NDMNN: A novel deep residual network based MNN method to remove batch effects from scRNA-seq data.

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has generated vast amounts of data. However, these data often exhibit batch effects due to various factors such as different time points, experimental personnel, and instruments used, which can obscure the biological differences in the data itself. Based on the characteristics of scRNA-seq data, we designed a dense deep residual network model, referred to as NDnetwork. Subsequently, we combined the NDnetwork model with the MNN method to correct batch effects in scRNA-seq data, and named it the NDMNN method. Comprehensive experimental results demonstrate that the NDMNN method outperforms existing commonly used methods for correcting batch effects in scRNA-seq data. As the scale of single-cell sequencing continues to expand, we believe that NDMNN will be a valuable tool for researchers in the biological community for correcting batch effects in their studies. The source code and experimental results of the NDMNN method can be found at https://github.com/mustang-hub/NDMNN.

Gene representation bias in spatial transcriptomics.

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.

scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

A heavy-tailed model for analyzing miRNA-seq raw read counts.

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

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.

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.

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.

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.

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.

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.

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.

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.

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/ .

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.