11P LINC00926 is a B cell-specific long non-coding RNA in lung adenocarcinoma and is associated with the prognosis of patients with this disease

Single cell-transcriptomic analysis informs the lncRNA landscape in metastatic castration resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.

Single-cell transcriptomics reveals long noncoding RNAs associated with tumor biology and the microenvironment in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous and lethal. Long noncoding RNAs (lncRNAs) are an important class of genes regulating tumorigenesis and progression. Prior bulk transcriptomic studies in PDAC have revealed the dysregulation of lncRNAs but lack single-cell resolution to distinguish lncRNAs in tumor-intrinsic biology and the tumor microenvironment (TME). We analyzed single-cell transcriptome data from 73 multiregion samples in 21 PDAC patients to evaluate lncRNAs associated with intratumoral heterogeneity and the TME in PDAC. We found 111 cell-specific lncRNAs that reflected tumor, immune and stromal cell contributions, associated with outcomes, and validated across orthogonal datasets. Single-cell analysis of tumor cells revealed lncRNAs associated with TP53 mutations and FOLFIRINOX treatment that were obscured in bulk tumor analysis. Lastly, tumor subcluster analysis revealed widespread intratumor heterogeneity and intratumoral lncRNAs associated with cancer hallmarks and tumor processes such as angiogenesis, epithelial-mesenchymal transition, metabolism and immune signaling. Intratumoral subclusters and lncRNAs were validated across six datasets and showed clinically relevant associations with patient outcomes. Our study provides the first comprehensive assessment of the lncRNA landscape in PDAC using single-cell transcriptomic data and can serve as a resource, PDACLncDB (accessible at https://www.maherlab.com/pdaclncdb-overview), to guide future functional studies.

Visualizing cluster-specific genes from single-cell transcriptomics data using Association Plots

Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using methods like PCA, t-SNE, or UMAP. Given a cluster of cells, one frequently searches for the genes highly expressed specifically in that cluster. At this point, visualization is usually replaced by studying a list of differentially expressed genes. Association Plots are derived from correspondence analysis and constitute a planar visualization of the features which characterize a given cluster of observations. We have adapted Association Plots to address the challenge of visualizing cluster-specific genes in large single-cell data sets. Our method is made available as a free R package called APL. We demonstrate the application of APL and Association Plots to single-cell RNA-seq data on two example data sets. First, we present how to delineate novel marker genes using Association Plots with the example of Peripheral Blood Mononuclear Cell data. Second, we show how to apply Association Plots for annotating cell clusters to known cell types using Association Plots and a predefined list of marker genes. To do this we will use data from the human cell atlas of fetal gene expression. Results from Association Plots will also be compared to methods for deriving differentially expressed genes, and we will show the integration of APL with Gene Ontology Enrichment.