Search

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.

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.

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.

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.

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.

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.