Extending the landscape of omics technologies by pathomics

作者：Roman David Bülow, David Laurin Hölscher, Ivan G. Costa, Peter Boor · 发表于：npj Systems Biology and Applications · 年份：2023 · DOI：10.1038/s41540-023-00301-9 · 被引用次数：78 · 研究领域：Genetics, Bioinformatics, and Biomedical Research、Biomedical and Engineering Education、Bioinformatics and Genomic Networks

Tissue analysis is vital for investigating disease mechanisms and guiding diagnostics, e.g., in cancer, communicable or non-communicable diseases. During the last decades, technological developments enabled deep molecular characterization of tissue samples. This is particularly driven by omics approaches such as genomics, transcriptomics, proteomics, metabolomics, etc. (Fig. 1 ) 1 . Omics aims to (quantitatively) analyze possibly all molecules of a specific type in a specimen, the proteome, transcriptome, metabolome, etc. The omics analyses are enabled by specific methods, e.g., genomics by large-throughput DNA sequencing termed Next Generation Sequencing (NGS; Fig. 1 ). Typically, results from omics analyses contain large numbers of features, e.g., expression of genes, from a large number of instances, e.g., cells. These results allow complex downstream analyses, e.g., uncovering regulatory networks 2 , cell transitions 3 , or key molecular disease drivers 4 . These approaches were missing important information on the spatial organization and structure of the analyzed tissues and organs. Recent approaches allow the integration of the relative position of the investigated instance, most commonly honeycomb-shaped tissue areas, within a given sample using spatial transcriptomics (for example, spatially resolved transcript amplicon readout mapping (STARmap) or NGS barcoding techniques) 5 or spatial proteomics (e.g., multiplexed antibody-based imaging methods: multi-epitope-ligan...