Spatial transcriptomics (ST) is an emerging technology that can add spatial information of gene expression to traditional histopathological images, thereby, has the potential to revolutionise our current understandings of tissue biology, especially cancer tissue. However, a vast amount of unexplored archival cancer tissue samples are formalin-fixed paraffin-embedded (FFPE).Making the best use of the precious cancer tissue resource would make breakthroughs needed in understanding the cancer cellular environment and how the diverse cells can be used as markers to inform diagnosis, treatment regimes and to predict outcomes. Notably, the measurement of gene expression from these tissues has been highly challenging due to several critical technical issues. While there have recently several protocols released been to start enabling researchers to perform spatial profiling for FFPE samples, it is still not well known how these protocols perform across various tissue types, archival time, storage types, and tissue quality. Moreover, for many projects, the ability to detect genes without relying on a pre-defined set of probes is an important, unmet requirement.
In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. These are among the most challenging tissue types to perform ST due to their fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from different years ago, spanning a range of different tissue qualities and complexity. Samples from patients with different cancer types, and cancer stages were compared. Further, we overlaid gene expression profiles with pathological information, revealing a new layer of molecular information that can reveal gene markers unique for cancer cells, locations and stages. Together, this work provides important technical perspectives to enable the applications of ST on cancer tissues. In addition, we present a pipeline for integrating molecular transcriptomics with histopathological analysis in cancer research and clinical applications