Single-cell transcriptome analysis reveals secretin as a hallmark of human enteroendocrine cell maturation.

The traditional nomenclature of enteroendocrine cells (EECs), established in 1977, applied the "one cell - one hormone" dogma, which distinguishes subpopulations based on the secretion of a specific hormone. These hormone-specific subpopulations included S cells for secretin (SCT), K cells for glucose-dependent insulinotropic polypeptide (GIP), N cells producing neurotensin (NTS), I cells producing cholecystokinin (CCK), D cells producing somatostatin (SST), and others. In the past 15 years, reinvestigations into murine and human organoid-derived EECs, however, strongly questioned this dogma and established that certain EECs coexpress multiple hormones. Using the Gut Cell Atlas, the largest available single-cell transcriptome dataset of human intestinal cells, this study consolidates that the original dogma is outdated not only for murine and human organoid-derived EECs, but also for primary human EECs, showing that the expression of certain hormones is not restricted to their designated cell type. Moreover, specific analyses into SCT-expressing cells reject the presence of any cell population that exhibits significantly elevated secretin expression compared to other cell populations, previously referred to as S cells. Instead, this investigation indicates that secretin production is realized jointly by other enteroendocrine subpopulations, validating corresponding observations in murine EECs also for human EECs. Furthermore, our findings corroborate that SCT expression peaks in mature EECs, in contrast, progenitor EECs exhibit markedly lower expression levels, supporting the hypothesis that SCT expression is a hallmark of EEC maturation.

Multi-omics regulatory network inference in the presence of missing data.

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

Computational tools for inferring transcription factor activity.

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

Namco: a microbiome explorer.

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.

Network analysis methods for studying microbial communities: A mini review.

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem.

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.

Functional CRISPR dissection of gene networks controlling human regulatory T cell identity.

Human regulatory T (Treg) cells are essential for immune homeostasis. The transcription factor FOXP3 maintains Treg cell identity, yet the complete set of key transcription factors that control Treg cell gene expression remains unknown. Here, we used pooled and arrayed Cas9 ribonucleoprotein screens to identify transcription factors that regulate critical proteins in primary human Treg cells under basal and proinflammatory conditions. We then generated 54,424 single-cell transcriptomes from Treg cells subjected to genetic perturbations and cytokine stimulation, which revealed distinct gene networks individually regulated by FOXP3 and PRDM1, in addition to a network coregulated by FOXO1 and IRF4. We also discovered that HIVEP2, to our knowledge not previously implicated in Treg cell function, coregulates another gene network with SATB1 and is important for Treg cell-mediated immunosuppression. By integrating CRISPR screens and single-cell RNA-sequencing profiling, we have uncovered transcriptional regulators and downstream gene networks in human Treg cells that could be targeted for immunotherapies.