Tumour immune rejection triggered by activation of α2-adrenergic receptors.

Immunotherapy based on immunecheckpoint blockade (ICB) using antibodies induces rejection of tumours and brings clinical benefit in patients with various cancer types1. However, tumours often resist immune rejection. Ongoing efforts trying to increase tumour response rates are based on combinations of ICB with compounds that aim to reduce immunosuppression in the tumour microenvironment but usually have little effect when used as monotherapies2,3. Here we show that agonists of α2-adrenergic receptors (α2-AR) have very strong anti-tumour activity when used as monotherapies in multiple immunocompetent tumour models, including ICB-resistant models, but not in immunodeficient models. We also observed marked effects in human tumour xenografts implanted in mice reconstituted with human lymphocytes. The anti-tumour effects of α2-AR agonists were reverted by α2-AR antagonists, and were absent in Adra2a-knockout (encoding α2a-AR) mice, demonstrating on-target action exerted on host cells, not tumour cells. Tumours from treated mice contained increased infiltrating T lymphocytes and reduced myeloid suppressor cells, which were more apoptotic. Single-cell RNA-sequencing analysis revealed upregulation of innate and adaptive immune response pathways in macrophages and T cells. To exert their anti-tumour effects, α2-AR agonists required CD4+ T lymphocytes, CD8+ T lymphocytes and macrophages. Reconstitution studies in Adra2a-knockout mice indicated that the agonists acted directly on macrophages, increasing their ability to stimulate T lymphocytes. Our results indicate that α2-AR agonists, some of which are available clinically, could substantially improve the clinical efficacy of cancer immunotherapy.

Axon guidance genes control hepatic artery development.

Earlier data on liver development demonstrated that morphogenesis of the bile duct, portal mesenchyme and hepatic artery is interdependent, yet how this interdependency is orchestrated remains unknown. Here, using 2D and 3D imaging, we first describe how portal mesenchymal cells become organised to form hepatic arteries. Next, we examined intercellular signalling active during portal area development and found that axon guidance genes are dynamically expressed in developing bile ducts and portal mesenchyme. Using tissue-specific gene inactivation in mice, we show that the repulsive guidance molecule BMP co-receptor A (RGMA)/neogenin (NEO1) receptor/ligand pair is dispensable for portal area development, but that deficient roundabout 2 (ROBO2)/SLIT2 signalling in the portal mesenchyme causes reduced maturation of the vascular smooth muscle cells that form the tunica media of the hepatic artery. This arterial anomaly does not impact liver function in homeostatic conditions, but is associated with significant tissular damage following partial hepatectomy. In conclusion, our work identifies new players in development of the liver vasculature in health and liver regeneration.

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments.

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Identification of RSK substrates using an analog-sensitive kinase approach.

The p90 ribosomal S6 kinases (RSK) family of serine/threonine kinases comprises four isoforms (RSK1-4) that lie downstream of the ERK1/2 mitogen-activated protein kinase pathway. RSKs are implicated in fine tuning of cellular processes such as translation, transcription, proliferation, and motility. Previous work showed that pathogens such as Cardioviruses could hijack any of the four RSK isoforms to inhibit PKR activation or to disrupt cellular nucleocytoplasmic trafficking. In contrast, some reports suggest nonredundant functions for distinct RSK isoforms, whereas Coffin-Lowry syndrome has only been associated with mutations in the gene encoding RSK2. In this work, we used the analog-sensitive kinase strategy to ask whether the cellular substrates of distinct RSK isoforms differ. We compared the substrates of two of the most distant RSK isoforms: RSK1 and RSK4. We identified a series of potential substrates for both RSKs in cells and validated RanBP3, PDCD4, IRS2, and ZC3H11A as substrates of both RSK1 and RSK4, and SORBS2 as an RSK1 substrate. In addition, using mutagenesis and inhibitors, we confirmed analog-sensitive kinase data showing that endogenous RSKs phosphorylate TRIM33 at S1119. Our data thus identify a series of potential RSK substrates and suggest that the substrates of RSK1 and RSK4 largely overlap and that the specificity of the various RSK isoforms likely depends on their cell- or tissue-specific expression pattern.

CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.

BACKGROUND: With the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps: signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user. RESULTS: Here, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics. CONCLUSION: CytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.

SUZ domain-containing proteins have multiple effects on nonsense-mediated decay target transcripts.

Many transcripts are targeted by nonsense-mediated decay (NMD), leading to their degradation and the inhibition of their translation. We found that the protein SUZ domain-containing protein 1 (SZRD1) interacts with the key NMD factor up-frameshift 1. When recruited to NMD-sensitive reporter gene transcripts, SZRD1 increased protein production, at least in part, by relieving translational inhibition. The conserved SUZ domain in SZRD1 was required for this effect. The SUZ domain is present in only three other human proteins besides SZRD1: R3H domain-containing protein 1 and 2 (R3HDM1, R3HDM2) and cAMP-regulated phosphoprotein 21 (ARPP21). We found that ARPP21, similarly to SZRD1, can increase protein production from NMD-sensitive reporter transcripts in an SUZ domain-dependent manner. This indicated that the SUZ domain-containing proteins could prevent translational inhibition of transcripts targeted by NMD. Consistent with the idea that SZRD1 mainly prevents translational inhibition, we did not observe a systematic decrease in the abundance of NMD targets when we knocked down SZRD1. Surprisingly, knockdown of SZRD1 in two different cell lines led to reduced levels of the NMD component UPF3B, which was accompanied by increased levels in a subset of NMD targets. This suggests that SZRD1 is required to maintain normal UPF3B levels and indicates that the effect of SZRD1 on NMD targets is not limited to a relief from translational inhibition. Overall, our study reveals that human SUZ domain-containing proteins play a complex role in regulating protein output from transcripts targeted by NMD.

Cardiovirus leader proteins retarget RSK kinases toward alternative substrates to perturb nucleocytoplasmic traffic.

Proteins from some unrelated pathogens, including small RNA viruses of the family Picornaviridae, large DNA viruses such as Kaposi sarcoma-associated herpesvirus and even bacteria of the genus Yersinia can recruit cellular p90-ribosomal protein S6 kinases (RSKs) through a common linear motif and maintain the kinases in an active state. On the one hand, pathogens' proteins might hijack RSKs to promote their own phosphorylation (direct target model). On the other hand, some data suggested that pathogens' proteins might dock the hijacked RSKs toward a third interacting partner, thus redirecting the kinase toward a specific substrate. We explored the second hypothesis using the Cardiovirus leader protein (L) as a paradigm. The L protein is known to trigger nucleocytoplasmic trafficking perturbation, which correlates with hyperphosphorylation of phenylalanine-glycine (FG)-nucleoporins (FG-NUPs) such as NUP98. Using a biotin ligase fused to either RSK or L, we identified FG-NUPs as primary partners of the L-RSK complex in infected cells. An L protein mutated in the central RSK-interaction motif was readily targeted to the nuclear envelope whereas an L protein mutated in the C-terminal domain still interacted with RSK but failed to interact with the nuclear envelope. Thus, L uses distinct motifs to recruit RSK and to dock the L-RSK complex toward the FG-NUPs. Using an analog-sensitive RSK2 mutant kinase, we show that, in infected cells, L can trigger RSK to use NUP98 and NUP214 as direct substrates. Our data therefore illustrate a novel virulence mechanism where pathogens' proteins hijack and retarget cellular protein kinases toward specific substrates, to promote their replication or to escape immunity.

Curated single cell multimodal landmark datasets for R/Bioconductor.

BACKGROUND: The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. RESULTS: We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data. CONCLUSIONS: We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics.

Missing values are a notable challenge when analyzing mass spectrometry-based proteomics data. While the field is still actively debating the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and the dramatic increase in missing values. A popular approach to deal with missing values is to perform imputation. Imputation has several drawbacks for which alternatives exist, but currently, imputation is still a practical solution widely adopted in single-cell proteomics data analysis. This perspective discusses the advantages and drawbacks of imputation. We also highlight 5 main challenges linked to missing value management in single-cell proteomics. Future developments should aim to solve these challenges, whether it is through imputation or data modeling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values, and for proper encoding of missing values.

MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data.

We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing, and visualization of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry experiments on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualization.

Replication of single-cell proteomics data reveals important computational challenges.

INTRODUCTION: Mass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses. AREAS COVERED: This special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses. EXPERT OPINION: Replicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges.

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection.

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

ensembldb: an R package to create and use Ensembl-based annotation resources.

SUMMARY: Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software. AVAILABILITY AND IMPLEMENTATION: ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteome Mapping of a Cyanobacterium Reveals Distinct Compartment Organization and Cell-Dispersed Metabolism.

Cyanobacteria are complex prokaryotes, incorporating a Gram-negative cell wall and internal thylakoid membranes (TMs). However, localization of proteins within cyanobacterial cells is poorly understood. Using subcellular fractionation and quantitative proteomics, we produced an extensive subcellular proteome map of an entire cyanobacterial cell, identifying ∼67% of proteins in Synechocystis sp. PCC 6803, ∼1000 more than previous studies. Assigned to six specific subcellular regions were 1,712 proteins. Proteins involved in energy conversion localized to TMs. The majority of transporters, with the exception of a TM-localized copper importer, resided in the plasma membrane (PM). Most metabolic enzymes were soluble, although numerous pathways terminated in the TM (notably those involved in peptidoglycan monomer, NADP+, heme, lipid, and carotenoid biosynthesis) or PM (specifically, those catalyzing lipopolysaccharide, molybdopterin, FAD, and phylloquinol biosynthesis). We also identified the proteins involved in the TM and PM electron transport chains. The majority of ribosomal proteins and enzymes synthesizing the storage compound polyhydroxybuyrate formed distinct clusters within the data, suggesting similar subcellular distributions to one another, as expected for proteins operating within multicomponent structures. Moreover, heterogeneity within membrane regions was observed, indicating further cellular complexity. Cyanobacterial TM protein localization was conserved in Arabidopsis (Arabidopsis thaliana) chloroplasts, suggesting similar proteome organization in more developed photosynthetic organisms. Successful application of this technique in Synechocystis suggests it could be applied to mapping the proteomes of other cyanobacteria and single-celled organisms. The organization of the cyanobacterial cell revealed here substantially aids our understanding of these environmentally and biotechnologically important organisms.

Negative feedback via RSK modulates Erk-dependent progression from naïve pluripotency.

Mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signalling is implicated in initiation of embryonic stem (ES) cell differentiation. The pathway is subject to complex feedback regulation. Here, we examined the ERK-responsive phosphoproteome in ES cells and identified the negative regulator RSK1 as a prominent target. We used CRISPR/Cas9 to create combinatorial mutations in RSK family genes. Genotypes that included homozygous null mutations in Rps6ka1, encoding RSK1, resulted in elevated ERK phosphorylation. These RSK-depleted ES cells exhibit altered kinetics of transition into differentiation, with accelerated downregulation of naïve pluripotency factors, precocious expression of transitional epiblast markers and early onset of lineage specification. We further show that chemical inhibition of RSK increases ERK phosphorylation and expedites ES cell transition without compromising multilineage potential. These findings demonstrate that the ERK activation profile influences the dynamics of pluripotency progression and highlight the role of signalling feedback in temporal control of cell state transitions.

Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.

The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.

hiPSC hepatocyte model demonstrates the role of unfolded protein response and inflammatory networks in α1-antitrypsin deficiency.

BACKGROUND & AIMS: α1-Antitrypsin deficiency (A1ATD) is an autosomal recessive disorder caused by mutations in the SERPINA1 gene. Individuals with the Z variant (Gly342Lys) retain polymerised protein in the endoplasmic reticulum (ER) of their hepatocytes, predisposing them to liver disease. The concomitant lack of circulating A1AT also causes lung emphysema. Greater insight into the mechanisms that link protein misfolding to liver injury will facilitate the design of novel therapies. METHODS: Human-induced pluripotent stem cell (hiPSC)-derived hepatocytes provide a novel approach to interrogate the molecular mechanisms of A1ATD because of their patient-specific genetic architecture and reflection of human physiology. To that end, we utilised patient-specific hiPSC hepatocyte-like cells (ZZ-HLCs) derived from an A1ATD (ZZ) patient, which faithfully recapitulated key aspects of the disease at the molecular and cellular level. Subsequent functional and "omics" comparisons of these cells with their genetically corrected isogenic-line (RR-HLCs) and primary hepatocytes/human tissue enabled identification of new molecular markers and disease signatures. RESULTS: Our studies showed that abnormal A1AT polymer processing (immobilised ER components, reduced luminal protein mobility and disrupted ER cisternae) occurred heterogeneously within hepatocyte populations and was associated with disrupted mitochondrial structure, presence of the oncogenic protein AKR1B10 and two upregulated molecular clusters centred on members of inflammatory (IL-18 and Caspase-4) and unfolded protein response (Calnexin and Calreticulin) pathways. These results were validated in a second patient-specific hiPSC line. CONCLUSIONS: Our data identified novel pathways that potentially link the expression of Z A1AT polymers to liver disease. These findings could help pave the way towards identification of new therapeutic targets for the treatment of A1ATD. LAY SUMMARY: This study compared the gene expression and protein profiles of healthy liver cells and those affected by the inherited disease α1-antitrypsin deficiency. This approach identified specific factors primarily present in diseased samples which could provide new targets for drug development. This study also demonstrates the interest of using hepatic cells generated from human-induced pluripotent stem cells to model liver disease in vitro for uncovering new mechanisms with clinical relevance.

Orchestrating high-throughput genomic analysis with Bioconductor.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.