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Analyzing mass spectrometry (MS)-based single-cell proteomics (SCP) data faces important challenges inherent to MS-based technologies and single-cell experiments. We present scplainer , a principled and standardized approach for extracting meaningful insights from SCP data using minimal data processing and linear modeling. scplainer performs variance analysis, differential abundance analysis, and component analysis while streamlining result visualization. scplainer effectively corrects for technical variability, enabling the integration of data sets from different SCP experiments. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors. Perception and appreciation of food flavour depends on many factors, posing a challenge for effective prediction. Here, the authors combine extensive chemical and sensory analyses of 250 commercial Belgian beers to train machine learning models that enable flavour and consumer appreciation prediction.

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 . A community of researchers working in the emerging field of single-cell proteomics propose best-practice experimental and computational recommendations and reporting guidelines for studies analyzing proteins from single cells by mass spectrometry.

Regulatory T cells (T regs ) can suppress immune responses through a variety of mechanisms. One such mechanism involves the activation of a surface-bound latent form of the cytokine transforming growth factor–β1 (TGF-β1). Within the cell, newly synthesized pro-TGF-β1 homodimers form disulfide bonds with the transmembrane protein GARP, which acts to chaperone and orient the cytokine for activation at the cell surface. Liénart et al. reveal how GARP interacts with TGF-β1, using a crystal structure in which the complex was stabilized using a Fab fragment from a monoclonal antibody (MHG-8) that binds to the complex. In so doing, they also demonstrate how MHG-8 prevents membrane-associated TGF-β1 release. These structural and mechanistic insights may inform treatments of diseases with altered TGF-β1 functionality and dysfunctional T reg activity, including cancer immunotherapy. Science , this issue p. 952 A crystal structure elucidates the mechanism of antibody-mediated blockade of TGF-β1 activation and immunosuppression by regulatory T cells. Transforming growth factor–β1 (TGF-β1) is one of very few cytokines produced in a latent form, requiring activation to exert any of its vastly diverse effects on development, immunity, and cancer. Regulatory T cells (T regs ) suppress immune cells within close proximity by activating latent TGF-β1 presented by GARP (glycoprotein A repetitions predominant) to integrin αVβ8 on their surface. We solved the crystal structure of GARP:latent TGF-β1 bound to an antibody that stabilizes the complex and blocks release of active TGF-β1. This finding reveals how GARP exploits an unusual medley of interactions, including fold complementation by the amino terminus of TGF-β1, to chaperone and orient the cytokine for binding and activation by αVβ8. Thus, this work further elucidates the mechanism of antibody-mediated blockade of TGF-β1 activation and immunosuppression by T regs .

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. 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. 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.

Development of the pancreas is driven by an intrinsic program coordinated with signals from other cell types in the epithelial environment. These intercellular communications have been so far challenging to study because of the low concentration, localized production and diversity of the signals released. Here, we combined scRNAseq data with a computational interactomic approach to identify signals involved in the reciprocal interactions between the various cell types of the developing pancreas. This in silico approach yielded 40,607 potential ligand-target interactions between the different main pancreatic cell types. Among this vast network of interactions, we focused on three ligands potentially involved in communications between epithelial and endothelial cells. BMP7 and WNT7B, expressed by pancreatic epithelial cells and predicted to target endothelial cells, and SEMA6D, involved in the reverse interaction. In situ hybridization confirmed the localized expression of Bmp7 in the pancreatic epithelial tip cells and of Wnt7b in the trunk cells. On the contrary, Sema6d was enriched in endothelial cells. Functional experiments on ex vivo cultured pancreatic explants indicated that tip cell-produced BMP7 limited development of endothelial cells. This work identified ligands with a restricted tissular and cellular distribution and highlighted the role of BMP7 in the intercellular communications contributing to vessel development and organization during pancreas organogenesis.

To locate mating partners and essential resources such as food, oviposition sites and shelter, insects rely to a large extent on chemical cues. While most research has focused on cues derived from plants and insects, there is mounting evidence that indicates that micro‐organisms emit volatile compounds that may play an important role in insect behaviour. In this study, we assessed how volatile compounds emitted by phylogenetically diverse bacteria affected the olfactory response of the primary parasitoid Aphidius colemani and one of its secondary parasitoids, Dendrocerus aphidum. Olfactory responses were evaluated for volatile blends emitted by bacteria isolated from diverse sources from the parasitoid's habitat, including aphids, aphid mummies and honeydew, and from the parasitoids themselves. Results revealed that A. colemani showed a wide variation in response to bacterial volatiles, ranging from significant attraction over no response to significant repellence. Our results further showed that the olfactory response of A. colemani to bacterial volatile emissions was different from that of D. aphidum. Gas chromatography‐mass spectrometry analysis of the volatile blends revealed that bacterial strains repellent to A. colemani produced significantly higher amounts of esters, organic acids, aromatics and cycloalkanes than attractive strains. Strains repellent to D. aphidum produced significantly higher amounts of alcohols and ketones, whereas the strains attractive to D. aphidum produced higher amounts of the monoterpenes limonene, linalool and geraniol. Overall, our results indicate that bacterial volatiles can have an important impact on insect olfactory responses, and should therefore be considered as an additional, so far often overlooked factor in studying multitrophic interactions between plants and insects. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

Analysing mass spectrometry (MS)-based single-cell proteomics (SCP) data is challenging. The data analysis must address numerous problems that are inherent to both MS-based proteomics technologies and single-cell experiments. This has led to the development of complex and divergent data processing workflows within the field. In this work, we present scplainer, a principled and standardised approach for extracting meaningful insights from SCP data. The approach relies on minimal data processing combined with linear modelling. The approach is a simple yet powerful approach for exploring and interpreting various types of SCP data. scplainer performs variance analysis,differential abundance analysis and component analysis while streamlining the visualization of the results. This thorough exploration enhances our capacity to gain a deeper understanding of the biological processes hidden in the data. Finally, we demonstrate that scplainer corrects for technical variability,and even enables the integration of data sets from different SCP experiments. The approach effectively generates high-quality data that are amenable to perform downstream analyses. In conclusion,this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Added FP control test, revised some of the text about mixed models.* https://github.com/UCLouvain-CBIO/2023-scplainer

Missing values are a notable challenge when analysing mass spectrometry-based proteomics data. While the field is still actively debating on 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 modelling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values and for proper encoding of missing values.

Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows, standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap and list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses.