Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer

Heterogeneity is the major challenge for cancer prevention and therapy. Here, we firstly constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from 7 patients. The sequential comparison of spatial tumor microenvironment (TME) characteristics from non-tumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100 μm wide cluster-cluster boundary contribute to maintaining intratumor architecture. Our study provides novel insights for diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

Transcriptome diversity is a systematic source of bias in RNA-sequencing data

ABSTRACTBackgroundRNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to detect and remove artifactual signals. Several factors such as sex, age, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER) has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors.ResultsHere we show that transcriptome diversity – a simple metric based on Shannon entropy – explains a large portion of variability in gene expression, and is a major factor detected by PEER. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. This prevalent confounding factor provides a simple explanation for a major source of systematic biases in gene expression estimates.ConclusionsOur results show that transcriptome diversity is a metric that captures a systematic bias in RNA-seq and is the strongest known factor encoded in PEER covariates.

Evaluation of the Interplay between the ADAR Editome and Immunotherapy in Melanoma

Background: RNA editing is a highly conserved posttranscriptional mechanism that contributes to transcriptome diversity. In mammals, it includes nucleobase deaminations that convert cytidine (C) into uridine (U) and adenosine (A) into inosine (I). Evidence from cancer studies indicates that RNA-editing enzymes promote certain mechanisms of tumorigenesis. On the other hand, recoding editing in mRNA can generate mutations in proteins that can participate in the Major Histocompatibility Complex (MHC) ligandome and can therefore be recognized by the adaptive immune system. Anti-cancer treatment based on the administration of immune checkpoint inhibitors enhance these natural anti-cancer immune responses. Results: Based on RNA-Seq datasets, we evaluated the editome of melanoma cell lines generated from patients pre- and post-immunotherapy with immune checkpoint inhibitors. Our results reveal a differential editing in Arthrobacter luteus (Alu) sequences between samples pre-therapy and relapses during therapy with immune checkpoint inhibitors. Conclusion: These data pave the way towards the development of new diagnostics and therapies targeted to editing that could help in preventing relapses during immunotherapies.

Automated Isoform Diversity Detector (AIDD): a pipeline for investigating transcriptome diversity of RNA-seq data

Background As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism—RNA editing due to post-transcriptional changes of individual nucleotides—remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. Results Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities. Conclusions AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

Automated Isoform Diversity Detector (AIDD): A pipeline for investigating transcriptome diversity of RNA-seq data

BackgroundAs the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides – remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.ResultsHere we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities.ConclusionsAIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

Brassica napus Infected with Leptosphaeria maculans

Alternative splicing (AS) is a post-transcriptional regulatory process that enhances transcriptome diversity, thereby affecting plant growth, development, and stress responses. To identify the new transcripts and changes in the isoform-level AS landscape of rapeseed (Brassica napus) infected with the fungal pathogen Leptosphaeria maculans, we compared eight RNA-seq libraries prepared from mock-inoculated and inoculated B. napus cotyledons and stems. The AS events that occurred in stems were almost the same as those in cotyledons, with intron retention representing the most common AS pattern. We identified 1892 differentially spliced genes between inoculated and uninoculated plants. We performed a weighted gene co-expression network analysis (WGCNA) to identify eight co-expression modules and their Hub genes, which are the genes most connected with other genes within each module. There are nine Hub genes, encoding nine transcription factors, which represent key regulators of each module, including members of the NAC, WRKY, TRAF, AP2/ERF-ERF, C2H2, C2C2-GATA, HMG, bHLH, and C2C2-CO-like families. Finally, 52 and 117 alternatively spliced genes in cotyledons and stems were also differentially expressed between mock-infected and infected materials, such as HMG and C2C2-Dof; which have dual regulatory mechanisms in response to L. maculans. The splicing of the candidate genes identified in this study could be exploited to improve resistance to L. maculans.