Microdissection—An Essential Prerequisite for Spatial Cancer Omics
Ferdinand von Eggeling, Franziska S. Hoffmann
Abstract
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Ferdinand von Eggeling, Franziska S. Hoffmann
Abstract
Open-access reader
The problem with cancer tissue is that its intratumoral heterogeneity and its complexity is extremely high as cells possess, depending on their location and function, different mutations, different mRNA expression and the highest intricacy in the protein pattern. Prior to genomic and proteomic analyses, it is therefore indispensable to identify the exact part of the tissue or even the exact cell. Laser-based microdissection is a tried and tested technique able to produce pure and well-defined cell material for further analysis with proteomic and genomic techniques. It sheds light on the heterogeneity of cancer or other complex diseases and enables the identification of biomarkers. This review aims to raise awareness for the reconsideration of laser-based microdissection and seeks to present current state-of-the-art combinations with omic techniques.
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The problem with cancer tissue is that its intratumoral heterogeneity and its complexity is extremely high as cells possess, depending on their location and function, different mutations, different mRNA expression and the highest intricacy in the protein pattern. Prior to genomic and proteomic analyses, it is therefore indispensable to identify the exact part of the tissue or even the exact cell. Laser-based microdissection is a tried and tested technique able to produce pure and well-defined cell material for further analysis with proteomic and genomic techniques. It sheds light on the heterogeneity of cancer or other complex diseases and enables the identification of biomarkers. This review aims to raise awareness for the reconsideration of laser-based microdissection and seeks to present current state-of-the-art combinations with omic techniques.
Key concepts: Laser capture microdissection, Microdissection, Computational biology, Proteomics, Biology, Identification (biology), Genomics, Cancer