Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms

Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

Abstract Background Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area. Results In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers. Conclusions Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy

Additional file 1 of Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

Additional file 1. Figure S1: Principal component analysis (PCA) plot. Plots were generated using variance stabilized counts prior to batch correction for Roskams-Hieter et al. (A), Chen et al. (B), Zhu et al. (C) and Block et al. (D) datasets. HD, healthy donor; LC, liver cancer. Figure S2: Performance of model training with Roskams-Hieter et al. (2022) dataset. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values. Figure S3: Differences in cellular sources of cfRNA between matched plasma and extracellular vesicle (EV) samples in the Block et al. (2022) dataset. (A) Importance of cell types in the classification of plasma and EV samples as measured by the Mean Decrease in Accuracy (MDA) value from the random forest model. Cell types were ordered in descending order of MDA. (B) Hepatocyte proportion differences between matched plasma and EV samples from each patient. Figure S4: Performance of model testing on each dataset represented with receiver operator characteristic (ROC) curves and area under ROC (AUC) values. The targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) models were trained with the Roskams-Hieter et al. (2022) dataset and tested on the remaining datasets. The results were assessed with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values . The error bars depicted in the figure represent the 95% confidence intervals. Figure S5: Confusion matrices of model testing. Confusion matrices for the targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) model testing. The left-hand top and right-hand bottom numbers represent the positive and negative predictive values, respectively. The numbers on the top and bottom represent the success rate of classifications. Figure S6: Age distribution in Roskams-Hieter et al. and Zhu et al. datasets. Age distribution of female and male healthy donor (HD) and liver cancer (LC) samples in Roskams-Hieter et al. and Zhu et al. datasets (only these datasets contained comprehensive per sample annotations) (A), all samples (B), samples misclassified by the targeted cellular deconvolution model. Figure S7: Influence of sample collection date on model performance. Performance of targeted cellular deconvolution model on Block et al. dataset (only this dataset contained bleed date information): full, after removing liver cancer samples generated after 2010 and after further removing liver cancer samples generated after 2016. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values

Characterization of RNA content in individual phase-separated coacervate microdroplets

Condensates formed by complex coacervation are hypothesized to have played a crucial part during the origin-of-life. In living cells, condensation organizes biomolecules into a wide range of membraneless compartments. Although RNA is a key component of biological condensates and the central component of the RNA world hypothesis, little is known about what determines RNA accumulation in condensates and to which extend single condensates differ in their RNA composition. To address this, we developed an approach to read the RNA content from single synthetic and protein-based condensates using high-throughput sequencing. We find that certain RNAs efficiently accumulate in condensates. These RNAs are strongly enriched in sequence motifs which show high sequence similarity to short interspersed elements (SINEs). We observe similar results for protein-derived condensates, demonstrating applicability across different in vitro reconstituted membraneless organelles. Thus, our results provide a new inroad to explore the RNA content of phase-separated droplets at single condensate resolution.ISSN:2041-172