胰腺癌首次单片空间解析多组学整合：高通量蛋白质组学与全转录组分析

The increasing prevalence of incidentally diagnosed pre-malignant pancreatic Intraductal Papillary Mucinous Neoplasms (IPMNs), the cost of surveillance and the low rate of progression to cancer, underscores the importance of identifying IPMNs likely to progress to cancer. Currently available imaging and endoscopic tools cannot assess the complexity of IPMNs in their evolution to cancer. Understanding the spatial and molecular heterogeneity of pancreatic pre-malignant lesions is a critical strategy to enhance biological understanding, improve early detection and inform therapeutic strategies. Spatial transcriptomics offers the ability to robustly profile the spatial molecular landscape of IPMNs; however, cell type heterogeneity poses challenges. A same slide multiomic approach, combining high-plex proteomic and transcriptomic profiling, overcomes the limitations of transcriptionally defined cell type heterogeneity while preserving the exploration of cellular functionality through transcriptomic pathway and gene module analysis. A 64 plex protein panel followed by a whole transcriptome RNA panel using CosMx SMI (Bruker) was applied to a Tissue Microarray (TMA) of 40 x 1.5mm cores from patients with pancreatic cancer originating in IPMNs from various histological subtypes. Experimental time was 9 days to first visualisation of the data.  From the RNA analysis 340,069 cells were identified with a mean of 1,545 transcripts and 1,157 unique genes per cell. In the protein 416,766 cells were identified, expression analysis demonstrated a mean fluorescence intensity of 16,287.  After alignment of the decoded RNA transcripts to the co-ordinates of the protein data there were 412,680 cells across 335 fields of view, enabling multiomic integration within the same tissue regions. The analysis pipeline incorporated three distinct approaches for cell typing: RNA: 33 clusters RNA & Protein: 28 clusters Protein : 44 clusters Cell type annotation of the RNA was performed using a hybrid computational-manual approach. The top 20 most highly expressed genes and top 20 most differentially expressed genes per cluster were identified and provided to a large language model (Claude, Anthropic), generating cell types at three levels of granularity. All annotations were reviewed to ensure consistency with established pancreatic cell type markers. Proteins were clustered and cell-typed using differential expression analysis. Protein-based cell typing showed enhanced alignment with multiomic cell typing compared to RNA alone, demonstrating the value of multiomic data in validating RNA-based cell type annotations with protein data. This multiomic approach provides unprecedented resolution of cellular heterogeneity in IPMNs highlighting the complexity the pre-malignant state and establishes a framework for identifying early markers of malignant progression.