SpatialDeX is a Reference-Free Method for Cell Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors.

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multi-cell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell type-specific spatial organization. To this end, we developed SpatialDeX, a regression model-based method for estimating cell type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring scRNA-seq references.

mCAUSE: Prioritizing mitochondrial targets that alleviate pancreatic cancer cell phenotypes.

Substantial changes in energy metabolism are a hallmark of pancreatic cancer. To adapt to hypoxic and nutrient-deprived microenvironments, pancreatic cancer cells remodel their bioenergetics from oxidative phosphorylation to glycolysis. This bioenergetic shift makes mitochondria an Achilles' heel. Since mitochondrial function remains essential for pancreatic cancer cells, further depleting mitochondrial energy production is an appealing treatment target. However, identifying effective mitochondrial targets for treatment is challenging. Here, we developed an approach, mitochondria-targeted cancer analysis using survival and expression (mCAUSE), to prioritize target proteins from the entire mitochondrial proteome. Selected proteins were further tested for their impact on pancreatic cancer cell phenotypes. We discovered that targeting a dynamin-related GTPase, OPA1, which controls mitochondrial fusion and cristae, effectively suppresses pancreatic cancer activities. Remarkably, when combined with a mutation-specific KRAS inhibitor, OPA1 inhibition showed a synergistic effect. Our findings offer a therapeutic strategy against pancreatic cancer by simultaneously targeting mitochondria dynamics and KRAS signaling.

Pan-cancer discovery of somatic mutations from RNA sequencing data.

Identification of somatic mutations (SMs) is essential for characterizing cancer genomes. While DNA-seq is the prevalent method for identifying SMs, RNA-seq provides an alternative strategy to discover tumor mutations in the transcribed genome. Here, we have developed a machine learning based pipeline to discover SMs based on RNA-seq data (designated as RNA-SMs). Subsequently, we have conducted a pan-cancer analysis to systematically identify RNA-SMs from over 8,000 tumors in The Cancer Genome Atlas (TCGA). In this way, we have identified over 105,000 novel SMs that had not been reported in previous TCGA studies. These novel SMs have significant clinical implications in designing targeted therapy for improved patient outcomes. Further, we have combined the SMs identified by both RNA-seq and DNA-seq analyses to depict an updated mutational landscape across 32 cancer types. This new online SM atlas, OncoDB ( https://oncodb.org ), offers a more complete view of gene mutations that underline the development and progression of various cancers.

Characteristics of Human and Microbiome RNA Profiles in Saliva.

Saliva is a convenient non-invasive source of liquid biopsy to monitor human health and diagnose diseases. In particular, extracellular vesicles (EVs) in saliva can potentially reveal clinically relevant information for systemic health. Recent studies have shown that RNA in saliva EVs could be exploited as biomarkers for disease diagnosis. However, there is no standardized protocol for profiling RNA in saliva EV nor clear guideline on selecting saliva fractions for biomarker analysis. To address these issues, we established a robust protocol for small RNA profiling from fractionated saliva. With this method, we performed comprehensive small RNA sequencing of four saliva fractions, including cell-free saliva (CFS), EV-depleted saliva (EV-D), exosome (EXO), and microvesicle (MV) from ten healthy volunteers. By comparing the expression profiles of total RNA from these fractions, we found that MV was most enriched in microbiome RNA (76.2% of total reads on average), whereas EV-D was notably enriched in human RNA (70.3% of total reads on average). As for human RNA composition, CFS and EV-D were both enriched in snoRNA and tRNA compared with the two EV fractions (EXO and MV, P < 0.05). Interestingly, EXO and MV had highly correlated expression profiles for various noncoding RNAs such as miRNA, tRNA, and yRNA. Our study revealed unique characteristics of circulating RNAs in various saliva fractions, which provides a guideline on preparing saliva samples to study specific RNA biomarkers of interest.

OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer.

Large-scale multi-omics datasets, most prominently from the TCGA consortium, have been made available to the public for systematic characterization of human cancers. However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at http://oncodb.org.

Comprehensive profiling of extracellular RNA in HPV-induced cancers using an improved pipeline for small RNA-seq analysis.

Extracellular RNAs (exRNAs) have attracted great attention due to their essential role in cell-to-cell communication as well as their potential as non-invasive disease biomarkers. However, at present, there is no consensus on the best method to profile exRNA expression, which leads to significant variability across studies. To address this issue, we established an experimental pipeline for comprehensive profiling of small exRNAs isolated from cell culture. By evaluating six RNA extraction protocols, we developed an improved method for robust recovery of vesicle-bound exRNAs. With this method, we performed small RNA sequencing of exosomes (EXOs), microvesicles (MVs) and source cells from 14 cancer cell lines. Compared to cells, EXOs and MVs were similarly enriched in tRNAs and rRNAs, but depleted in snoRNAs. By miRNA profiling analysis, we identified a subset of miRNAs, most noticeably miR-122-5p, that were significantly over-represented in EXOs and MVs across all 14 cell lines. In addition, we also identified a subset of EXO miRNAs associated with cancer type or human papillomavirus (HPV) status, suggesting their potential roles in HPV-induced cancers. In summary, our work has laid a solid foundation for further standardization on exRNA analysis across various cellular systems.