotgun Lipidomics

Complete surgical resection is essential for oropharyngeal squamous cell carcinoma (OPSCC) therapy, underscoring the need for improved intraoperative margin assessment. To advance in-vivo diagnostics of OPSCC, Nanosecond infrared laser (NIRL) tissue sampling combined with shotgun lipidomic analysis reveals lipidome differences between OPSCC tissue and adjacent healthy tissue. Ablations were performed on tonsil squamous cell carcinoma on n=28 samples from 11 patients with an established chamber setup and a subset of n=6 samples from three patients with a custom-made handheld applicator. Welchs t-test results (p=0.05, two-fold change) revealed a similar OPSCC lipid profile in 7 of 11 patients. Potential tumor lipid markers were identified as consistently significantly increased, despite biological heterogeneity of the samples, underscoring their potential diagnostic value. Tissue ablation with a custom-made handheld applicator coupled to a laser fiber was successful and the lipidomic analysis was consistent to the chamber setup. Although our setup is currently limited by an ablation time exceeding one minute, this study demonstrates the potential of the handheld applicator for future applications such as endoscopy or intraoperative diagnostics.

cancer biology↗

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Multiple pancreatic islet single-cell RNA sequencing (scRNA-seq) datasets have been generated to study development, homeostasis, and diabetes. However, there is no consensus on cell states and pathways across conditions as well as the value of preclinical mouse models. Since these challenges can only be resolved by jointly analyzing multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including an autoimmune type 1 diabetes (T1D) model (NOD), a gluco-/lipotoxicity T2D model (db/db), and a chemical streptozotocin (STZ) {beta}-cell ablation model. MIA is a curated resource for interactive exploration and computational querying, providing new insights inaccessible from individual datasets. The {beta}-cell landscape of MIA revealed new disease progression cell states and cross-publication differences between previously suggested marker genes. We show that in the STZ model {beta}-cells transcriptionally correlate to human T2D and mouse db/db, but are less similar to human T1D and mouse NOD. We observe different pathways shared between immature, aged, and diabetes model {beta}-cells. In conclusion, our work presents the first comprehensive analysis of {beta}-cell responses to different stressors, providing a roadmap for the understanding of {beta}-cell plasticity, compensation, and demise.

bioinformatics↗