Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing.

BACKGROUND: Deciphering intra- and inter-tumoural heterogeneity is essential for understanding the biology of gastric cancer (GC) and its metastasis and identifying effective therapeutic targets. However, the characteristics of different organ-tropism metastases of GC are largely unknown. METHODS: Ten fresh human tissue samples from six patients, including primary tumour and adjacent non-tumoural samples and six metastases from different organs or tissues (liver, peritoneum, ovary, lymph node) were evaluated using single-cell RNA sequencing. Validation experiments were performed using histological assays and bulk transcriptomic datasets. RESULTS: Malignant epithelial subclusters associated with invasion features, intraperitoneal metastasis propensity, epithelial-mesenchymal transition-induced tumour stem cell phenotypes, or dormancy-like characteristics were discovered. High expression of the first three subcluster-associated genes displayed worse overall survival than those with low expression in a GC cohort containing 407 samples. Immune and stromal cells exhibited cellular heterogeneity and created a pro-tumoural and immunosuppressive microenvironment. Furthermore, a 20-gene signature of lymph node-derived exhausted CD8+ T cells was acquired to forecast lymph node metastasis and validated in GC cohorts. Additionally, although anti-NKG2A (KLRC1) antibody have not been used to treat GC patients even in clinical trials, we uncovered not only malignant tumour cells but one endothelial subcluster, mucosal-associated invariant T cells, T cell-like B cells, plasmacytoid dendritic cells, macrophages, monocytes, and neutrophils may contribute to HLA-E-KLRC1/KLRC2 interaction with cytotoxic/exhausted CD8+ T cells and/or natural killer (NK) cells, suggesting novel clinical therapeutic opportunities in GC. Additionally, our findings suggested that PD-1 expression in CD8+ T cells might predict clinical responses to PD-1 blockade therapy in GC. CONCLUSIONS: This study provided insights into heterogeneous microenvironment of GC primary tumours and organ-specific metastases and provide support for precise diagnosis and treatment.

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single-cell sequencing.

BACKGROUND: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. METHODS: In this study, we compared the single-cell transcriptomes of immune cells from 12 species. Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. RESULTS: Our data revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular crosstalks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. CONCLUSIONS: This study is the first to provide a comprehensive analysis of the cross-species single-cell transcriptome atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders.

MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease.

Variable levels of gene expression between tissues complicates the use of RNA sequencing of patient biosamples to delineate the impact of genomic variants. Here, we describe a gene- and tissue-specific metric to inform the feasibility of RNA sequencing. This overcomes limitations of using expression values alone as a metric to predict RNA-sequencing utility. We have derived a metric, minimum required sequencing depth (MRSD), that estimates the depth of sequencing required from RNA sequencing to achieve user-specified sequencing coverage of a gene, transcript, or group of genes. We applied MRSD across four human biosamples: whole blood, lymphoblastoid cell lines (LCLs), skeletal muscle, and cultured fibroblasts. MRSD has high precision (90.1%-98.2%) and overcomes transcript region-specific sequencing biases. Applying MRSD scoring to established disease gene panels shows that fibroblasts, of these four biosamples, are the optimum source of RNA for 63.1% of gene panels. Using this approach, up to 67.8% of the variants of uncertain significance in ClinVar that are predicted to impact splicing could be assayed by RNA sequencing in at least one of the biosamples. We demonstrate the utility and benefits of MRSD as a metric to inform functional assessment of splicing aberrations, in particular in the context of Mendelian genetic disorders to improve diagnostic yield.

Zero-preserving imputation of single-cell RNA-seq data.

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.

Hepatocyte glutathione S-transferase mu 2 prevents non-alcoholic steatohepatitis by suppressing ASK1 signaling.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. The advanced stage of NAFLD, non-alcoholic steatohepatitis (NASH), has been recognized as a leading cause of end-stage liver injury for which there are no FDA-approved therapeutic options. Glutathione S-transferase Mu 2 (GSTM2) is a phase II detoxification enzyme. However, the roles of GSTM2 in NASH have not been elucidated. METHODS: Multiple RNA-seq analyses were used to identify hepatic GSTM2 expression in NASH. In vitro and in vivo gain- or loss-of-function approaches were used to investigate the role and molecular mechanism of GSTM2 in NASH. RESULTS: We identified GSTM2 as a sensitive responder and effective suppressor of NASH progression. GSTM2 was significantly downregulated during NASH progression. Hepatocyte GSTM2 deficiency markedly aggravated insulin resistance, hepatic steatosis, inflammation and fibrosis induced by a high-fat diet and a high-fat/high-cholesterol diet. Mechanistically, GSTM2 sustained MAPK pathway signaling by directly interacting with apoptosis signal-regulating kinase 1 (ASK1). GSTM2 directly bound to the N-terminal region of ASK1 and inhibited ASK1 N-terminal dimerization to subsequently repress ASK1 phosphorylation and the activation of its downstream JNK/p38 signaling pathway under conditions of metabolic dysfunction. CONCLUSIONS: These data demonstrated that hepatocyte GSTM2 is an endogenous suppressor that protects against NASH progression by blocking ASK1 N-terminal dimerization and phosphorylation. Activating GSTM2 holds promise as a therapeutic strategy for NASH. CLINICAL TRIAL NUMBER: IIT-2021-277. LAY SUMMARY: New therapeutic strategies for non-alcoholic steatohepatitis are urgently needed. We identified that the protein GSTM2 exerts a protective effect in response to metabolic stress. Therapies that aim to increase the activity of GSTM2 could hold promise for the treatment of non-alcoholic steatohepatitis.

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data.

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 106 cells and confers remarkable cross-tissue and cross-species zero-shot transfer-learning performance. Using gene set enrichment analysis, we find that scETM-learned topics are enriched in biologically meaningful and disease-related pathways. Lastly, scETM enables the incorporation of known gene sets into the gene embeddings, thereby directly learning the associations between pathways and topics via the topic embeddings.

Health care costs associated with hepatitis C virus infection in First Nations populations in Ontario: a retrospective matched cohort study.

BACKGROUND: Colonization and marginalization have affected the risk for and experience of hepatitis C virus (HCV) infection for First Nations people in Canada. In partnership with the Ontario First Nations HIV/AIDS Education Circle, we estimated the publicly borne health care costs associated with HCV infection among Status First Nations people in Ontario. METHODS: In this retrospective matched cohort study, we used linked health administrative databases to identify Status First Nations people in Ontario who tested positive for HCV antibodies or RNA between 2004 and 2014, and Status First Nations people who had no HCV testing records or only a negative test result (control group, matched 2:1 to case participants). We estimated total and net costs (difference between case and control participants) for 4 phases of care: prediagnosis (6 mo before HCV infection diagnosis), initial (after diagnosis), late (liver disease) and terminal (6 mo before death), until death or Dec. 31, 2017, whichever occurred first. We stratified costs by sex and residence within or outside of First Nations communities. All costs were measured in 2018 Canadian dollars. RESULTS: From 2004 to 2014, 2197 people were diagnosed with HCV infection. The mean net total costs per 30 days of HCV infection were $348 (95% confidence interval [CI] $277 to $427) for the prediagnosis phase, $377 (95% CI $288 to $470) for the initial phase, $1768 (95% CI $1153 to $2427) for the late phase and $893 (95% CI -$1114 to $3149) for the terminal phase. After diagnosis of HCV infection, net costs varied considerably among those who resided within compared to outside of First Nations communities. Net costs were higher for females than for males except in the terminal phase. INTERPRETATION: The costs per 30 days of HCV infection among Status First Nations people in Ontario increased substantially with progression to advanced liver disease and finally to death. These estimates will allow for planning and evaluation of provincial and territorial population-specific hepatitis C control efforts.

The triumphs and limitations of computational methods for scRNA-seq.

The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.

Inferring transcriptomic cell states and transitions only from time series transcriptome data.

Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .

Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer.

BACKGROUND & AIMS: Our previous genomic whole-exome sequencing (WES) data identified the key ErbB pathway mutations that play an essential role in regulating the malignancy of gallbladder cancer (GBC). Herein, we tested the hypothesis that individual cellular components of the tumor microenvironment (TME) in GBC function differentially to participate in ErbB pathway mutation-dependent tumor progression. METHODS: We engaged single-cell RNA-sequencing to reveal transcriptomic heterogeneity and intercellular crosstalk from 13 human GBCs and adjacent normal tissues. In addition, we performed WES analysis to reveal the genomic variations related to tumor malignancy. A variety of bulk RNA-sequencing, immunohistochemical staining, immunofluorescence staining and functional experiments were employed to study the difference between tissues with or without ErbB pathway mutations. RESULTS: We identified 16 cell types from a total of 114,927 cells, in which epithelial cells, M2 macrophages, and regulatory T cells were predominant in tumors with ErbB pathway mutations. Furthermore, epithelial cell subtype 1, 2 and 3 were mainly found in adenocarcinoma and subtype 4 was present in adenosquamous carcinoma. The tumors with ErbB pathway mutations harbored larger populations of epithelial cell subtype 1 and 2, and expressed higher levels of secreted midkine (MDK) than tumors without ErbB pathway mutations. Increased MDK resulted in an interaction with its receptor LRP1, which is expressed by tumor-infiltrating macrophages, and promoted immunosuppressive macrophage differentiation. Moreover, the crosstalk between macrophage-secreted CXCL10 and its receptor CXCR3 on regulatory T cells was induced in GBC with ErbB pathway mutations. Elevated MDK was correlated with poor overall survival in patients with GBC. CONCLUSIONS: This study has provided valuable insights into transcriptomic heterogeneity and the global cellular network in the TME, which coordinately functions to promote the progression of GBC with ErbB pathway mutations; thus, unveiling novel cellular and molecular targets for cancer therapy. LAY SUMMARY: We employed single-cell RNA-sequencing and functional assays to uncover the transcriptomic heterogeneity and intercellular crosstalk present in gallbladder cancer. We found that ErbB pathway mutations reduced anti-cancer immunity and led to cancer development. ErbB pathway mutations resulted in immunosuppressive macrophage differentiation and regulatory T cell activation, explaining the reduced anti-cancer immunity and worse overall survival observed in patients with these mutations.

Assessing the involvement of the placental microbiome and virome in preeclampsia using non coding RNA sequencing.

OBJECTIVES: Preeclampsia is a dangerous pregnancy complication. The source of preeclampsia is unknown, though the placenta is believed to have a central role in its pathogenesis. An association between maternal infection and preeclampsia has been demonstrated, yet the involvement of the placental microbiome in the etiology of preeclampsia has not been determined. In this study, we examined whether preeclampsia is associated with an imbalanced microorganism composition in the placenta. METHODS: To this end, we developed a novel method for the identification of bacteria/viruses based on sequencing of small non-coding RNA, which increases the microorganism-to-host ratio, this being a major challenge in microbiome methods. We validated the method on various infected tissues and demonstrated its efficiency in detecting microorganisms in samples with extremely low bacterial/viral biomass. We then applied the method to placenta specimens from preeclamptic and healthy pregnancies. Since the placenta is a remarkably large and heterogeneous organ, we explored the bacterial and viral RNA at each of 15 distinct locations. RESULTS: Bacterial RNA was detected at all locations and was consistent with previous studies of the placental microbiome, though without significant differences between the preeclampsia and control groups. Nevertheless, the bacterial RNA composition differed significantly between various areas of the placenta. Viral RNA was detected in extremely low quantities, below the threshold of significance, thus viral abundance could not be determined. CONCLUSIONS: Our results suggest that the bacterial and viral abundance in the placenta may have only limited involvement in the pathogenesis of preeclampsia. The evidence of a heterogenic bacterial RNA composition in the various placental locations warrants further investigation to capture the true nature of the placental microbiome.

Statistical Modeling of High Dimensional Counts.

Statistical modeling of count data from RNA sequencing (RNA-seq) experiments is important for proper interpretation of results. Here I will describe how count data can be modeled using count distributions, or alternatively analyzed using nonparametric methods. I will focus on basic routines for performing data input, scaling/normalization, visualization, and statistical testing to determine sets of features where the counts reflect differences in gene expression across samples. Finally, I discuss limitations and possible extensions to the models presented here.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

RNA-Seq Data Analysis in Galaxy.

A complete RNA-Seq analysis involves the use of several different tools, with substantial software and computational requirements. The Galaxy platform simplifies the execution of such bioinformatics analyses by embedding the needed tools in its web interface, while also providing reproducibility. Here, we describe how to perform a reference-based RNA-Seq analysis using Galaxy, from data upload to visualization and functional enrichment analysis of differentially expressed genes.

Databases for RNA Editing Collections.

A-to-I RNA editing in humans plays a relevant role since it can influence gene expression and increase proteome diversity. In addition, its deregulation has been linked to a variety of human diseases, including neurological disorders and cancer.In the last decade, massive transcriptome sequencing through the RNAseq technology has dramatically improved the investigation of RNA editing at single nucleotide resolution. Nowadays, different bioinformatics resources to discover and/or collect A-to-I events have been released. Hereafter, we initially provide an overview of the state-of-the-art RNA editing databases and, then, we focus on REDIportal, the largest collection of A-to-I events with more than 4.5 million sites from 2660 humans GTEx samples.

A novel combination of serum microRNAs for the detection of early gastric cancer.

BACKGROUND: The aim of this study was to identify serum miRNAs that discriminate early gastric cancer (EGC) samples from non-cancer controls using a large cohort. METHODS: This retrospective case-control study included 1417 serum samples from patients with EGC (seen at the National Cancer Center Hospital in Tokyo between 2008 and 2012) and 1417 age- and gender-matched non-cancer controls. The samples were randomly assigned to discovery and validation sets and the miRNA expression profiles of whole serum samples were comprehensively evaluated using a highly sensitive DNA chip (3D-Gene®) designed to detect 2565 miRNA sequences. Diagnostic models were constructed using the levels of several miRNAs in the discovery set, and the diagnostic performance of the model was evaluated in the validation set. RESULTS: The discovery set consisted of 708 samples from EGC patients and 709 samples from non-cancer controls, and the validation set consisted of 709 samples from EGC patients and 708 samples from non-cancer controls. The diagnostic EGC index was constructed using four miRNAs (miR-4257, miR-6785-5p, miR-187-5p, and miR-5739). In the discovery set, a receiver operating characteristic curve analysis of the EGC index revealed that the area under the curve (AUC) was 0.996 with a sensitivity of 0.983 and a specificity of 0.977. In the validation set, the AUC for the EGC index was 0.998 with a sensitivity of 0.996 and a specificity of 0.953. CONCLUSIONS: A novel combination of four serum miRNAs could be a useful non-invasive diagnostic biomarker to detect EGC with high accuracy. A multicenter prospective study is ongoing to confirm the present observations.

Nanopore direct RNA sequencing detects DUX4-activated repeats and isoforms in human muscle cells.

Facioscapulohumeral muscular dystrophy (FSHD) is an inherited muscle disease caused by misexpression of the DUX4 gene in skeletal muscle. DUX4 is a transcription factor, which is normally expressed in the cleavage-stage embryo and regulates gene expression involved in early embryonic development. Recent studies revealed that DUX4 also activates the transcription of repetitive elements such as endogenous retroviruses (ERVs), mammalian apparent long terminal repeat (LTR)-retrotransposons and pericentromeric satellite repeats (Human Satellite II). DUX4-bound ERV sequences also create alternative promoters for genes or long non-coding RNAs, producing fusion transcripts. To further understand transcriptional regulation by DUX4, we performed nanopore long-read direct RNA sequencing (dRNA-seq) of human muscle cells induced by DUX4, because long reads show whole isoforms with greater confidence. We successfully detected differential expression of known DUX4-induced genes and discovered 61 differentially expressed repeat loci, which are near DUX4-ChIP peaks. We also identified 247 gene-ERV fusion transcripts, of which 216 were not reported previously. In addition, long-read dRNA-seq clearly shows that RNA splicing is a common event in DUX4-activated ERV transcripts. Long-read analysis showed non-LTR transposons including Alu elements are also transcribed from LTRs. Our findings revealed further complexity of DUX4-induced ERV transcripts. This catalogue of DUX4-activated repetitive elements may provide useful information to elucidate the pathology of FSHD. Also, our results indicate that nanopore dRNA-seq has complementary strengths to conventional short-read complementary DNA sequencing.

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.