Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor.

Circulating tumor cells (CTCs) that undergo epithelial-to-mesenchymal transition (EMT) can provide valuable information regarding metastasis and potential therapies. However, current studies on the EMT overlook alternative splicing. Here, we used single-cell full-length transcriptome data and mRNA sequencing of CTCs to identify stage-specific alternative splicing of partial EMT and mesenchymal states during pancreatic cancer metastasis. We classified definitive tumor and normal epithelial cells via genetic aberrations and demonstrated dynamic changes in the epithelial-mesenchymal continuum in both epithelial cancer cells and CTCs. We provide the landscape of alternative splicing in CTCs at different stages of EMT, uncovering cell-type-specific splicing patterns and splicing events in cell surface proteins suitable for therapies. We show that MBNL1 governs cell fate through alternative splicing independently of changes in gene expression and affects the splicing pattern during EMT. We found a high frequency of events that contained multiple premature termination codons and were enriched with C and G nucleotides in close proximity, which influence the likelihood of stop codon readthrough and expand the range of potential therapeutic targets. Our study provides insights into the EMT transcriptome's dynamic changes and identifies potential diagnostic and therapeutic targets in pancreatic cancer.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

A pan-cancer analysis of alternative splicing of splicing factors in 6904 patients.

Great progress has been made in the investigation on mutation and expression of splicing factor. However, little is known on the role of alternative splicing of splicing factors across cancers. Here, we reported a pan-cancer analysis of alternative splicing of splicing factors spanning 6904 patients across 16 cancer types, and identified 167 splicing factors with implications regulating cancer-specific splicing patterns through alternative splicing. Furthermore, we found that abnormal splicing events of splicing factors could serve as potential common regulators for alternative splicing in different cancers. In addition, we developed a splicing-derived neoepitopes database (ASPNs), which provided the corresponding putative alternative splicing-derived neoepitopes of 16 cancer types. Our results suggested that alternative splicing of splicing factors involved in the pre-RNA splicing process was common across cancer types and may represent an underestimated hallmark of tumorigenesis.