Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues. 14/18

BackgroundADAR (adenosine deaminase acting on RNA) proteins convert adenosine into inosine in double-stranded RNAs and have been shown to increase gene product diversity in a number of bilaterians, particularly mammals and flies. This enzyme family appears to have evolved from an ADAT (adenosine deaminase acting on tRNA) ancestor, via the addition of a double-stranded RNA binding domain. The modern vertebrate ADAR family is comprised of ADAD, ADAR2 and ADAR1, each of which has a conserved domain architecture. To reconstruct the origin of this protein family, we identified and categorised ADAR family members encoded in the genomes and/or transcriptomes of early-branching metazoan and closely related non-metazoan taxa, including thirteen sponge and ten ctenophore species.ResultsWe demonstrate that the ADAR protein family is a metazoan innovation, with the three ADAR subtypes being present in representatives of the earliest phyletic lineages of animals – sponges and ctenophores – but not in other closely related choanoflagellate and filasterean holozoans. ADAR1 is missing from all ctenophore genomes and transcriptomes surveyed. Depending on the relationship of sponges and ctenophores to the rest of the Metazoa, this is consistent with either ADAR1 being lost in ctenophores, as it has been in multiple metazoan lineages, or being an innovation that evolved after ctenophores diverged from the rest of the animal kingdom. The presence of Z-DNA binding domains in some sponge ADARs indicates an ancestral ADAR included this domain and it has been lost in multiple animal lineages.ConclusionsThe ADAR family appears to be a metazoan innovation, with all family members in place in the earliest phyletic branches of the crown Metazoa. The presence of ADARs in sponges and ctenophores is consistent with A-to-I editing being a post-transcriptional regulatory mechanism that was used by the last common ancestor to all living animals and subsequently has been preserved in most modern lineages.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0279-3) contains supplementary material, which is available to authorized users.

Although discriminating self from nonself is a cardinal animal trait, metazoan allorecognition genes do not appear to be homologous. Here, we characterize the Aggregation Factor (AF) gene family, which encodes putative allorecognition factors in the demosponge Amphimedon queenslandica, and trace its evolution across 24 sponge (Porifera) species. The AF locus in Amphimedon is comprised of a cluster of five similar genes that encode Calx-beta and Von Willebrand domains and a newly defined Wreath domain, and are highly polymorphic. Further AF variance appears to be generated through individualistic patterns of RNA editing. The AF gene family varies between poriferans, with protein sequences and domains diagnostic of the AF family being present in Amphimedon and other demosponges, but absent from other sponge classes. Within the demosponges, AFs vary widely with no two species having the same AF repertoire or domain organization. The evolution of AFs suggests that their diversification occurs via high allelism, and the continual and rapid gain, loss and shuffling of domains over evolutionary time. Given the marked differences in metazoan allorecognition genes, we propose the rapid evolution of AFs in sponges provides a model for understanding the extensive diversification of self–nonself recognition systems in the animal kingdom.

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PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.