BackgroundBiological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.ResultsWe created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries.ConclusionsThe application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

BackgroundThe three-dimensional organization of the genome is tightly connected to its biological function. The Hi-C approach was recently introduced as a method that can be used to identify higher-order chromatin interactions genome-wide. The aim of this study was to determine genome-wide chromatin interaction frequencies using the Hi-C approach in mouse sperm cells and embryonic fibroblasts.ResultsThe obtained data demonstrate that the three-dimensional genome organizations of sperm and fibroblast cells show a high degree of similarity both with each other and with the previously described mouse embryonic stem cells. Both A- and B-compartments and topologically associated domains are present in spermatozoa and fibroblasts. Nevertheless, sperm cells and fibroblasts exhibit statistically significant differences between each other in the contact probabilities of defined loci. Tight packaging of the sperm genome results in an enrichment of long-range contacts compared with the fibroblasts. However, only 30% of the differences in the number of contacts are based on differences in the densities of their genome packages; the main source of the differences is the gain or loss of contacts that are specific for defined genome regions. We find that the dependence of the contact probability on genomic distance for sperm is close to the dependence predicted for the fractal globular folding of chromatin.ConclusionsOverall, we can conclude that the three-dimensional structure of the genome is passed through generations without being dramatically changed in sperm cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0642-0) contains supplementary material, which is available to authorized users.

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artifacts and thus ignored in data acquisition and analysis, are the result of biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell-monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be re-visited.

CD8 T cells are considered important contributors to the immune response against Mycobacterium tuberculosis, yet limited information is currently known regarding their specific immune signature and phenotype. In this study, we applied a cell population transcriptomics strategy to define immune signatures of human latent tuberculosis infection (LTBI) in memory CD8 T cells. We found a 41-gene signature that discriminates between memory CD8 T cells from healthy LTBI subjects and uninfected controls. The gene signature was dominated by genes associated with mucosal-associated invariant T cells (MAITs) and reflected the lower frequency of MAITs observed in individuals with LTBI. There was no evidence for a conventional CD8 T cell-specific signature between the two cohorts. We, therefore, investigated MAITs in more detail based on Va7.2 and CD161 expression and staining with an MHC-related protein 1 (MR1) tetramer. This revealed two distinct populations of CD8 + Va7.2 + CD161 + MAITs: MR1 tetramer + and MR1 tetramer 2 , which both had distinct gene expression compared with memory CD8 T cells. Transcriptomic analysis of LTBI versus noninfected individuals did not reveal significant differences for MR1 tetramer + MAITs. However, gene expression of MR1 tetramer 2 MAITs showed large interindividual diversity and a tuberculosis-specific signature. This was further strengthened by a more diverse TCR-a and-b repertoire of MR1 tetramer 2 cells as compared with MR1 tetramer +. Thus, circulating memory CD8 T cells in subjects with latent tuberculosis have a reduced number of conventional MR1 tetramer + MAITs as well as a difference in phenotype in the rare population of MR1 tetramer 2 MAITs compared with uninfected controls. ImmunoHorizons, 2020, 4: 292-307.

Our recent work has highlighted that care needs to be taken when interpreting single cell data originating from flow cytometry acquisition or cell sorting: We found that doublets of T cells bound to other immune cells are often present in the live singlet gate of human peripheral blood samples acquired by flow cytometry. This hidden "contamination" generates atypical gene signatures of mixed cell lineage in what is assumed to be single cells, which can lead to data misinterpretation, such as the description of novel immune cell types. Here, based on the example of T cellmonocyte complexes, we identify experimental and data analysis strategies to help distinguishing between singlets and cell-cell complexes in non-imaging flow cytometry and single-cell sorting. We found robust molecular signatures in both T cell-monocyte and T cell-B cell complexes that can distinguish them from singlets at both protein and mRNA levels. Imaging flow cytometry with appropriate gating strategy (matching the one used in cell sorting) and direct microscopy imaging after cell sorting were the two methods of choice to detect the presence of cell-cell complexes in suspicious dualexpressing cells. We finally applied this knowledge to highlight the likely presence of T cell-B cell complexes in a recently published dataset describing a novel cell population with mixed T cell and B cell lineage properties.

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Tuberculosis (TB) is a major infectious disease worldwide, and is associated with several challenges for control and eradication. First, more accurate diagnostic tools that better represent the spectrum of infection states are required; in particular, identify the latent TB infected individuals with high risk of developing active TB. Second, we need to better understand, from a mechanistic point of view, why the immune system is unsuccessful in some cases for control and elimination of the pathogen. Host transcriptomics is a powerful approach to identify both diagnostic and mechanistic immune signatures of diseases. We have recently reported that optimal study design for these two purposes should be guided by different sets of criteria. Here, based on already published transcriptomics signatures of tuberculosis, we further develop these guidelines and identify additional factors to consider for obtaining diagnostic vs. mechanistic signatures in terms of cohorts, samples, data generation and analysis. Diagnostic studies should aim to identify small disease signatures with high discriminatory power across all affected populations, and against similar pathologies to TB. Specific focus should be made on improving the diagnosis of infected individuals at risk of developing active disease. Conversely, mechanistic studies should focus on tissues biopsies, immune relevant cell subsets, state of the art transcriptomic techniques and bioinformatics tools to understand the biological meaning of identified gene signatures that could facilitate therapeutic interventions. Finally, investigators should ensure their data are made publicly available along with complete annotations to facilitate metadata and cross-study analyses.

Upon Ag encounter, T cells can rapidly divide and form an effector population, which plays an important role in fighting acute infections. In humans, little is known about the molecular markers that distinguish such effector cells from other T cell populations. To address this, we investigated the molecular profile of T cells present in individuals with active tuberculosis (ATB), where we expect Ag encounter and expansion of effector cells to occur at higher frequency in contrast to Mycobacterium tuberculosis–sensitized healthy IGRA+ individuals. We found that the frequency of HLA-DR+ cells was increased in circulating CD4 T cells of ATB patients, and was dominantly expressed in M. tuberculosis Ag–specific CD4 T cells. We tested and confirmed that HLA-DR is a marker of recently divided CD4 T cells upon M. tuberculosis Ag exposure using an in vitro model examining the response of resting memory T cells from healthy IGRA+ to Ags. Thus, HLA-DR marks a CD4 T cell population that can be directly detected ex vivo in human peripheral blood, whose frequency is increased during ATB disease and contains recently divided Ag-specific effector T cells. These findings will facilitate the monitoring and study of disease-specific effector T cell responses in the context of ATB and other infections.