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SUMMARYDysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19.HIGHLIGHTSLarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19Lymphopenia and active T and B cell responses coexist and are shaped by age and sexSARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responsesCytokine storms with systemic S100A8/A9 are associated with COVID-19 severity

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas., Single cell RNA-seq is a powerful method to assign cell identity, but the purity of cell clusters arising from this data is not clear. Here the authors present an entropy-based statistic called ROGUE to quantify the purity of cell clusters, and identify subtypes within clusters.

Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such asin situsequencing show great promise, technical difficulties currently limit their wide usage. Since cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, here we present CSOmap, a computational strategy to infer cellular interaction from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of tumor microenvironments for multiple cancers and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be widely applicable to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.

Additional file 2. Review history.

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.

T cells are key elements of cancer immunotherapy1 but certain fundamental properties, such as the development and migration of T cells within tumours, remain unknown. The enormous T cell receptor (TCR) repertoire, which is required for the recognition of foreign and self-antigens2, could serve as lineage tags to track these T cells in tumours3. Here we obtained transcriptomes of 11,138 single T cells from 12 patients with colorectal cancer, and developed single T cell analysis by RNA sequencing and TCR tracking (STARTRAC) indices to quantitatively analyse the dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. Although both CD8+ effector and ‘exhausted’ T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, most tumour-infiltrating T regulatory (Treg) cells showed clonal exclusivity, whereas certain Treg cell clones were developmentally linked to several T helper (TH) cell clones. Notably, we identified two IFNG+ TH1-like cell clusters in tumours that were associated with distinct IFNγ-regulating transcription factors —the GZMK+ effector memory T cells, which were associated with EOMES and RUNX3, and CXCL13+BHLHE40+ TH1-like cell clusters, which were associated with BHLHE40. Only CXCL13+BHLHE40+ TH1-like cells were preferentially enriched in patients with microsatellite-instable tumours, and this might explain their favourable responses to immune-checkpoint blockade. Furthermore, IGFLR1 was highly expressed in both CXCL13+BHLHE40+ TH1-like cells and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provide a powerful approach to dissect the T cell properties in colorectal cancer comprehensively, and could provide insights into the dynamic relationships of T cells in other cancers. An integrated RNA-sequencing approach demonstrates that CXCL13+ TH1-like cells are preferentially enriched in microsatellite-instable tumours from patients with colorectal cancer, and IGFLR1 is identified as a co-stimulatory molecule.

Background Tumor-reactive CD8+ tumor-infiltrating lymphocytes (TILs) represent a subtype of T cells that can recognize and destroy tumor specifically. Understanding the regulatory mechanism of tumor-reactive CD8+ T cells has important therapeutic implications. Yet the DNA methylation status of this T cell subtype has not been elucidated. Results In this study, we segregate tumor-reactive and bystander CD8+ TILs, as well as naïve and effector memory CD8+ T cell subtypes as controls from colorectal cancer patients, to compare their transcriptome and methylome characteristics. Transcriptome profiling confirms previous conclusions that tumor-reactive TILs have an exhausted tissue-resident memory signature. Whole-genome methylation profiling identifies a distinct methylome pattern of tumor-reactive CD8+ T cells, with tumor-reactive markers CD39 and CD103 being specifically demethylated. In addition, dynamic changes are observed during the transition of naïve T cells into tumor-reactive CD8+ T cells. Transcription factor binding motif enrichment analysis identifies several immune-related transcription factors, including three exhaustion-related genes (NR4A1, BATF, and EGR2) and VDR, which potentially play an important regulatory role in tumor-reactive CD8+ T cells. Conclusion Our study supports the involvement of DNA methylation in shaping tumor-reactive and bystander CD8+ TILs, and provides a valuable resource for the development of novel DNA methylation markers and future therapeutics.

The clinical success of immune checkpoint blockade provides great hope for curing cancers. However, the patient responses are not even. Precise understanding of tumor immunity is necessary to improving the current cancer immunotherapies and to developing new treatment options. Here we applied full-length single cell RNA-seq (scRNA-seq) to three cancer types and provide a comprehensive single T cell data resource for understanding various characteristics of tumor-infiltrating T cells. We also developed an analytical framework named as STARTRAC to quantitatively characterize the dynamic properties of various T cell subsets including tissue preference, clonal expansion, migration, and state transitions from the scRNA-seq snapshots of tumor immune microenvironments. Conserved and cancer type-specific T cell subsets and developmental patterns were revealed, and detailed molecular portrait of the tumor immunity-relevant T cell clusters were provided, shedding lights into the cellular and molecular mechanisms underlying the composition, heterogeneity, and formation of tumor immune microenvironments. Important genes such as LAYN and IGFLR1 also provided new options for future development of cancer therapeutics.

Cancer immunotherapies have shown sustained clinical responses in treating non-small-cell lung cancer1–3, but efficacy varies and depends in part on the amount and properties of tumor infiltrating lymphocytes4–6. To depict the baseline landscape of the composition, lineage and functional states of tumor infiltrating lymphocytes, here we performed deep single-cell RNA sequencing for 12,346 T cells from 14 treatment-naive non-small-cell lung cancer patients. Combined expression and T cell antigen receptor based lineage tracking revealed a significant proportion of inter-tissue effector T cells with a highly migratory nature. As well as tumor-infiltrating CD8+ T cells undergoing exhaustion, we observed two clusters of cells exhibiting states preceding exhaustion, and a high ratio of “pre-exhausted” to exhausted T cells was associated with better prognosis of lung adenocarcinoma. Additionally, we observed further heterogeneity within the tumor regulatory T cells (Tregs), characterized by the bimodal distribution of TNFRSF9, an activation marker for antigen-specific Tregs. The gene signature of those activated tumor Tregs, which included IL1R2, correlated with poor prognosis in lung adenocarcinoma. Our study provides a new approach for patient stratification and will help further understand the functional states and dynamics of T cells in lung cancer. Deep single-cell RNA sequencing of tumor-infiltrating T cells in non-small-cell lung cancer identifies features associated with functional states and clinical outcome

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes were associated with distinct clinical features including age, sex, severity, and disease stages of COVID-19. SARS-CoV-2 RNAs were found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within viral positive cells. Systemic up-regulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and developing effective therapeutic strategies for COVID-19., Highlights Detailed COVID-19 immune landscape depicted by integrated 1.46 million single cells Peripheral immune subtypes differentially associated with distinct clinical features SARS-CoV-2 RNAs are present in diverse epithelial and immune cells Megakaryocytes and monocyte subsets may contribute to cytokine storms, Analysis of immune landscape in the lung and peripheral blood of COVID patients across different regions in China at the single cell level documents the presence of viral RNAs in diverse cell types and highlights the potential contribution of megakaryocytes and monocyte subsets to cytokine storms.

Despite mounting evidence for SARS-CoV-2 engagement with immune cells, most express little, if any, of the canonical receptor of SARS-CoV-2, ACE2. Here, using a myeloid-cell receptor-focused ectopic expression screen, we identified several C-type lectins (DC-SIGN, L-SIGN, LSECtin, ASGR1, and CLEC10A) and Tweety family member 2 (TTYH2) as glycan-dependent binding partners of the SARS-CoV-2 spike. Except for TTYH2, these molecules primarily interacted with spike via regions outside of the receptor-binding domain. Single-cell RNA-sequencing analysis of pulmonary cells from COVID-19 patients indicated predominant expression of these molecules on myeloid cells. Although these receptors do not support active replication of SARS-CoV-2, their engagement with virus induced robust proinflammatory responses in myeloid cells that correlated with COVID-19 severity. We also generated a bispecific anti-spike nanobody that not only blocked ACE2-mediated infection but also the myeloid receptors-mediated proinflammatory responses. Our findings suggest SARS-CoV-2-myeloid receptor interactions promote immune hyper-activation, which represents potential targets for COVID-19 therapy., Graphical Abstract, Most immune cells express little, if any, of the canonical SARS-CoV-2 receptor, ACE2. Lu et al. report that C-type lectins and TTYH2 act as SARS-CoV-2 myeloid cell-interacting partners that trigger immune hyper-activation, but not infection. These findings raise the possibility that these virus-myeloid cell interactions are directly involved in COVID-19 immunopathogenesis and could be targeted for COVID-19 therapy.

Enterovirus 71 (EV71) causes outbreaks of hand, foot and mouth disease (HFMD), primarily in the Asia-Pacific area, that are often associated with complications of severe to fatal neurological symptoms. There are currently no anti-viral therapies or vaccines available for the treatment of EV71 infection. Illustrating human antibody responses neutralizing EV71 infection could potentially provide valuable information for the development of effective therapies and vaccines. Here, we constructed a comprehensive phage display library based on peripheral blood of eight EV71-infected donors and identified 27 EV71-specific human antibodies, of which four have neutralizing activity in in vitro experiments. Deep sequencing analysis of the antibody heavy chains at the transcript level of another three independent EV71-infected donors and three controls demonstrates that heavy chains of the EV71-specific antibodies are conserved among EV71-infected individuals but absent in controls, suggesting convergent evolution of human antibodies against EV71.

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.

An improved understanding of the lung microbiome may lead to better strategies to diagnose, treat, and prevent pulmonary tuberculosis (PTB). However, the characteristics of the lung microbiomes of patients with TB remain largely undefined. In this study, 163 bronchoalveolar lavage (BAL) samples were collected from 163 sputum-negative suspected PTB patients. Furthermore, 12 paired BAL samples were obtained from 12 Mycobacterium tuberculosis-positive (MTB+) patients before and after negative conversion following a two-month anti-TB treatment. The V3-V4 region of the 16S ribosomal RNA (rRNA) gene was used to characterize the microbial composition of the lungs. The results showed that the prevalence of MTB in the BAL samples was 42.9% (70/163) among the sputum-negative patients. The α-diversity of lung microbiota was significantly less diverse in MTB+ patients compared with Mycobacterium tuberculosis-negative (MTB-) patients. There was a significant difference in β-diversity between MTB+ and MTB- patients. MTB+ patients were enriched with Anoxybacillus, while MTB- patients were enriched with Prevotella, Alloprevotella, Veillonella, and Gemella. There was no significant difference between the Anoxybacillus detection rates of MTB+ and MTB- patients. The paired comparison between the BAL samples from MTB+ patients and their negative conversion showed that BAL negative-conversion microbiota had a higher α-diversity. In conclusion, distinct features of airway microbiota could be identified between samples from patients with and without MTB. Our results imply links between lung microbiota and different clinical groups of active PTB.

Summary Single-cell RNA sequencing (scRNA-seq) is a powerful tool for defining cellular diversity in tumors, but its application toward dissecting mechanisms underlying immune-modulating therapies is scarce. We performed scRNA-seq analyses on immune and stromal populations from colorectal cancer patients, identifying specific macrophage and conventional dendritic cell (cDC) subsets as key mediators of cellular cross-talk in the tumor microenvironment. Defining comparable myeloid populations in mouse tumors enabled characterization of their response to myeloid-targeted immunotherapy. Treatment with anti-CSF1R preferentially depleted macrophages with an inflammatory signature but spared macrophage populations that in mouse and human expresses pro-angiogenic/tumorigenic genes. Treatment with a CD40 agonist antibody preferentially activated a cDC population and increased Bhlhe40+ Th1-like cells and CD8+ memory T cells. Our comprehensive analysis of key myeloid subsets in human and mouse identifies critical cellular interactions regulating tumor immunity and defines mechanisms underlying myeloid-targeted immunotherapies currently undergoing clinical testing.

T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we have generated a RNA sequencing dataset of 11,138 T cells isolated from peripheral blood, adjacent normal and tumour tissues of 12 colorectal cancer (CRC) patients, including 4 with microsatellite instability (MSI). The dataset contained an expression profile of 10,805 T cells, as well as the full-length T cell receptor (TCR) sequences of 9,878 cells after quality control. To facilitate data mining of our T cell dataset, we developed a web-based application to deliver systematic interrogations and customizable functionalities (http://crctcell.cancer-pku.cn/). Functioning with our dataset, the web tool enables the characterization of TILs based on both transcriptome and assembled TCR sequences at the single cell level, which will help unleash the potential value of our CRC T cell data resource., Design Type(s)transcription profiling design • disease analysis objectiveMeasurement Type(s)transcription profiling assayTechnology Type(s)RNA sequencingFactor Type(s)Microsatellite Instability • age • sex • experimental condition • tumor stageSample Characteristic(s)Homo sapiens • lymphocyte Machine-accessible metadata file describing the reported data (ISA-Tab format)

Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

Motivation Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene’s ability to reconstruct cell–cell relationship and minimize gene redundancy by considering gene–gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM’s advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as ‘mouse cell marker atlas’, which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. Availability and implementation scTIM is freely available at https://github.com/Frank-Orwell/scTIM. Supplementary information Supplementary data are available at Bioinformatics online.

Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45-cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

The Cancer Genome Atlas (TCGA) project produced RNA-Seq data for tens of thousands of cancer and non-cancer samples with clinical survival information, providing an unprecedented opportunity for analyzing prognostic genes and their isoforms. In this study, we performed the first large-scale identification of transcriptional isoforms that are specifically associated with patient prognosis, even without gene-level association. These specific isoforms are defined as Transcripts Associated with Patient Prognosis (TAPPs). Although a group of TAPPs are the principal isoforms of their genes with intact functional protein domains, another group of TAPPs lack important protein domains found in their canonical gene isoforms. This dichotomy in the distribution of protein domains may indicate different patterns of TAPPs association with cancer. TAPPs in protein-coding genes, especially those with altered protein domains, are rich in known cancer driver genes. We further identified multiple types of cancer recurrent TAPPs, such as DCAF17-201, providing a new approach for the detection of cancer-associated events. In order to make the wide research community to study prognostic isoforms, we developed a portal named GESUR (http://gesur.cancer-pku.cn/), which illustrates the detailed prognostic characteristics of TAPPs and other isoforms. Overall, our integrated analysis of gene expression and clinical parameters provides a new perspective for understanding the applications of different gene isoforms in tumor progression.

The clinical success of immune checkpoint blockade provides great hope for curing cancers. However, the patient responses are not even. Precise understanding of tumor immunity is necessary to improving the current cancer immunotherapies and to developing new treatment options. Here we applied full-length single cell RNA-seq (scRNA-seq) to three cancer types and provide a comprehensive single T cell data resource for understanding various characteristics of tumor-infiltrating T cells. We also developed an analytical framework named as STARTRAC to quantitatively characterize the dynamic properties of various T cell subsets including tissue preference, clonal expansion, migration, and state transitions from the scRNA-seq snapshots of tumor immune microenvironments. Conserved and cancer type-specific T cell subsets and developmental patterns were revealed, and detailed molecular portrait of the tumor immunity-relevant T cell clusters were provided, shedding lights into the cellular and molecular mechanisms underlying the composition, heterogeneity, and formation of tumor immune microenvironments. Important genes such as LAYN and IGFLR1 also provided new options for future development of cancer therapeutics.

Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties across different tumors remain elusive. Here, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer types, we identified distinct features of TIMs across cancer types. Mast cells in nasopharyngeal cancer were found to be associated with better prognosis and exhibited an anti-tumor phenotype with a high ratio of TNF+/VEGFA+ cells. Systematic comparison between cDC1- and cDC2-derived LAMP3+ cDCs revealed their differences in transcription factors and external stimulus. Additionally, pro-angiogenic tumor-associated macrophages (TAMs) were characterized with diverse markers across different cancer types, and the composition of TIMs appeared to be associated with certain features of somatic mutations and gene expressions. Our results provide a systematic view of the highly heterogeneous TIMs and suggest future avenues for rational, targeted immunotherapies.

Polydimethylsiloxane (PDMS)-grafted nanoparticles and PDMS were added, respectively, to inhibit the dewetting of polymer films and the formation of surface patterns in spin coating. Uniform and flat films were successfully achieved with the addition of PDMS-grafted silica nanoparticles or PDMS. Time-of-flight secondary ion mass spectrometry depth profiling indicated that PDMS-grafted silica nanoparticles and PDMS preferentially segregated to the surface. A high concentration of bromine end groups was observed at the interface. The surface layer of PDMS or PDMS-grafted silica nanoparticles can decrease the surface tension of the polymer solutions and reduce the evaporation rates of the solvents, providing more time for the bromine end groups to anchor themselves at the silicon substrates. Copyright © 2016 John Wiley & Sons, Ltd.

Background Rodents represent around 43% of all mammalian species, are widely distributed, and are the natural reservoirs of a diverse group of zoonotic viruses, including hantaviruses, Lassa viruses, and tick-borne encephalitis viruses. Thus, analyzing the viral diversity harbored by rodents could assist efforts to predict and reduce the risk of future emergence of zoonotic viral diseases. Results We used next-generation sequencing metagenomic analysis to survey for a range of mammalian viral families in rodents and other small animals of the orders Rodentia, Lagomorpha, and Soricomorpha in China. We sampled 3,055 small animals from 20 provinces and then outlined the spectra of mammalian viruses within these individuals and the basic ecological and genetic characteristics of novel rodent and shrew viruses among the viral spectra. Further analysis revealed that host taxonomy plays a primary role and geographical location plays a secondary role in determining viral diversity. Many viruses were reported for the first time with distinct evolutionary lineages, and viruses related to known human or animal pathogens were identified. Phylogram comparison between viruses and hosts indicated that host shifts commonly happened in many different species during viral evolutionary history. Conclusions These results expand our understanding of the viromes of rodents and insectivores in China and suggest that there is high diversity of viruses awaiting discovery in these species in Asia. These findings, combined with our previous bat virome data, greatly increase our knowledge of the viral community in wildlife in a densely populated country in an emerging disease hotspot. Electronic supplementary material The online version of this article (10.1186/s40168-018-0554-9) contains supplementary material, which is available to authorized users.

Clustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

Motivation Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. Results Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown ‘novel’ isoforms and highly computational efficiency compared to other tools. Availability and implementation scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. Supplementary information Supplementary data are available at Bioinformatics online.

A comparative evaluation is reported of the performance of different discharge regimes—AC powered dielectric barrier discharge (DBD) and DC powered corona discharge of positive (DC+) and negative (DC−) polarity—implemented within the same reactor, a benchtop prototype for an advanced oxidation stage in water treatment. For all three modes of operation, DBD, DC+ and DC−, the reactor performance was tested using phenol as model pollutant and determining the process energy efficiency (k E and G 50), kinetics (t 1/2), nitrated byproducts and extent of mineralization to CO2. Finally, comparison with a smaller DBD reactor of similar configuration previously used in our lab (Marotta et al 2011 Plasma Proc. Polym. 8 867) shows that scaling up was carried out successfully and holds promise for larger scale applications.

Figure S1. Arenavirus G. Phylogenetic tree showing the relationships (amino acid) between arenaviruses in the G polymerase. The viruses found in this study are labeled in red font. Figure S2. Arenavirus N. Phylogenetic tree showing the relationships (amino acid) between arenaviruses in the N polymerase. The viruses found in this study are labeled in red font. Figure S3. Phylogenetic tree showing the relationships (amino acid) between arteriviruses in the pp1a proteins. The viruses found in this study are labeled in red font. Figure S4. Genomic organization of the Rat-arterivirus-1/Ningxia2015 and Rat-arterivirus/Jilin2014. Figure S5. phylogenetic tree based on the polyproteins of TBEV. The viruses found in this study are labeled in red font. Figure S6. Phylogenetic tree based on the complete Spike (S) proteins of CoVs. The viruses found in this study are labeled in red font. Figure S7. Phylogenetic treebased on the L proteins of ParaVs. The viruses found in this study are labeled in red font. Figure S8. Phylogenetic tree based on the polyproteins of Noroviruses. The viruses found in this study are labeled in red font. Figure S9. Phylogenetic tree based on diverse sequences of partial amino acid of the polymerases of AdVs. The viruses found in this study are labeled in red font. Figure S10. The phylogenetic relationships between CoVs and their hosts. Figure S11. The phylogenetic relationships between PicoVs and their hosts. Figure S12. The phylogenetic relationships between AstVs and their hosts. Figure S13. The phylogenetic relationships between CVs and their hosts. Figure S14. The phylogenetic relationships between ParVs and their hosts. (DOCX 5298 kb)

Infections caused by dermatophytes, Trichophyton rubrum in particular, are among the most common diseases in humans. In this study, we present a proteogenomic analysis of T. rubrum based on whole-genome proteomics and RNA-Seq studies. We confirmed 4291 expressed proteins in T. rubrum and validated their annotated gene structures based on 35 874 supporting peptides. In addition, we identified 323 novel peptides (not present in the current annotated protein database of T. rubrum) that can be used to enhance current T. rubrum annotations. A total of 104 predicted genes supported by novel peptides were identified, and 127 gene models suggested by the novel peptides that conflicted with existing annotations were manually assigned based on transcriptomic evidence. RNA-Seq confirmed the validity of 95% of the total peptides. Our study provides evidence that confirms and improves the genome annotation of T. rubrum and represents the first survey of T. rubrum genome annotations based on experimental evidence. Additionally, our integrated proteomics and multisourced transcriptomics approach provides stronger evidence for annotation refinement than proteomic data alone, which helps to address the dilemma of one-hit wonders (uncertainties supported by only one peptide).

Human rhinovirus (HRV) is an important causative agent of acute respiratory tract infections (ARTIs). The roles of specific HRV genotypes in patients suffering from ARTIs have not been well established. We recruited 147 adult inpatients with community-acquired pneumonia (CAP) and 291 adult outpatients with upper ARTIs (URTIs). Respiratory pathogens were screened via PCR assays. HRV was detected in 42 patients, with 35 species A, five B and two C. Seventeen genotypes were identified, and HRV-A21 ranked the highest (9/42, 21.4%). The HRV-A21-positive infections were detected in four patients with CAP and in five with URTIs, all without co-infections. The HRV-A21 genome sequenced in this study contained 12 novel coding polymorphisms in viral protein (VP) 1, VP2 EF loop, VP3 knob and 3D regions. The infections of HRV-A21 virus obtained in this study could not be neutralized by antiserum of HRV-A21 prototype strain (VR-1131), indicating remarkable antigenic variation. Metagenomic analysis showed the HRV-A21 reads were dominant in bronchoalveolar lavage fluid of the three HRV-A21-positive patients with severe CAP, in which two dead. Our results highlight an unexpected infection of genotype HRV-A21 in the clinic, indicating the necessity of precise genotyping and surveillance of HRVs to improve the clinical management of ARTIs.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Summary The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. A cluster of LAMP3+ dendritic cells (DCs) appeared to be the mature form of conventional DCs and possessed the potential to migrate from tumors to LNs. LAMP3+ DCs also expressed diverse immune-relevant ligands and exhibited potential to regulate multiple subtypes of lymphocytes. Of the macrophages in tumors that exhibited distinct transcriptional states, tumor-associated macrophages (TAMs) were associated with poor prognosis, and we established the inflammatory role of SLC40A1 and GPNMB in these cells. Further, myeloid and lymphoid cells in ascites were predominantly linked to tumor and blood origins, respectively. The dynamic properties of diverse CD45+ cell types revealed by this study add new dimensions to the immune landscape of HCC.

Massively parallel sequencing allows efficient determination of genomic sequences and intra-host polymorphisms for viruses. However, the sequencing depth that guarantees safe interpretation of the observed data is unclear. We demonstrated that 10-fold genome coverage may allow safe genome assembly, and 1000-fold coverage is required to obtain reliable polymorphism estimation.

Stable and unstable spin-coated polymer films were prepared using various solvents and substrates. The relationship between polymer end-group concentrations and stability of spin-coated polymer films was revealed by time-of-flight secondary ion mass spectrometry depth profiling. A high concentration of bromine end groups at the interface between the polymer and the substrate helped to prevent the dewetting of films. In contrast, the bromine end groups were found to be more evenly distributed in unstable thin films. The extent to which the bromine end groups segregate to the interface depended on the competitive interactions between the polymer, the solvent and the substrate. Stronger polymer–solvent and solvent–substrate interactions prevented the segregation of the bromine end groups to the interface, resulting in unstable polymer films. Copyright © 2013 John Wiley & Sons, Ltd.

The effects of argon (Ar) and a mixture of Ar and oxgyen(Ar/O2) plasmas on amorphous and semi-crystalline poly(bisphenol A hexane ether) thin films were investigated by time-of-flight secondary ion mass spectroscopy (ToF-SIMS) and principal component analysis (PCA). PCA results of the ToF-SIMS spectra indicate that an Ar/O2 plasma produced less physical sputtering and had a higher chemical reactivity than did an Ar plasma, regardless of whether an amorphous or a crystalline surface was involved. However, the chemical differences between the Ar- and Ar/O2-plasma-treated semi-crystalline films were much smaller. The observed results can be explained by the higher resistance of the polymer crystalline regions to physical sputtering and chemical etching. Copyright © 2013 John Wiley & Sons, Ltd.

The past decades witnessed extensive efforts to study the relationship among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. High-throughput experimental methodologies make many model organism's PPIs known, which allows us to apply machine learning methods to learn understandable rules from the available PPIs. Under the machine learning framework, the composition vectors are usually applied to encode proteins as real-value vectors. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vectors. Thus formulation to estimate the noise induced by the background distribution of amino acids may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which were successfully used in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and human PPIs datasets, surprisingly, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that the noise in phylogenetic tree construction may be valuable information in PPIs prediction. (C) 2012 Elsevier Ltd. All rights reserved.

Rodents are important reservoir hosts of many important zoonotic viruses. The family Picornaviridae contains clinically important pathogens that infect humans and animals, and increasing numbers of rodent picornaviruses have recently been associated with zoonoses. We collected 574 pharyngeal and anal swab specimens from 287 rodents of 10 different species from eight representative regions of China from October 2013 to July 2015. Seven representative sequences identified from six rodent species were amplified as full genomes and classified into four lineages. Three lineage 1 viruses belonged to a novel genus of picornaviruses and was more closely related to Hepatovirus than to others genera of picornaviruses based on aa homology. Lineage 2, lineage 3, and lineage 4 viruses belonged to the genera Rosavirus, Hunnivirus, and Enterovirus, respectively, representing new species. According to both phylogenetic and identity analyses, Lineage 2 viruses had a close relationship with rosavirus 2 which was recovered from the feces of a child in Gambia and Lineage 3 viruses had a close relationship with domestic animal Hunnivirus. Lineage 4 viruses provide the first evidence of these enteroviruses and their evolution in rodent hosts in China.

Studies have demonstrated that {\textasciitilde}60\%-80\% of emerging infectious diseases (EIDs) in humans originated from wild life. Bats are natural reservoirs of a large variety of viruses, including many important zoonotic viruses that cause severe diseases in humans and domestic animals. However, the understanding of the viral population and the ecological diversity residing in bat populations is unclear, which complicates the determination of the origins of certain EIDs. Here, using bats as a typical wildlife reservoir model, virome analysis was conducted based on pharyngeal and anal swab samples of 4440 bat individuals of 40 major bat species throughout China. The purpose of this study was to survey the ecological and biological diversities of viruses residing in these bat species, to investigate the presence of potential bat-borne zoonotic viruses and to evaluate the impacts of these viruses on public health. The data obtained in this study revealed an overview of the viral community present in these bat samples. Many novel bat viruses were reported for the first time and some bat viruses closely related to known human or animal pathogens were identified. This genetic evidence provides new clues in the search for the origin or evolution pattern of certain viruses, such as coronaviruses and noroviruses. These data offer meaningful ecological information for predicting and tracing wildlife-originated EIDs.

Several lineage B betacoronaviruses termed severe acute respiratory syndrome (SARS)–like CoVs (SL-CoVs) were identified from Rhinolophus bats in China. These viruses are characterized by a set of unique accessory open reading frames (ORFs) that are located between the M and N genes. Among unique accessory ORFs, ORF8 is most hypervariable. In this study, the ORF8s of all SL-CoVs were classified into 3 types, and, for the first time, it was found that very few SL-CoVs from Rhinolophus sinicus have ORF8s that are identical to that of human SARS-CoV. This finding provides new genetic evidence for Chinese horseshoe bats as the source of human SARS-CoV.

Bats are natural hosts for a large variety of zoonotic viruses. This study aimed to describe the range of bat viromes, including viruses from mammals, insects, fungi, plants, and phages, in 11 insectivorous bat species (216 bats in total) common in six provinces of China. To analyze viromes, we used sequence-independent PCR amplification and next-generation sequencing technology (Solexa Genome Analyzer II; Illumina). The viromes were identified by sequence similarity comparisons to known viruses. The mammalian viruses included those of the Adenoviridae , Herpesviridae , Papillomaviridae , Retroviridae , Circoviridae , Rhabdoviridae , Astroviridae , Flaviridae , Coronaviridae , Picornaviridae , and Parvovirinae ; insect viruses included those of the Baculoviridae , Iflaviridae , Dicistroviridae , Tetraviridae , and Densovirinae ; fungal viruses included those of the Chrysoviridae , Hypoviridae , Partitiviridae , and Totiviridae ; and phages included those of the Caudovirales , Inoviridae , and Microviridae and unclassified phages. In addition to the viruses and phages associated with the insects, plants, and bacterial flora related to the diet and habitation of bats, we identified the complete or partial genome sequences of 13 novel mammalian viruses. These included herpesviruses, papillomaviruses, a circovirus, a bocavirus, picornaviruses, a pestivirus, and a foamy virus. Pairwise alignments and phylogenetic analyses indicated that these novel viruses showed little genetic similarity with previously reported viruses. This study also revealed a high prevalence and diversity of bat astroviruses and coronaviruses in some provinces. These findings have expanded our understanding of the viromes of bats in China and hinted at the presence of a large variety of unknown mammalian viruses in many common bat species of mainland China.

Surface patterns were observed on spin-coated poly(bisphenol A decane ether) (BA-C10) films prepared with chloroform and tetrahydrofuran as the solvents. The interior structure of these surface patterns were analyzed using a time-of-flight secondary ion mass spectrometry (ToF-SIMS) equipped with a bismuth cluster source for ion imaging and a C(60)(+) cluster source for depth profiling. For the first time, the surface patterns have been shown to be hollow rather than solid using ToF-SIMS three-dimensional (3D) analysis and optical techniques. Moreover, the microarea depth profiling analysis indicated that the hollow structure was sandwiched between two polymer layers rather than sitting on the substrate. The height of the hollow structure and the thicknesses of the polymer layers above and below the hollow structure were also estimated from the depth profiling results.

Recent studies demonstrate that the Hadley Circulation has intensified and expanded for the past three decades, which has important implications for subtropical societies and may lead to profound changes in global climate. However, the robustness of this intensification and expansion that should be considered when interpreting long-term changes of the Hadley Circulation is still a matter of debate. It also remains largely unknown how the Hadley Circulation has evolved over longer periods. Here, we present long-term variability of the Hadley Circulation using the 20th Century Reanalysis. It shows a slight strengthening and widening of the Hadley Circulation since the late 1970s, which is not inconsistent with recent assessments. However, over centennial timescales (1871–2008), the Hadley Circulation shows a tendency towards a more intense and narrower state. More importantly, the width of the Hadley Circulation might have not yet completed a life-cycle since 1871. The strength and width of the Hadley Circulation during the late 19th to early 20th century show strong natural variability, exceeding variability that coincides with global warming in recent decades. These findings raise the question of whether the recent change in the Hadley Circulation is primarily attributed to greenhouse warming or to a long-period oscillation of the Hadley Circulation – substantially longer than that observed in previous studies.

Background High-throughput biotechnologies have been widely used to characterize clinical samples from various perspectives e.g., epigenomics, genomics and transcriptomics. However, because of the heterogeneity of these technologies and their outputs, individual analysis of the various types of data is hard to create a comprehensive view of disease subtypes. Integrative methods are of pressing need. Methods In this study, we evaluated the possible issues that hamper integrative analysis of the heterogeneous disease data types, and proposed iBFE, an effective and efficient computational method to subvert those issues from a feature extraction perspective. Results Strict experiments on both simulated and real datasets demonstrated that iBFE can easily overcome issues caused by scale conflicts, noise conflicts, incompleteness of patient relationships, and conflicts between patient relationships, and that iBFE can effectively combine the merits of DNA methylation, mRNA expression and microRNA (miRNA) expression datasets to accurately identify disease subtypes of significantly different prognosis. Conclusions iBFE is an effective and efficient method for integrative analysis of heterogeneous genomic data to accurately identify disease subtypes. The Matlab code of iBFE is freely available from http://zhangroup.aporc.org/iBFE. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0154-5) contains supplementary material, which is available to authorized users.

This paper reports plasma-induced styrene removal characteristics in air with a homemade ac/dc plasma system. The removal efficiency and reaction kinetics are investigated in terms of source specifications, relative humidity (RH), initial concentrations, by-products, and specific plasma energy density. The result shows that, the larger the difference between peak and dc voltages, the better the removal efficiency. For an initial concentration of 100-110 ppm, the maximum efficiency is around 95% at the energy density of 37-39 J/L, and about 94 ppm is removed under a lower RH. It drops, however, to about 50 and 25 ppm for the RH of 64% and 89%, respectively. Meantime, with increasing humidity, the global plasma kinetics is changed from nonsignificant to significant linear radical termination processes. With regard to by-products, they are mainly benzaldehyde and phenylacetaldehyde.

Signal transduction pathways play important roles in various biological processes such as cell cycle, apoptosis, proliferation, differentiation and responses to the external stimuli. Efficient computational methods are of great demands to map signaling pathways systematically based on the interactome and microarray data in the post-genome era. This paper proposes a novel approach to infer the pathways based on the network flow well studied in the operation research. The authors define a potentiality variable for each protein to denote the extent to which it contributes to the objective pathway. And the capacity on each edge is not a constant but a function of the potentiality variables of the corresponding two proteins. The total potentiality of all proteins is given an upper bound. The approach is formulated to a linear programming model and solved by the simplex method. Experiments on the yeast sporulation data suggest this novel approach recreats successfully the backbone of the MAPK signaling pathway with a low upper bound of the total potentiality. By increasing the upper bound, the approach successfully predicts all the members of the Mitogen-activated protein kinases (MAPK) pathway responding to the pheromone. This simple but effective approach can also be used to infer the genetic information processing pathways underlying the expression quantitative trait loci (eQTL) associations, illustrated by the second example.