Project description:Fresh resected human lung tissue (parenchymal lung and distal airway specimens) was obtained via the CPC BioArchive at the Comprehensive Pneumology Center Munich (CPC-M, Munich, Germany) and profiled using single cell RNA sequencing technology (Drop-seq). In total, we analysed parenchymal tissue of uninvolved areas of tumour resection material from four patients.
Project description:Fresh resected lung tissues were obtained from six tuberculosis patients with elevated pulmonary 18F-FDG avidity. Lung tissues with 18F-FDG avidity and nearby uninvolved tissues were profiled with single-cell RNA sequencing.
Project description:Fresh resected lung tissues were obtained from nine tuberculosis patients with elevated pulmonary 18F-FDG avidity. Lung tissues with 18F-FDG avidity and nearby uninvolved tissues were profiled with mRNA sequencing.
Project description:Fresh resected lung tissues were obtained from nine tuberculosis patients with elevated pulmonary 18F-FDG avidity. Lung tissues with 18F-FDG avidity and nearby uninvolved tissues were profiled with miRNA sequencing.
Project description:Lung cancer is the leading cause of cancer-related deaths world-wide. ~85% of lung carcinomas are non–small cell lung carcinoma (NSCLC). Tumor cell heterogeneity is very poorly defined. However, it is known to be important for tumor response to cancer therapy and cancer agressivenes. We subjected three NSCLC tumors resected from different patients to Drop-seq in order to 1) elucidate the capability of scRNA-seq analysis in identifying different tumor cell populations; and 2) ascertain the clinical value of the genes which distinguish cancer cells from other cells in the tissue. As anticipated, the tissue composition of independently collected samples varied. Despite deficient populations in some samples, both donor and patient samples contributed to the majority of cell populations. However, cancer cells were all patient-specific. These findings emphasize the utility of single cell gene expression data in identification of tumor cell populations. The collected data might be further used for predicting of drugs specific to the biology of activated pathways and patient outcome.