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Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is a key to tumor progression. Here, we deconvoluted bulk tumor transcriptomes to infer cross-talk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. This approach recovered known transcriptional hallmarks of cancer and stromal cells and was concordant with single-cell, in situ hybridization and IHC data. Inferred autocrine cancer cell interactions varied between tissues but often converged on Ephrin, BMP, and FGFR-signaling pathways. Analysis of immune checkpoints nominated interactions with high levels of cancer-to-immune cross-talk across distinct tumor types. Strikingly, PD-L1 was found to be highly expressed in stromal rather than cancer cells. Overall, our study presents a new resource for hypothesis generation and exploration of cross-talk in the TME.Significance:This study provides deconvoluted bulk tumor transcriptomes across multiple cancer types to infer cross-talk in the tumor microenvironment.

Background & Aims Neutralising antibodies (NAbs) play a key role in clearance of HCV. NAbs have been isolated and mapped to several domains on the HCV envelope proteins. However, the immunodominance of these epitopes in HCV infection remains unknown, hindering efforts to elicit optimal epitope-specific responses. Furthermore, it remains unclear which epitope-specific responses are associated with broad NAb (bNAb) activity in primary HCV infection. The aim of this study was to define B cell immunodominance in primary HCV, and its implications on neutralisation breadth and clearance. Methods Using samples from 168 patients with primary HCV infection, the antibody responses targeted 2 immunodominant domains, termed domains B and C. Genotype 1 and 3 infections were associated with responses targeted towards different bNAb domains. Results No epitopes were uniquely targeted by clearers compared to those who developed chronic infection. Samples with bNAb activity were enriched for multi-specific responses directed towards the epitopes antigenic region 3, antigenic region 4, and domain D, and did not target non-neutralising domains. Conclusions This study outlines for the first time a clear NAb immunodominance profile in primary HCV infection, and indicates that it is influenced by the infecting virus. It also highlights the need for a vaccination strategy to induce multi-specific responses that do not target non-neutralising domains. Lay summary Neutralising antibodies will likely form a key component of a protective hepatitis C virus vaccine. In this work we characterise the predominant neutralising and non-neutralising antibody (epitope) targets in acute hepatitis C virus infection. We have defined the natural hierarchy of epitope immunodominance, and demonstrated that viral genotype can impact on this hierarchy. Our findings highlight key epitopes that are associated with broadly neutralising antibodies, and the deleterious impact of mounting a response towards some of these domains on neutralising breadth. These findings should guide future efforts to design immunogens aimed at generating neutralising antibodies with a vaccine candidate.

T-cell exhaustion is a hallmark of hepatitis C virus (HCV) infection and limits protective immunity in chronic viral infections and cancer. Limited knowledge exists of the initial viral and immune dynamics that characterise exhaustion in humans. We studied longitudinal blood samples from a unique cohort of subjects with primary infection using single cell multi-omics to identify the functions and phenotypes of HCV-specific CD8+ T cells. Early elevated IFN-γ response against the transmitted virus was associated with the rate of immune escape, larger clonal expansion, and early onset of exhaustion. Irrespective of disease outcome we discovered progenitors of early-exhaustion with intermediate expression of PD-1. Intra clonal analysis revealed distinct trajectories with multiple fates suggesting evolutionary plasticity of precursor cells. These findings challenge current paradigm on the contribution of CD8+ T cells to HCV disease outcome and provide data for future studies on T-cell differentiation in human infections.One sentence summaryProgenitors of T-cell exhaustion in acute HCV infection

During ontogeny, macrophages develop from CD115+precursors, including erythro-myeloid progenitors (EMP). EMP arise in the embryonic yolk sac, the primary site of early haematopoiesis. In adults, CD115+bone marrow-derived monocytes represent essential macrophage precursors. Herein, we identify a CD115-macrophage precursor within the adult bone marrow that is unrelated to the classical monocyte lineage but rather shares transcriptomic and functional characteristics of embryonic EMP. These EMPROR (forErythroMyeloidPrecursor) cells are capable of efficiently generating macrophages in disease settings. During early development, EMPROR cells were largely absent from the yolk sac but were instead found at the embryonic-maternal interface in the uterine wall. Unexpectedly, the latter site contains robust haematopoietic activity and harbours defined embryonic haematopoietic progenitor cells, including classical CD115+EMP. Our data suggest the existence of an alternative pathway of macrophage generation in the adult. Further, we uncover a hitherto unknown site of earliest blood cell development.

Indigenous people worldwide are at high risk of developing severe influenza disease. HLA-A*24:02 allele, highly prevalent in Indigenous populations, is associated with influenza-induced mortality, although the basis for this association is unclear. Here, we define CD8+ T-cell immune landscapes against influenza A (IAV) and B (IBV) viruses in HLA-A*24:02-expressing Indigenous and non-Indigenous individuals, human tissues, influenza-infected patients and HLA-A*24:02-transgenic mice. We identify immunodominant protective CD8+ T-cell epitopes, one towards IAV and six towards IBV, with A24/PB2550–558-specific CD8+ T cells being cross-reactive between IAV and IBV. Memory CD8+ T cells towards these specificities are present in blood (CD27+CD45RA− phenotype) and tissues (CD103+CD69+ phenotype) of healthy individuals, and effector CD27−CD45RA−PD-1+CD38+CD8+ T cells in IAV/IBV patients. Our data show influenza-specific CD8+ T-cell responses in Indigenous Australians, and advocate for T-cell-mediated vaccines that target and boost the breadth of IAV/IBV-specific CD8+ T cells to protect high-risk HLA-A*24:02-expressing Indigenous and non-Indigenous populations from severe influenza disease.

Cytotoxic T lymphocytes (CTLs) are thought to arrive at target sites either via random search or following signals by other leukocytes. Here, we reveal independent emergent behaviour in CTL populations attacking tumour masses. Primary murine CTLs coordinate their migration in a process reminiscent of the swarming observed in neutrophils. CTLs engaging cognate targets accelerate the recruitment of distant T cells through long-range homotypic signalling, in part mediated via the diffusion of chemokines CCL3 and CCL4. Newly arriving CTLs augment the chemotactic signal, further accelerating mass recruitment in a positive feedback loop. Activated effector human T cells and chimeric antigen receptor (CAR) T cells similarly employ intra-population signalling to drive rapid convergence. Thus, CTLs recognising a cognate target can induce a localised mass response by amplifying the direct recruitment of additional T cells independently of other leukocytes., eLife digest Immune cells known as cytotoxic T lymphocytes, or CTLs for short, move around the body searching for infected or damaged cells that may cause harm. Once these specialised killer cells identify a target, they launch an attack, removing the harmful cell from the body. CTLs can also recognise and eliminate cancer cells, and can be infused into cancer patients as a form of treatment called adoptive cell transfer immunotherapy. Unfortunately, this kind of treatment does not yet work well on solid tumours because the immune cells often do not infiltrate them sufficiently. It is thought that CTLs arrive at their targets either by randomly searching or by following chemicals secreted by other immune cells. However, the methods used to map the movement of these killer cells have made it difficult to determine how populations of CTLs coordinate their behaviour independently of other cells in the immune system. To overcome this barrier, Galeano Niño, Pageon, Tay et al. employed a three-dimensional model known as a tumouroid embedded in a matrix of proteins, which mimics the tissue environment of a real tumour in the laboratory. These models were used to track the movement of CTLs extracted from mice and humans, as well as human T cells engineered to recognise cancer cells. The experiments showed that when a CTL identifies a tumour cell, it releases chemical signals known as chemokines, which attract other CTLs and recruit them to the target site. Further experiments and computer simulations revealed that as the number of CTLs arriving at the target site increases, this amplifies the chemokine signal being secreted, resulting in more and more CTLs being attracted to the tumour. Other human T cells that had been engineered to recognize cancer cells were also found to employ this method of mass recruitment, and collectively ‘swarm’ towards targeted tumours. These findings shed new light on how CTLs work together to attack a target. It is possible that exploiting the mechanism used by CTLs could help improve the efficiency of tumour-targeting immunotherapies. However, further studies are needed to determine whether these findings can be applied to solid tumours in cancer patients.

Indigenous people worldwide are at high-risk of developing severe influenza disease. HLA-A*24:02 allele, highly prevalent in Indigenous populations, is associated with influenza-induced mortality, although the basis for this association is unclear. We defined CD8+ T-cell immune landscapes against influenza A (IAV) and B (IBV) viruses in HLA-A*24:02-expressing Indigenous and non-Indigenous individuals, human tissues, influenza-infected patients and HLA-A*24:02-transgenic mice. We identified immunodominant protective CD8+ T-cell epitopes, one towards IAV and six towards IBV, with A24/PB2550-558-specific CD8+ T-cells cells being cross-reactive between IAV and IBV. Memory CD8+ T-cells towards these specificities were present in blood (CD27+CD45RA- phenotype) and tissues (CD103+CD69+ phenotype) of healthy subjects, and effector CD27-CD45RA-PD-1+CD38+CD8+ T-cells in IAV/IBV patients. Our data present the first evidence of influenza-specific CD8+ T-cell responses in Indigenous Australians, and advocate for T-cell-mediated vaccines that target and boost the breadth of IAV/IBV-specific CD8+ T-cells to protect high-risk HLA-A*24:02-expressing Indigenous and non-Indigenous populations from severe influenza disease.One Sentence SummaryInfluenza-specific CD8+ T-cell specificities restricted by HLA-A*24:02.

Indigenous people worldwide are at high risk of developing severe influenza disease. HLA-A*24:02 allele, highly prevalent in Indigenous populations, is associated with influenza-induced mortality, although the basis for this association is unclear. Here, we define CD8+ T-cell immune landscapes against influenza A (IAV) and B (IBV) viruses in HLA-A*24:02-expressing Indigenous and non-Indigenous individuals, human tissues, influenza-infected patients and HLA-A*24:02-transgenic mice. We identify immunodominant protective CD8+ T-cell epitopes, one towards IAV and six towards IBV, with A24/PB2550–558-specific CD8+ T cells being cross-reactive between IAV and IBV. Memory CD8+ T cells towards these specificities are present in blood (CD27+CD45RA− phenotype) and tissues (CD103+CD69+ phenotype) of healthy individuals, and effector CD27−CD45RA−PD-1+CD38+CD8+ T cells in IAV/IBV patients. Our data show influenza-specific CD8+ T-cell responses in Indigenous Australians, and advocate for T-cell-mediated vaccines that target and boost the breadth of IAV/IBV-specific CD8+ T cells to protect high-risk HLA-A*24:02-expressing Indigenous and non-Indigenous populations from severe influenza disease., The immunology of Indigenous populations is generally understudied outside the context of diseases that are prevalent in these communities. Here the authors identify prevalence of influenza CD8+ T cell epitopes in an Indigenous Australian population expressing the susceptibility allomorph HLA A*24:02 and validate immunodominance of some of these epitopes in mice.

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is a key to tumor progression. Here, we deconvoluted bulk tumor transcriptomes to infer cross-talk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. This approach recovered known transcriptional hallmarks of cancer and stromal cells and was concordant with single-cell, in situ hybridization and IHC data. Inferred autocrine cancer cell interactions varied between tissues but often converged on Ephrin, BMP, and FGFR-signaling pathways. Analysis of immune checkpoints nominated interactions with high levels of cancer-to-immune cross-talk across distinct tumor types. Strikingly, PD-L1 was found to be highly expressed in stromal rather than cancer cells. Overall, our study presents a new resource for hypothesis generation and exploration of cross-talk in the TME. Significance: This study provides deconvoluted bulk tumor transcriptomes across multiple cancer types to infer cross-talk in the tumor microenvironment.

Objectives Cytomegalovirus (CMV) is known to have a significant impact on immune recovery post‐allogeneic haemopoietic stem cell transplant (HSCT). Adoptive therapy with donor‐derived or third‐party virus‐specific T cells (VST) can restore CMV immunity leading to clinical benefit in prevention and treatment of post‐HSCT infection. We developed a mass cytometry approach to study natural immune recovery post‐HSCT and assess the mechanisms underlying the clinical benefits observed in recipients of VST. Methods A mass cytometry panel of 38 antibodies was utilised for global immune assessment (72 canonical innate and adaptive immune subsets) in HSCT recipients undergoing natural post‐HSCT recovery (n = 13) and HSCT recipients who received third‐party donor‐derived CMV‐VST as salvage for unresponsive CMV reactivation (n = 8). Results Mass cytometry identified distinct immune signatures associated with CMV characterised by a predominance of innate cells (monocytes and NK) seen early and an adaptive signature with activated CD8+ T cells seen later. All CMV‐VST recipients had failed standard antiviral pharmacotherapy as a criterion for trial involvement; 5/8 had failed to develop the adaptive immune signature by study enrolment despite significant CMV antigen exposure. Of these, VST administration resulted in development of the adaptive signature in association with CMV control in three patients. Failure to respond to CMV‐VST in one patient was associated with persistent absence of the adaptive immune signature. Conclusion The clinical benefit of CMV‐VST may be mediated by the recovery of an adaptive immune signature characterised by activated CD8+ T cells., In this study, immune profiling with mass cytometry identified immune signatures associated with cytomegalovirus (CMV) reactivation after allogeneic stem cell transplant in patients undergoing natural immune recovery. Failure to control CMV was associated with failure to develop an adaptive immune signature that could be reversed with third‐party CMV virus‐specific T cells in some recipients.

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp, while it failed for datasets with

Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information. Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians. Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

Influenza A, B and C viruses (IAV, IBV and ICV, respectively) circulate globally and infect humans, with IAV and IBV causing the most severe disease. CD8+ T cells confer cross-protection against IAV strains, however the responses of CD8+ T cells to IBV and ICV are understudied. We investigated the breadth of CD8+ T cell cross-recognition and provide evidence of CD8+ T cell cross-reactivity across IAV, IBV and ICV. We identified immunodominant CD8+ T cell epitopes from IBVs that were protective in mice and found memory CD8+ T cells directed against universal and influenza-virus-type-specific epitopes in the blood and lungs of healthy humans. Lung-derived CD8+ T cells displayed tissue-resident memory phenotypes. Notably, CD38+Ki67+CD8+ effector T cells directed against novel epitopes were readily detected in IAV- or IBV-infected pediatric and adult subjects. Our study introduces a new paradigm whereby CD8+ T cells confer unprecedented cross-reactivity across all influenza viruses, a key finding for the design of universal vaccines.

BackgroundSingle cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.ResultsWe developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.ConclusionsVDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

Heterogeneity of T cells is a hallmark of a successful adaptive immune response, harnessing the vast diversity of antigen-specific T cells into a coordinated evolution of effector and memory outcomes. The T cell receptor (TCR) repertoire is highly diverse to account for the highly heterogeneous antigenic world. During the response to a virus multiple individual clones of antigen specific CD8+ (Ag-specific) T cells can be identified against a single epitope and multiple epitopes are recognised. Advances in single-cell technologies have provided the potential to study Ag-specific T cell heterogeneity at both surface phenotype and transcriptome levels, thereby allowing investigation of the diversity within the same apparent sub-population. We propose a new method (VDJPuzzle) to reconstruct the native TCRαβ from single cell RNA-seq data of Ag-specific T cells and then to link these with the gene expression profile of individual cells. We applied this method using rare Ag-specific T cells isolated from peripheral blood of a subject who cleared hepatitis C virus infection. We successfully reconstructed productive TCRαβ in 56 of a total of 63 cells (89%), with double α and double β in 18, and 7% respectively, and double TCRαβ in 2 cells. The method was validated via standard single cell PCR sequencing of the TCR. We demonstrate that single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. This approach allows a detailed analysis of the TCR diversity and its relationship with the transcriptional profile of different clones.

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

Pathogenic autoantibodies arise in many autoimmune diseases, but it is not understood how the cells making them evade immune checkpoints. Here, single-cell multi-omics analysis demonstrates a shared mechanism with lymphoid malignancy in the formation of public rheumatoid factor autoantibodies responsible for mixed cryoglobulinemic vasculitis. By combining single-cell DNA and RNA sequencing with serum antibody peptide sequencing and antibody synthesis, rare circulating B lymphocytes making pathogenic autoantibodies were found to comprise clonal trees accumulating mutations. Lymphoma driver mutations in genes regulating B cell proliferation and V(D)J mutation (CARD11, TNFAIP3, CCND3, ID3, BTG2, and KLHL6) were present in rogue B cells producing the pathogenic autoantibody. Antibody V(D)J mutations conferred pathogenicity by causing the antigen-bound autoantibodies to undergo phase transition to insoluble aggregates at lower temperatures. These results reveal a pre-neoplastic stage in human lymphomagenesis and a cascade of somatic mutations leading to an iconic pathogenic autoantibody.

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Influenza A, B and C viruses (IAV, IBV and ICV, respectively) circulate globally and infect humans, with IAV and IBV causing the most severe disease. CD8

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Motivation The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. The vast repertoire and adaptive variation of BCR sequences due to V(D)J recombination and somatic hypermutation necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing presents the opportunity for simultaneous capture of paired BCR heavy and light chains and the transcriptomic signature. Results We developed VDJPuzzle, a novel bioinformatic tool that reconstructs productive, full-length B-cell receptor sequences of both heavy and light chains and extract somatic mutations on the VDJ region. VDJPuzzle successfully reconstructed BCRs from 100% (n=117) human and 96.5% (n=200) murine B cells. The reconstructed BCRs were successfully validated with single-cell Sanger sequencing. Availability and implementation VDJPuzzle is available at https://bitbucket.org/kirbyvisp/vdjpuzzle2. Supplementary information Supplementary data are available at Bioinformatics online.

The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. Cell-to-cell variability of BCR sequences due to V(D)J recombination and somatic hypermutation (SHM) necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing (scRNA-seq) presents the opportunity for simultaneous capture of the BCR sequence and transcriptomic signature for a detailed understanding of the dynamics of an immune response.We developed VDJPuzzle 2.0, a bioinformatic tool that reconstructs productive, full-length B-cell receptor sequences of both heavy and light chains. VDJPuzzle successfully reconstructs BCRs from 98.3% (n=117) of human and 96.5% (n=200) from murine B cells. 92.0% of clonotypes and 90.3% of mutations were concordant with single-cell Sanger sequencing of the immunoglobulin chains. VDJPuzzle is available at https://bitbucket.org/kirbyvisp/vdjpuzzle2

Severe influenza A virus (IAV) infection is associated with immune dysfunction. Here, we show circulating CD8+ T-cell profiles from patients hospitalized with avian H7N9, seasonal IAV, and influenza vaccinees. Patient survival reflects an early, transient prevalence of highly activated CD38+HLA-DR+PD-1+ CD8+ T cells, whereas the prolonged persistence of this set is found in ultimately fatal cases. Single-cell T cell receptor (TCR)-αβ analyses of activated CD38+HLA-DR+CD8+ T cells show similar TCRαβ diversity but differential clonal expansion kinetics in surviving and fatal H7N9 patients. Delayed clonal expansion associated with an early dichotomy at a transcriptome level (as detected by single-cell RNAseq) is found in CD38+HLA-DR+CD8+ T cells from patients who succumbed to the disease, suggesting a divergent differentiation pathway of CD38+HLA-DR+CD8+ T cells from the outset during fatal disease. Our study proposes that effective expansion of cross-reactive influenza-specific TCRαβ clonotypes with appropriate transcriptome signatures is needed for early protection against severe influenza disease., Virus-specific CD8+ T cells are crucial during H7N9 influenza infection, but CD8+ T cell dysfunction is associated with poor prognosis. Here, the authors use molecular and phenotypic analysis to establish persistence of clonally diverse CD8+ T cell populations during fatal infection.

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

Summary We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.