ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Mutation-induced LZTR1 polymerization provokes cardiac pathology in recessive Noonan syndrome

Noonan syndrome patients harboring causative variants in LZTR1 are particularly at risk to develop severe and early-onset hypertrophic cardiomyopathy. In this study, we investigate the mechanistic consequences of a homozygous variant LZTR1L580P by using patient-specific and CRISPR-Cas9-corrected induced pluripotent stem cell (iPSC) cardiomyocytes. Molecular, cellular, and functional phenotyping in combination with in silico prediction identify an LZTR1L580P-specific disease mechanism provoking cardiac hypertrophy. The variant is predicted to alter the binding affinity of the dimerization domains facilitating the formation of linear LZTR1 polymers. LZTR1 complex dysfunction results in the accumulation of RAS GTPases, thereby provoking global pathological changes of the proteomic landscape ultimately leading to cellular hypertrophy. Furthermore, our data show that cardiomyocyte-specific MRAS degradation is mediated by LZTR1 via non-proteasomal pathways, whereas RIT1 degradation is mediated by both LZTR1-dependent and LZTR1-independent pathways. Uni- or biallelic genetic correction of the LZTR1L580P missense variant rescues the molecular and cellular disease phenotype, providing proof of concept for CRISPR-based therapies.

Role of the Putative Histidine Kinase BP1092 in Bordetella pertussis Virulence Regulation and Intracellular Survival

Bordetella pertussis persists inside host cells, and virulence factors are crucial for intracellular adaptation. The regulation of B. pertussis virulence factor transcription primarily occurs through the modulation of the two-component system (TCS) known as BvgAS. However, additional regulatory systems have emerged as potential contributors to virulence regulation. Here, we investigate the impact of BP1092, a putative TCS histidine kinase that shows increased levels after bacterial internalization by macrophages, on B. pertussis proteome adaptation under nonmodulating (Bvg+) and modulating (Bvg-) conditions. Using mass spectrometry, we compare B. pertussis wild-type (wt), a BP1092-deficient mutant (ΔBP1092), and a ΔBP1092 trans-complemented strain under both conditions. We find an altered abundance of 10 proteins, including five virulence factors. Specifically, under nonmodulating conditions, the mutant strain showed decreased levels of FhaB, FhaS, and Cya compared to the wt. Conversely, under modulating conditions, the mutant strain exhibited reduced levels of BvgA and BvgS compared to those of the wt. Functional assays further revealed that the deletion of BP1092 gene impaired B. pertussis ability to survive within human macrophage THP-1 cells. Taken together, our findings allow us to propose BP1092 as a novel player involved in the intricate regulation of B. pertussis virulence factors and thus in adaptation to the intracellular environment. The data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD041940.

Reduced interleukin-18 secretion by human monocytic cells in response to infections with hyper-virulent Streptococcus pyogenes

BACKGROUND

Streptococcus pyogenes (group A streptococcus, GAS) causes a variety of diseases ranging from mild superficial infections of the throat and skin to severe invasive infections, such as necrotizing soft tissue infections (NSTIs). Tissue passage of GAS often results in mutations within the genes encoding for control of virulence (Cov)R/S two component system leading to a hyper-virulent phenotype. Dendritic cells (DCs) are innate immune sentinels specialized in antigen uptake and subsequent T cell priming. This study aimed to analyze cytokine release by DCs and other cells of monocytic origin in response to wild-type and natural covR/S mutant infections.

METHODS

Human primary monocyte-derived (mo)DCs were used. DC maturation and release of pro-inflammatory cytokines in response to infections with wild-type and covR/S mutants were assessed via flow cytometry. Global proteome changes were assessed via mass spectrometry. As a proof-of-principle, cytokine release by human primary monocytes and macrophages was determined.

RESULTS

In vitro infections of moDCs and other monocytic cells with natural GAS covR/S mutants resulted in reduced secretion of IL-8 and IL-18 as compared to wild-type infections. In contrast, moDC maturation remained unaffected. Inhibition of caspase-8 restored secretion of both molecules. Knock-out of streptolysin O in GAS strain with unaffected CovR/S even further elevated the IL-18 secretion by moDCs. Of 67 fully sequenced NSTI GAS isolates, 28 harbored mutations resulting in dysfunctional CovR/S. However, analyses of plasma IL-8 and IL-18 levels did not correlate with presence or absence of such mutations.

CONCLUSIONS

Our data demonstrate that strains, which harbor covR/S mutations, interfere with IL-18 and IL-8 responses in monocytic cells by utilizing the caspase-8 axis. Future experiments aim to identify the underlying mechanism and consequences for NSTI patients.

Gene expression signature predicts rate of type 1 diabetes progression

BACKGROUND

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

METHODS

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.

FINDINGS

We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.

INTERPRETATION

There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.

FUNDING

A full list of funding bodies can be found under Acknowledgments.

Two-sample test with g-modeling and its applications

Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and identically distributed in each group. However, in some applications the observed data are not identically distributed but associated with some unobserved parameters which are identically distributed. To address this challenge, we propose a novel two-sample testing procedure as a combination of the g $$ g $$ -modeling density estimation introduced by Efron and the two-sample Kolmogorov-Smirnov test. We also propose efficient bootstrap algorithms to estimate the statistical significance for such tests. We demonstrate the utility of the proposed approach with two biostatistical applications: the analysis of surgical nodes data with binomial model and differential expression analysis of single-cell RNA sequencing data with zero-inflated Poisson model.

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

The balance between NANOG and SOX17 mediated by TET proteins regulates specification of human primordial germ cell fate

Background

Human primordial germ cells (hPGCs) initiate from the early post-implantation embryo at week 2–3 and undergo epigenetic reprogramming during development. However, the regulatory mechanism of DNA methylation during hPGC specification is still largely unknown due to the difficulties in analyzing early human embryos. Using an in vitro model of hPGC induction, we found a novel function of TET proteins and NANOG in the hPGC specification which was different from that discovered in mice.

Methods

Using the CRISPR–Cas9 system, we generated a set of TET1, TET2 and TET3 knockout H1 human embryonic stem cell (hESC) lines bearing a BLIMP1-2A-mKate2 reporter. We determined the global mRNA transcription and DNA methylation profiles of pluripotent cells and induced hPGC-like cells (hPGCLCs) by RNA-seq and whole-genome bisulfite sequencing (WGBS) to reveal the involved signaling pathways after TET proteins knockout. ChIP-qPCR was performed to verify the binding of TET and NANOG proteins in the SOX17 promoter. Real-time quantitative PCR, western blot and immunofluorescence were performed to measure gene expression at mRNA and protein levels. The efficiency of hPGC induction was evaluated by FACS.

Results

In humans, TET1, TET2 and TET3 triple-knockout (TKO) human embryonic stem cells (hESCs) impaired the NODAL signaling pathway and impeded hPGC specification in vitro, while the hyperactivated NODAL signaling pathway led to gastrulation failure when Tet proteins were inactivated in mouse. Specifically, TET proteins stimulated SOX17 through the NODAL signaling pathway and directly regulates NANOG expression at the onset of hPGCLCs induction. Notably, NANOG could bind to SOX17 promoter to regulate its expression in hPGCLCs specification. Furthermore, in TKO hESCs, DNMT3B-mediated hypermethylation of the NODAL signaling-related genes and NANOG/SOX17 promoters repressed their activation and inhibited hPGCLC induction. Knockout of DNMT3B in TKO hESCs partially restored NODAL signaling and NANOG/SOX17 expression, and rescued hPGCLC induction.

Conclusion

Our results show that TETs-mediated oxidation of 5-methylcytosine modulates the NODAL signaling pathway and its downstream genes, NANOG and SOX17, by promoting demethylation in opposition to DNMT3B-mediated methylation, suggesting that the epigenetic balance of DNA methylation and demethylation in key genes plays a fundamental role in early hPGC specification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00917-0.

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

Persistent coxsackievirus B1 infection triggers extensive changes in the transcriptome of human pancreatic ductal cells

Enteroviruses, particularly the group B coxsackieviruses (CVBs), have been associated with the development of type 1 diabetes. Several CVB serotypes establish chronic infections in human cells in vivo and in vitro. However, the mechanisms leading to enterovirus persistency and, possibly, beta cell autoimmunity are not fully understood. We established a carrier-state-type persistent infection model in human pancreatic cell line PANC-1 using two distinct CVB1 strains and profiled the infection-induced changes in cellular transcriptome. In the current study, we observed clear changes in the gene expression of factors associated with the pancreatic microenvironment, the secretory pathway, and lysosomal biogenesis during persistent CVB1 infections. Moreover, we found that the antiviral response pathways were activated differently by the two CVB1 strains. Overall, our study reveals extensive transcriptional responses in persistently CVB1-infected pancreatic cells with strong opposite but also common changes between the two strains.

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

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.

Dissecting immune cell stat regulation network reveals biomarkers to predict ICB therapy responders in melanoma

Background

Immunotherapy is a revolutionary strategy in cancer therapy, but the resistance of which is one of the important challenges. Detecting the regulation of immune cells and biomarkers concerning immune checkpoint blockade (ICB) therapy is of great significance.

Methods

Here, we firstly constructed regulation networks for 11 immune cell clusters by integrating biological pathway data and single cell sequencing data in metastatic melanoma with or without ICB therapy. We then dissected these regulation networks and identified differently expressed genes between responders and non-responders. Finally, we trained and validated a logistic regression model based on ligands and receptors in the regulation network to predict ICB therapy response.

Results

We discovered the regulation of genes across eleven immune cell stats. Functional analysis indicated that these stat-specific networks consensually enriched in immune response corrected pathways and highlighted antigen processing and presentation as a core pathway in immune cell regulation. Furthermore, some famous ligands like SIRPA, ITGAM, CD247and receptors like CD14, IL2 and HLA-G were differently expressed between cells of responders and non-responders. A predictive model of gene sets containing ligands and receptors performed accuracy prediction with AUCs above 0.7 in a validation dataset suggesting that they may be server as biomarkers for predicting immunotherapy response.

Conclusions

In summary, our study presented the gene–gene regulation landscape across 11 immune cell clusters and analysis of these networks revealed several important aspects and immunotherapy response biomarkers, which may provide novel insights into immune related mechanisms and immunotherapy response prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02962-8.

Diversity and intratumoral heterogeneity in human gallbladder cancer progression revealed by single-cell RNA sequencing

BACKGROUND

Gallbladder cancer (GC) is a malignant disease characterized with highly cellular heterogeneity and poor prognosis. Determining the intratumoral heterogeneity and microenvironment (TME) can provide novel therapeutic strategies for GC.

METHODS

We performed the single-cell RNA sequencing on the primary and lymph node metastatic gallbladder tumors and the adjacent normal tissues of five patients. The transcriptomic atlas and ligand-receptor-based intercellular communication networks of the single cells were characterized.

RESULTS

The transcriptomic landscape of 24,887 single cells was obtained and characterized as 10 cellular clusters, including epithelial, neuroendocrine tumor cells, T&NK cells, B cells, RGS5+ fibroblasts, POSTN+ fibroblasts, PDGFRA+ fibroblasts, endothelial, myeloid cells, and mast cells. Different types of GC harbored distinct epithelial tumor subpopulations, and squamous cell carcinoma could be differentiated from adenocarcinoma cells. Abundant immune cells infiltrated into adenocarcinoma and squamous cell carcinoma, rather than neuroendocrine neoplasms, which showed significant enrichment of stromal cells. CD4+/FOXP3+ T-reg and CD4+/CXCL13+ T helper cells with higher exhausting biomarkers, as well as a dynamic lineage transition of tumor-associated macrophages from CCL20hi /CD163lo , CCL20lo /CD163hi to APOE+, were identified in GC tissues, suggesting the immunosuppressive and tumor-promoting status of immune cells in TME. Two distinct endothelial cells (KDR+ and ACKR1+), which were involved in angiogenesis and lymphangiogenesis, showed remarkable ligand-receptor interactions with primary GC cells and macrophages in gallbladder tumors.

CONCLUSIONS

This study reveals a widespread reprogramming across multiple cell populations in GC progression, dissects the cellular heterogeneity and interactions in gallbladder TME, and provides potential therapeutic targets for GC.

Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

Effects of paracetamol (acetaminophen) on gene expression and permeability properties of the rat placenta and fetal brain

Background: Paracetamol (acetaminophen) is widely used in pregnancy and generally regarded as "safe" by regulatory authorities. Methods: Clinically relevant doses of paracetamol were administered intraperitoneally to pregnant rats twice daily from embryonic day E15 to 19 (chronic) or as a single dose at E19 (acute). Control samples were from un-treated age-matched animals. At E19, rats were anaesthetised, administered a final paracetamol dose, uteruses were opened and fetuses exposed for sample collection. For RNA sequencing, placentas and fetal brains were removed and flash frozen. Fetal and maternal plasma and cerebrospinal fluid were assayed for ⍺-fetoprotein and interleukin 1β (IL1β). Brains were fixed and examined (immunohistochemistry) for plasma protein distribution. Placental permeability to a small molecule ( 14C-sucrose) was tested by injection into either mother or individual fetuses; fetal and maternal blood was sampled at regular intervals to 90 minutes. Results: RNA sequencing revealed a large number of genes up- or down-regulated in placentas from acutely or chronically treated animals compared to controls. Most notable was down-regulation of three acute phase plasma proteins (⍺-fetoprotein, transferrin, transthyretin) in acute and especially chronic experiments and marked up-regulation of immune-related genes, particularly cytokines, again especially in chronically treated dams. IL1β increased in plasma of most fetuses from treated dams but to variable levels and no IL1β was detectable in plasma of control fetuses or any of the dams. Increased placental permeability appeared to be only from fetus to mother for both 14C-sucrose and ⍺-fetoprotein, but not in the reverse direction. In the fetal brain, gene regulatory changes were less prominent than in the placenta of treated fetuses and did not involve inflammatory-related genes; there was no evidence of increased blood-brain barrier permeability. Conclusion: Results suggest that paracetamol may induce an immune-inflammatory-like response in placenta and more caution should be exercised in use of paracetamol in pregnancy.

Effects of paracetamol (acetaminophen) on gene expression and permeability properties of the rat placenta and fetal brain

Background: Paracetamol (acetaminophen) is widely used in pregnancy and generally regarded as "safe" by regulatory authorities. Methods: Clinically relevant doses of paracetamol were administered intraperitoneally to pregnant rats twice daily from embryonic day E15 to 19 (chronic) or as a single dose at E19 (acute). Control samples were from un-treated age-matched animals. At E19, rats were anaesthetised, administered a final paracetamol dose, uteruses were opened and fetuses exposed for sample collection. For RNA sequencing, placentas and fetal brains were removed and flash frozen. Fetal and maternal plasma and cerebrospinal fluid were assayed for α-fetoprotein and interleukin 1β (IL1β). Brains were fixed and examined (immunohistochemistry) for plasma protein distribution. Placental permeability to a small molecule ( 14C-sucrose) was tested by injection into either mother or individual fetuses; fetal and maternal blood was sampled at regular intervals to 90 minutes. Results: RNA sequencing revealed a large number of genes up- or down-regulated in placentas from acutely or chronically treated animals compared to controls. Most notable was down-regulation of three acute phase plasma proteins (α-fetoprotein, transferrin, transthyretin) in acute and especially chronic experiments and marked up-regulation of immune-related genes, particularly cytokines, again especially in chronically treated dams. IL1β increased in plasma of most fetuses from treated dams but to variable levels and no IL1β was detectable in plasma of control fetuses or any of the dams. Increased placental permeability appeared to be only from fetus to mother for both 14C-sucrose and α-fetoprotein, but not in the reverse direction. In the fetal brain, gene regulatory changes were less prominent than in the placenta of treated fetuses and did not involve inflammatory-related genes; there was no evidence of increased blood-brain barrier permeability. Conclusion: Results suggest that paracetamol may induce an immune-inflammatory-like response in placenta and more caution should be exercised in use of paracetamol in pregnancy.

Reproducibility-optimized detection of differential DNA methylation

Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.

Characterization of the interleukin-17 effect on articular cartilage in a translational model: an explorative study

Background

Osteoarthritis (OA) is a progressive, chronic disease characterized by articular cartilage destruction. The pro-inflammatory cytokine IL-17 levels have been reported elevated in serum and synovial fluid of OA patients and correlated with increased cartilage defects and bone remodeling. The aim of this study was to characterize an IL-17-mediated articular cartilage degradation ex-vivo model and to investigate IL-17 effect on cartilage extracellular matrix protein turnover.

Methods

Full-depth bovine femoral condyle articular cartilage explants were cultured in serum-free medium for three weeks in the absence, or presence of cytokines: IL-17A (100 ng/ml or 25 ng/ml), or 10 ng OSM combined with 20 ng/ml TNFα (O + T). RNA isolation and PCR analysis were performed on tissue lysates to confirm IL-17 receptor expression. GAG and ECM-turnover biomarker release into conditioned media was assessed with dimethyl methylene blue and ELISA assays, respectively. Gelatin zymography was used for matrix metalloproteinase (MMP) 2 and MMP9 activity assessment in conditioned media, and shotgun LC-MS/MS for identification and label-free quantification of proteins and protein fragments in conditioned media. Western blotting was used to validate MS results.

Results

IL-17RA mRNA was expressed in bovine full-depth articular cartilage and the treatment with IL-17A did not interfere with metabolic activity of the model. IL-17A induced cartilage breakdown; conditioned media GAG levels were 3.6-fold-elevated compared to untreated. IL-17A [100 ng/ml] induced ADAMTS-mediated aggrecan degradation fragment release (14-fold increase compared to untreated) and MMP-mediated type II collagen fragment release (6-fold-change compared to untreated). MS data analysis revealed 16 differentially expressed proteins in IL-17A conditioned media compared to untreated, and CHI3L1 upregulation in conditioned media in response to IL-17 was confirmed by Western blotting.

Conclusions

We showed that IL-17A has cartilage modulating potential. It induces collagen and aggrecan degradation indicating an upregulation of MMPs. This was confirmed by zymography and mass spectrometry data. We also showed that the expression of other cytokines is induced by IL-17A, which provide further insight to the pathways that are active in response to IL-17A. This exploratory study confirms that IL-17A may play a role in cartilage pathology and that the applied model may be a good tool to further investigate it.

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compared the performance of 12 differential expression analysis methods for RNA-seq data, including recent variants in widely used software packages, using both RNA spike-in and simulation data for negative binomial (NB) model. Performance of edgeR, DESeq2, and ROTS was particularly different between the two benchmark tests. Then, each method was tested under most extensive simulation conditions especially demonstrating the large impacts of proportion, dispersion, and balance of differentially expressed (DE) genes. DESeq2, a robust version of edgeR (edgeR.rb), voom with TMM normalization (voom.tmm) and sample weights (voom.sw) showed an overall good performance regardless of presence of outliers and proportion of DE genes. The performance of RNA-seq DE gene analysis methods substantially depended on the benchmark used. Based on the simulation results, suitable methods were suggested under various test conditions.

BCseq: accurate single cell RNA-seq quantification with bias correction

With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.

Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes

The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.

Maximal information coefficient applied to differentially expressed genes identification: A feasibility study

BACKGROUND:

The main obstacle encountered in microarray technology is how to mine the valuable information under the profiles and study the genes function.

OBJECTIVE:

Maximal information coefficient (MIC) is a novel, non-parametric statistic that has been successfully applied to genome-wide association studies and differentially gene and miRNA expression analysis. However, the data used in these applications are not gold standard but real data.

METHODS:

Therefore, this study attempts to test the feasibility of MIC for differentially expressed gene identification with simulation data.

RESULTS:

Our experiments indicate that, MIC perfermance is better than Limma always, which is almost the same level of SAM, ROTS or DESeq2. However, the count of AUC < 0.5 of MIC is significantly smaller than the three methods, and MIC does not exhibit an abnormal phenomenon in which the AUC increases as the noise increases.

CONCLUSIONS:

Compared to the existing methods, our experiments show that MIC is not only in the first tier in identifying differentially expressed genes and noise immunity, but also shows better robustness and stronger data/environment adaptability.

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing

Cancer-associated fibroblasts (CAFs) are a major constituent of the tumor microenvironment, although their origin and roles in shaping disease initiation, progression and treatment response remain unclear due to significant heterogeneity. Here, following a negative selection strategy combined with single-cell RNA sequencing of 768 transcriptomes of mesenchymal cells from a genetically engineered mouse model of breast cancer, we define three distinct subpopulations of CAFs. Validation at the transcriptional and protein level in several experimental models of cancer and human tumors reveal spatial separation of the CAF subclasses attributable to different origins, including the peri-vascular niche, the mammary fat pad and the transformed epithelium. Gene profiles for each CAF subtype correlate to distinctive functional programs and hold independent prognostic capability in clinical cohorts by association to metastatic disease. In conclusion, the improved resolution of the widely defined CAF population opens the possibility for biomarker-driven development of drugs for precision targeting of CAFs.

Cancer-associated fibroblasts (CAFs) are an important component of the breast tumour microenvironment. Here, single-cell RNA sequencing of CAFs from a mouse model of breast cancer defines three transcriptomically distinct subpopulations with putatively different functions.

Periostin as a novel biomarker for postoperative recurrence of chronic rhinosinitis with nasal polyps

We previously reported that chronic rhinosinusitis with nasal polyps (CRSwNP) was subdivided into four chronic rhinosinusitis (CRS) subtypes using the JESREC scoring system. We sought to identify the gene expression profile and biomarkers related with CRSwNP by RNA-sequence. RNA-sequencing was performed to identify differentially expressed genes between nasal polyps (NPs) and inferior turbinate mucosa from 6 patients with CRSwNP, and subsequently, quantitative real-time PCR was performed to verify the results. ELISA was performed to identify possible biomarkers for postoperative recurrence. In the RNA-sequencing results, periostin (POSTN) expression was the highest in NP. We focused on POSTN and investigated the protein level of POSTN by immunohistochemistry and ELISA. POSTN was diffusely expressed in moderate and severe eosinophilic CRS using immunohistochemistry, and its staining pattern was associated with the severity of the phenotype of the CRSwNP (P < 0.05). There was a significant difference between the POSTN high/low groups for postoperative recurrence when the cutoff point was set at 115.5 ng/ml (P = 0.0072). Our data suggests that the protein expression level of POSTN was associated with the severity of CRSwNP, and serum POSTN can be a novel biomarker for postoperative recurrence of CRSwNP.

Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq

Single-cell RNA sequencing (scRNA-seq) has emerged as a central genome-wide method to characterize cellular identities and processes. Consequently, improving its sensitivity, flexibility, and cost-efficiency can advance many research questions. Among the flexible plate-based methods, single-cell RNA barcoding and sequencing (SCRB-seq) is highly sensitive and efficient. Here, we systematically evaluate experimental conditions of this protocol and find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to develop a scRNA-seq library protocol we call molecular crowding SCRB-seq (mcSCRB-seq), which we show to be one of the most sensitive, efficient, and flexible scRNA-seq methods to date.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

A Vitis vinifera basic helix-loop-helix transcription factor enhances plant cell size, vegetative biomass and reproductive yield

Strategies for improving plant size are critical targets for plant biotechnology to increase vegetative biomass or reproductive yield. To improve biomass production, a codon-optimized helix-loop-helix transcription factor (VvCEB1opt ) from wine grape was overexpressed in Arabidopsis thaliana resulting in significantly increased leaf number, leaf and rosette area, fresh weight and dry weight. Cell size, but typically not cell number, was increased in all tissues resulting in increased vegetative biomass and reproductive organ size, number and seed yield. Ionomic analysis of leaves revealed the VvCEB1opt -overexpressing plants had significantly elevated, K, S and Mo contents relative to control lines. Increased K content likely drives increased osmotic potential within cells leading to greater cellular growth and expansion. To understand the mechanistic basis of VvCEB1opt action, one transgenic line was genotyped using RNA-Seq mRNA expression profiling and revealed a novel transcriptional reprogramming network with significant changes in mRNA abundance for genes with functions in delayed flowering, pathogen-defence responses, iron homeostasis, vesicle-mediated cell wall formation and auxin-mediated signalling and responses. Direct testing of VvCEB1opt -overexpressing plants showed that they had significantly elevated auxin content and a significantly increased number of lateral leaf primordia within meristems relative to controls, confirming that cell expansion and organ number proliferation were likely an auxin-mediated process. VvCEB1opt overexpression in Nicotiana sylvestris also showed larger cells, organ size and biomass demonstrating the potential applicability of this innovative strategy for improving plant biomass and reproductive yield in crops.

Comparison of methods to detect differentially expressed genes between single-cell populations

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.

scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n = 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at https://bioinfo.uth.edu/scrnaseqdb/.

ROTS: An R package for reproducibility-optimized statistical testing

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.

Identification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is among the most common human malignancies.

Methods

In order to provide better understanding of the molecular biology of ccRCC and to identify potential diagnostic/prognostic biomarker and therapeutic targets, we utilized a microarray to profile mRNA expression of corresponding normal and malignant renal tissues. Real-time PCR, Western Blot and immunohistochemistry were applied to study the expression of candidate biomarkers. ccRCC cell lines were treated with sertraline to inhibit the dopamine transporter SLC6A3.

Results

Differential expression of fourteen mRNAs, yet not studied in ccRCC in depth, was confirmed using qPCR (upregulation: SLC6A3, NPTX2, TNFAIP6, NDUFA4L2, ENPP3, FABP6, SPINK13; downregulation: FXYD4, SLC12A1, KNG1, NPHS2, SLC13A3, GCGR, PLG). Up-/downregulation was also confirmed for FXYD4, KNG1, NPTX2 and SLC12A1 by Western Blot on the protein level. In contrast to the mRNA expression, protein expression of the dopamine transporter SLC6A3 was lower in ccRCC compared to normal renal tissue. Immunohistochemistry indicated that this decrease was due to higher concentrations of SLC6A3 in the proximal tubules. Immunohistochemical analyses further demonstrated that high SLC6A3 expression in ccRCC tissue was correlated with a shorter period of recurrence-free survival following surgery. Treatment of ccRCC cells with the SLC6A3 inhibitor sertraline induced dose-dependent cell-death.

Conclusion

Our study identified several novel biomarkers with diagnostic potential and further investigations on sertraline as therapeutic agent in ccRCC patients are warranted.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-016-0495-5) contains supplementary material, which is available to authorized users.

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.