Multilineage murine stem cells generate complex organoids to model distal lung development and disease.

Organoids derived from mouse and human stem cells have recently emerged as a powerful tool to study organ development and disease. We here established a three-dimensional (3D) murine bronchioalveolar lung organoid (BALO) model that allows clonal expansion and self-organization of FACS-sorted bronchioalveolar stem cells (BASCs) upon co-culture with lung-resident mesenchymal cells. BALOs yield a highly branched 3D structure within 21 days of culture, mimicking the cellular composition of the bronchioalveolar compartment as defined by single-cell RNA sequencing and fluorescence as well as electron microscopic phenotyping. Additionally, BALOs support engraftment and maintenance of the cellular phenotype of injected tissue-resident macrophages. We also demonstrate that BALOs recapitulate lung developmental defects after knockdown of a critical regulatory gene, and permit modeling of viral infection. We conclude that the BALO model enables reconstruction of the epithelial-mesenchymal-myeloid unit of the distal lung, thereby opening numerous new avenues to study lung development, infection, and regenerative processes in vitro.

Rapid detection and online analysis of microbial changes through flow cytometry.

Short-read 16 S rRNA gene sequencing is the dominating technology to profile microbial communities in different habitats. Its uncontested taxonomic resolution paved the way for major contributions to the field. Sample measurement and analysis, that is, sequencing, is rather slow-in order of days. Alternatively, flow cytometry can be used to profile the microbiota of various sources within a few minutes per sample. To keep up with high measurement speed, we developed the open source-analyzing tool FlowSoFine. To validate the ability to distinguish microbial profiles, we examined human skin samples of three body sites (N = 3 × 54) with flow cytometry and 16 S rRNA gene amplicon sequencing. Confirmed by sequencing of the very same samples, body site was found to be significantly different by flow cytometry. For a proof-of-principle multidimensional approach, using stool samples of patients (N = 40) with/without inflammatory bowel diseases, we could discriminate the health status by their bacterial patterns. In conclusion, FlowSoFine enables the generation and comparison of cytometric fingerprints of microbial communities from different sources. The implemented interface supports the user through all analytical steps to work out the biological relevant signals from raw measurements to publication ready figures. Furthermore, we present flow cytometry as a valid method for skin microbiota analysis.

CXCL13 is expressed in a subpopulation of neuroendocrine cells in the murine trachea and lung.

The conducting airways are lined by distinct cell types, comprising basal, secretory, ciliated, and rare cells, including ionocytes, solitary cholinergic chemosensory cells, and solitary and clustered (neuroepithelial bodies) neuroendocrine cells. Airway neuroendocrine cells are in clinical focus since they can give rise to small cell lung cancer. They have been implicated in diverse functions including mechanosensation, chemosensation, and regeneration, and were recently identified as regulators of type 2 immune responses via the release of the neuropeptide calcitonin gene-related peptide (CGRP). We here assessed the expression of the chemokine CXCL13 (B cell attracting chemokine) by these cells by RT-PCR, in silico analysis of publicly available sequencing data sets, immunohistochemistry, and immuno-electron microscopy. We identify a phenotype of neuroendocrine cells in the naïve mouse, producing the chemokine CXCL13 predominantly in solitary neuroendocrine cells of the tracheal epithelium (approx. 70% CXCL13+) and, to a lesser extent, in the solitary neuroendocrine cells and neuroepithelial bodies of the intrapulmonary bronchial epithelium (< 10% CXCL13+). In silico analysis of published sequencing data of murine tracheal epithelial cells was consistent with the results obtained by immunohistochemistry as it revealed that neuroendocrine cells are the major source of Cxcl13-mRNA, which was expressed by 68-79% of neuroendocrine cells. An unbiased scRNA-seq data analysis of overall gene expression did not yield subclusters of neuroendocrine cells. Our observation demonstrates phenotypic heterogeneity of airway neuroendocrine cells and points towards a putative immunoregulatory role of these cells in bronchial-associated lymphoid tissue formation and B cell homeostasis.

WASP: a versatile, web-accessible single cell RNA-Seq processing platform.

BACKGROUND: The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. RESULTS: Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. CONCLUSIONS: With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP's post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

ASA3P: An automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates.

Whole genome sequencing of bacteria has become daily routine in many fields. Advances in DNA sequencing technologies and continuously dropping costs have resulted in a tremendous increase in the amounts of available sequence data. However, comprehensive in-depth analysis of the resulting data remains an arduous and time-consuming task. In order to keep pace with these promising but challenging developments and to transform raw data into valuable information, standardized analyses and scalable software tools are needed. Here, we introduce ASA3P, a fully automatic, locally executable and scalable assembly, annotation and analysis pipeline for bacterial genomes. The pipeline automatically executes necessary data processing steps, i.e. quality clipping and assembly of raw sequencing reads, scaffolding of contigs and annotation of the resulting genome sequences. Furthermore, ASA3P conducts comprehensive genome characterizations and analyses, e.g. taxonomic classification, detection of antibiotic resistance genes and identification of virulence factors. All results are presented via an HTML5 user interface providing aggregated information, interactive visualizations and access to intermediate results in standard bioinformatics file formats. We distribute ASA3P in two versions: a locally executable Docker container for small-to-medium-scale projects and an OpenStack based cloud computing version able to automatically create and manage self-scaling compute clusters. Thus, automatic and standardized analysis of hundreds of bacterial genomes becomes feasible within hours. The software and further information is available at: asap.computational.bio.