Revolutionary developments in high-throughput “-omics” measurements allow researchers to address biological questions that were previously out of reach. Next-generation sequencing (NGS) technologies and mass spectrometry-based proteomics have become standard procedure to analyse gene and protein expression levels in biological systems. These measurements can be conducted on a bulk level to provide the average levels in cell populations or on a single-cell level to visualize inherently heterogeneous cellular populations. For example, single-cell high-throughput genomic measurements such as single-cell RNA sequencing and single nucleus RNA-Seq can dissect the cellular heterogeneity of brain cells, and elucidate their specific functions and states even from archived clinical samples. In addition to transcriptome, single-cell methods can robustly decipher the genomes and the epigenomes with methods like single-cell ATAC-Seq, which measures the accessible genome of individual cells. Injury to the CNS triggers heterogeneous responses in all cell types of CNS which can be characterized in detail using both bulk and single-cell genomic measurements. In order to centralize and integrate our expertise in genomics and provide experimental and computational support to all projects, we formed a Genomics and Bioinformatics Platform. Within this platform we support our entire consortium from experimental design to bioinformatics data analysis for genomic methods, which include bulk genomic analysis and single-cell methods such as scRNAseq, snRNASeq, single-cell ATAC-Seq and as well as CITE-seq (combined measurements on the same cell for transcriptomes and epitopes). For each project, we are involved in the experimental design, together with the PIs, and determine suitable analytical equipment and technologies. These technologies include drop-seq (10X Genomics), flow-cytometry based isolation and laser capture microdissection methods for genomics analysis. The platform can provide training to members on experimental design, sample preparation and analysis to generate high-quality data. By centralizing sequencing library generation and analysis, we provide optimized methods for rapid turnaround and high-quality data. Following sequencing, collaborators can receive a standardized in-depth analysis package and the raw sequencing data. If non-standard analysis tasks are required, we can collaborate with members to develop specialized analysis workflows, which will include integrative analysis of genomic methods with non-genomic data such as mass spectrometry-based proteomics and lipidomics. In addition, we assist wet-lab researchers with the mining of genomic and non-genomic datasets. This not only allows our scientists to obtain high quality and reproducible data but also helps them to share and compare datasets from different projects and published studies. Our platform works in close collaboration with the single-cell transcriptomics hub of the Munich Cluster of Systems Neurology (SyNergy).
