Henry Löffler-Wirth

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules
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Single-cell transcriptomics has been used for analysis of heterogeneous populations of cells during developmental processes and for analysis of tumor cell heterogeneity. More recently, analysis of pseudotime (PT) dynamics of heterogeneous cell populations has been established as a powerful concept to study developmental processes. Here we perform PT analysis of 3 melanoma short-term cultures with different genetic backgrounds to study specific and concordant properties of PT dynamics of selected
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For this study, we screened prospectively recruited patients with a histopathological reference diagnosis of cerebral tumors of WHO grade II and III, known KPS at diagnosis, information on extent of resection by early postoperative neuroimaging, available frozen tissue specimens from the initial operation, and documented clinical outcome.

Molecular profiling of cerebral gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological
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We present an extensive and detailed study on the gene expression landscape of peripheral blood of healthy individuals based on transcriptome data of 3,388 individuals screened in the population study LIFE between 2011 and 2014. For analysis we applied a neural network technique using self-organizing maps (SOM). Our home-made R-program ‘oposSOM’ enables to reduce the dimension of expression data from tens of thousands of genes to a few thousand ‘meta-genes’ and generates portraits of transcriptional
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We present a transcriptome map of mature B-cell lymphomas that was obtained by analyzing expression data of 873 biopsy specimens including diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma (BL), mixed FL/DLBCL lymphomas, primary mediastinal large B-cell lymphoma (PMBL), multiple myeloma (MM), IRF4-rearranged large cell lymphoma, MYC-negative high-grade B-cell lymphomas with Chr. 11q aberration pattern (mnBLL-11q), and Mantle cell lymphoma (MCL) to describe their
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Analysis of large-scale molecular biological data using self-organizing maps

Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning
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