InterCellar enables interactive analysis and exploration of cell−cell communication in single-cell transcriptomic data

Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand−receptor interactions. Despite the existence of various tools capable of inferring cell−cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell−cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand−receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell−cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell−cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell−cell communication and facilitate hypothesis generation in diverse biological systems.

Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates

Patterns of sequencing coverage along a bacterial genome---summarized by a peak-to-trough ratio (PTR)---have been shown to accurately reflect microbial growth rates, revealing a new facet of microbial dynamics and host-microbe interactions. Here, we introduce CoPTR (Compute PTR): a tool for computing PTRs from complete reference genomes and assemblies. Using simulations and data from growth experiments in simple and complex communities, we show that CoPTR is more accurate than the current state-of-the-art, while also providing more PTR estimates overall. We further develop theory formalizing a biological interpretation for PTRs. Using a reference database of 2935 species, we applied CoPTR to a case-control study of 1304 metagenomic samples from 106 individuals with inflammatory bowel disease. We show that growth rates are personalized, are only loosely correlated with relative abundances, and are associated with disease status. We conclude by demonstrating how PTRs can be combined with relative abundances and metabolomics to investigate their effect on the microbiome.

FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.

Could Causal Discovery in Proteogenomics Assist in Understanding Gene–Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause–effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants.

bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk sequencing results

Motivation: Bulk sequencing experiments are essential for exploring a wide range of biological questions. To bring data analysis closer to its interpretation, and facilitate both interactive, exploratory tasks and the sharing of easily accessible information, we present bulkAnalyseR, an R package that offers a seamless, customisable solution for most bulk RNAseq datasets. Results: In bulkAnalyseR, we integrate state-of-the-art approaches, without relying on extensive computational support. We replace static summary images with interactive panels to further strengthen the usability and interpretability of data. The package enables standard analyses on bulk sequencing output, using an expression matrix as the starting point (with the added flexibility of choosing subsets of samples). In an interactive web-based interface, steps such as quality checking, noise detection, inference of differential expression and expression patterns, and biological interpretation (enrichment analyses and identification of regulatory interactions), can be customised, easing the exploration and testing of hypotheses. Availability: bulkAnalyseR is available on GitHub, along with extensive documentation and usage examples (https://github.com/Core-Bioinformatics/bulkAnalyseR).

Phosphoproteomics Sample Preparation Impacts Biological Interpretation of Phosphorylation Signaling Outcomes

The influence of phosphoproteomics sample preparation methods on the biological interpretation of signaling outcome is unclear. Here, we demonstrate a strong bias in phosphorylation signaling targets uncovered by comparing the phosphoproteomes generated by two commonly used methods—strong cation exchange chromatography-based phosphoproteomics (SCXPhos) and single-run high-throughput phosphoproteomics (HighPhos). Phosphoproteomes of embryonic stem cells exposed to ionizing radiation (IR) profiled by both methods achieved equivalent coverage (around 20,000 phosphosites), whereas a combined dataset significantly increased the depth (>30,000 phosphosites). While both methods reproducibly quantified a subset of shared IR-responsive phosphosites that represent DNA damage and cell-cycle-related signaling events, most IR-responsive phosphoproteins (>82%) and phosphosites (>96%) were method-specific. Both methods uncovered unique insights into phospho-signaling mediated by single (SCXPhos) versus double/multi-site (HighPhos) phosphorylation events; particularly, each method identified a distinct set of previously unreported IR-responsive kinome/phosphatome (95% disparate) directly impacting the uncovered biology.

Untargeted Analysis of Lemna minor Metabolites: Workflow and Prioritization Strategy Comparing Highly Confident Features between Different Mass Spectrometers

Metabolomics approaches provide a vast array of analytical datasets, which require a comprehensive analytical, statistical, and biochemical workflow to reveal changes in metabolic profiles. The biological interpretation of mass spectrometric metabolomics results is still obstructed by the reliable identification of the metabolites as well as annotation and/or classification. In this work, the whole Lemna minor (common duckweed) was extracted using various solvents and analyzed utilizing polarity-extended liquid chromatography (reversed-phase liquid chromatography (RPLC)-hydrophilic interaction liquid chromatography (HILIC)) connected to two time-of-flight (TOF) mass spectrometer types, individually. This study (introduces and) discusses three relevant topics for the untargeted workflow: (1) A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two different mass spectrometers using the same plant material type. (2) A statistical procedure was observed prioritizing significant detected features (dependent and independent of the mass spectrometer using the predictive methodology Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). (3) Relevant features were transferred to a prioritization tool (the FOR-IDENT platform (FI)) and were compared with the implemented compound database PLANT-IDENT (PI). This compound database is filled with relevant compounds of the Lemnaceae, Poaceae, Brassicaceae, and Nymphaceae families according to analytical criteria such as retention time (polarity and LogD (pH 7)) and accurate mass (empirical formula). Thus, an untargeted analysis was performed using the new tool as a prioritization and identification source for a hidden-target screening strategy. Consequently, forty-two compounds (amino acids, vitamins, flavonoids) could be recognized and subsequently validated in Lemna metabolic profile using reference standards. The class of flavonoids includes free aglycons and their glycosides. Further, according to our knowledge, the validated flavonoids robinetin and norwogonin were for the first time identified in the Lemna minor extracts.

High-Speed Optical Resolution Photoacoustic Microscopy With a MEMS Scanner: Novel and Simple Distortion Correction Method

Optical resolution photoacoustic microscopy (OR-PAM) is a remarkable biomedical imaging tool that can selectively visualize microtissues with optical-dependent high resolution. However, traditional OR-PAM using mechanical stages provides slow imaging speed, making biological interpretation of in-vivo tissue difficult. Here, we developed a high-speed OR-PAM using a recently commercialized MEMS mirror. This system (MEMS-OR-PAM) consisted of a 1-axis MEMS mirror and a mechanical stage. Furthermore, this study proposed a novel calibration method that quickly removes the spatial distortion caused by fast MEMS scanning. The proposed calibration method needs to run imaging sequence only once using a ruler target and it can easily correct distortions caused by both the scan geometry of the MEMS mirror and its nonlinear motion. The combination of the MEMS-OR-PAM and the distortion correction method was verified by three experiments.; 1) Leaf skeleton phantom imaging to test the distortion correction efficacy.; 2) Spatial resolution and depth of focus (DOF) measurement for the system performance.; 3) In-Vivo finger capillaries imaging to verify their biomedical use. The results showed that the combination could achieve a high-speed (32 sec in 2 mm×4 mm) and high-lateral resolution (~6 µm) imaging capability and precisely visualize the circulating structure of the finger capillaries.

Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers

Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

Comparing single cell datasets using DensityMorph

The proliferation of single cell datasets has brought a wealth of information, but also great challenges in data analysis. Obtaining a cohesive overview of multiple single cell samples is difficult and requires consideration of cell population structure - which may or may not be well defined - along with subtle shifts in expression within cell populations across samples, and changes in population frequency across samples. Ideally, all this would be integrated with the experimental design, e.g. time point, genotype, treatment etc. Data visualisation is the most effective way of communicating analysis but often this takes the form of a plethora of t-SNE plots, colour coded according to marker and sample. In this manuscript, I introduce a novel exploratory data analysis and visualisation method that is centred around a novel quasi-distance (DensityMorph) between single cell samples. DensityMorph makes it possible to plot single cell samples in a manner analogous to performing principal component analysis on microarray samples. Biological interpretation is ensured by the introduction of Explanatory Components, which show how marker expression and coexpression drive the differences between samples. This method is a breakthrough in terms of displaying the most pertinent biological changes across single cell samples in a compact plot. Finally, it can be used either as a stand-alone method or to structure other types of analysis such as manual flow cytometry gating or cell population clustering.