Predicting and improving complex beer flavor through machine learning.

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.

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.

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 .

The Current State of Single-Cell Proteomics Data Analysis.

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 and 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, list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses. © 2023 Wiley Periodicals LLC.

Therapeutic activity of GARP:TGF-ß1 blockade in murine primary myelofibrosis.

Primary myelofibrosis (PMF) is a myeloproliferative neoplasm characterized by the clonal expansion of myeloid cells, notably megakaryocytes (MKs), and an aberrant cytokine production leading to bone marrow (BM) fibrosis and insufficiency. Current treatment options are limited. TGF-ß1, a profibrotic and immunosuppressive cytokine, is involved in PMF pathogenesis. While all cell types secrete inactive, latent TGF-ß1, only a few activate the cytokine via cell type-specific mechanisms. The cellular source of the active TGF-ß1 implicated in PMF is not known. Transmembrane protein GARP binds and activates latent TGF-ß1 on the surface of regulatory T lymphocytes (Tregs) and MKs or platelets. Here, we found an increased expression of GARP in the BM and spleen of mice with PMF and tested the therapeutic potential of a monoclonal antibody (mAb) that blocks TGF-ß1 activation by GARP-expressing cells. GARP:TGF-ß1 blockade reduced not only fibrosis but also the clonal expansion of transformed cells. Using mice carrying a genetic deletion of Garp in either Tregs or MKs, we found that the therapeutic effects of GARP:TGF-ß1 blockade in PMF imply targeting GARP on Tregs. These therapeutic effects, accompanied by increased IFN-γ signals in the spleen, were lost upon CD8 T-cell depletion. Our results suggest that the selective blockade of TGF-ß1 activation by GARP-expressing Tregs increases a CD8 T-cell-mediated immune reaction that limits transformed cell expansion, providing a novel approach that could be tested to treat patients with myeloproliferative neoplasms.

Identification and implication of tissue-enriched ligands in epithelial-endothelial crosstalk during pancreas development.

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.

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.

Structural basis of latent TGF-ß1 presentation and activation by GARP on human regulatory T cells.

Transforming growth factor-ß1 (TGF-ß1) 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 (Tregs) suppress immune cells within close proximity by activating latent TGF-ß1 presented by GARP (glycoprotein A repetitions predominant) to integrin αVß8 on their surface. We solved the crystal structure of GARP:latent TGF-ß1 bound to an antibody that stabilizes the complex and blocks release of active TGF-ß1. This finding reveals how GARP exploits an unusual medley of interactions, including fold complementation by the amino terminus of TGF-ß1, to chaperone and orient the cytokine for binding and activation by αVß8. Thus, this work further elucidates the mechanism of antibody-mediated blockade of TGF-ß1 activation and immunosuppression by Tregs.