Spatial transcriptomics (ST) is a powerful approach for cancers molecular and cellular characterization. Pancreatic intraepithelial neoplasia (PanIN) is a pancreatic ductal adenocarcinoma (PDAC) premalignancy diagnosed from formalin-fixed and paraffin-embedded (FFPE) specimens limiting single-cell based investigations. We developed a new FFPE ST analysis protocol for PanINs complemented with novel transfer learning approaches. The first transfer learning approach, to assign cell types to ST spots and integrate the transcriptional signatures, shows that PanINs are surrounded by PDAC cancer associated fibroblasts (CAFs) subtypes, including the rare antigen-presenting CAFs. Furthermore, most PanINs are of the classical PDAC subtype while one sample expresses cancer stem cell markers. A second transfer learning approach, to integrate ST PanIN data with PDAC scRNA-seq data, identifies a shift between inflammatory and proliferative signaling as PanINs progress to PDAC. Our data support a model of inflammatory signaling and PanIN-CAF interactions promoting premalignancy progression and PDAC immunosuppressive characteristics.
The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.
We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal survival rates and a better understanding of PanINs biology will provide valuable insights for early therapeutic interventions. Thus, we used PanINs as our model system to implement the FFPE ST workflow. Our workflow for FFPE ST analysis facilitates sectioning of small regions (5mm in diameter) from a paraffin block that are stained and imaged with H&E and concurrently measured for genome-wide transcriptional profiling. Subsequently, the image is used for automated cell annotation using an algorithm, CODA, trained to identify normal and neoplastic pancreatic cell types. CODA identified the normal pancreatic histological regions (ducts, acini, islets of Langerhans, stroma), as well as the neoplastic cells. This automated analysis enables isolation of specific spots for differential expression analysis to pinpoint the transcriptional changes that occur within neoplastic cells along ducts in PanIn and their changes between high-grade and low-grade lesions. The spatial gene expression analysis identified clusters that mapped to the cell types annotated by CODA and the marker genes of each cluster matched known markers for the correspondent cell type. Although PanINs are very small in size (< 1mm), we found specific clusters accurately mapped to these lesions in each sample. Overall, the spatial sequencing data presented enough depth and complexity to allow differential expression and pathway analysis. We observed a significant number of deregulated genes in PanINs compared to normal ducts. Some deregulated genes are known PanIN markers, but potential new markers were also identified. Moreover, the integration of CODA with gene expression changes enables us to verify that unique stromal regions annotated with CODA and associated with PanIns are in fact heterogeneous and formed by distinct cell subtypes. Altogether, our workflow combining automated cell annotation with STA from the same section provides a methodology to precisely examine the sample architecture while measuring heterogeneity at the transcriptional level. This combined approach can be applied to different FFPE tumor types to leverage the use of large bioarchives of samples not previously accessible to genome-wide spatial methods. Citation Format: Alexander T. Bell, Kohei Fujikura, Jacob Stern, Rena Chan, James Chell, Stephen Williams, Ashley Kiemen, Elizabeth M. Jaffee, Denis Wirtz, Laura D. Wood, Elana J. Fertig, Luciane T. Kagohara. Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 637.
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