Single-cell RNA sequencing reveals peripheral blood leukocyte responses to spinal cord injury in mice with humanised immune systems.

Next-generation humanised mouse models and single-cell RNA sequencing (scRNAseq) approaches enable in-depth studies into human immune cell biology. Here we used NSG-SGM3 mice engrafted with human umbilical cord haematopoietic stem cells to investigate how human immune cells respond to and/or are changed by traumatic spinal cord injury (SCI). We hypothesised that the use of such mice could help advance our understanding of spinal cord injury-induced immune depression syndrome (SCI-IDS), and also how human leukocytes change as they migrate from the circulation into the lesion site. Our scRNAseq experiments, supplemented by flow cytometry, demonstrate the existence of up to 11 human immune cell (sub-) types and/or states across the blood and injured spinal cord (7 days post-SCI) of humanised NSG-SGM3 mice. Further comparisons of human immune cell transcriptomes between naïve, sham-operated and SCI mice identified a total of 579 differentially expressed genes, 190 of which were 'SCI-specific' (that is, genes regulated only in response to SCI but not sham surgery). Gene ontology analysis showed a prominent downregulation of immune cell function under SCI conditions, including for T cell receptor signalling and antigen presentation, confirming the presence of SCI-IDS and the transcriptional signature of human leukocytes in association with this phenomenon. We also highlight the activating influence of the local spinal cord lesion microenvironment by comparing the transcriptomes of circulating versus infiltrated human immune cells; those isolated from the lesion site were enriched for genes relating to both immune cell activity and function (e.g., oxidative phosphorylation, T cell proliferation and antigen presentation). We lastly applied an integrated bioinformatics approach to determine where immune responses in humanised NSG-SGM3 mice appear congruent to the native responses of human SCI patients, and where they diverge. Collectively, our study provides a valuable resource and methodological framework for the use of these mice in translational research.

Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues.

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments.

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules-peritubular capillaries by screening for co-expression of ligand-receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand-receptor interactions were identified within glomeruli and regions of proximal tubules-peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

Transcriptomic Profiling of the Allorecognition Response to Grafting in the Demosponge Amphimedon queenslandica.

Sponges, despite their simple body plan, discriminate between self and nonself with remarkable specificity. Sponge grafting experiments simulate the effects of natural self or nonself contact under laboratory conditions. Here we take a transcriptomic approach to investigate the temporal response to self and nonself grafts in the marine demosponge Amphimedon queenslandica. Auto- and allografts were established, observed and sampled over a period of three days, over which time the grafts either rejected or accepted, depending on the identity of the paired individuals, in a replicable and predictable manner. Fourteen transcriptomes were generated that spanned the auto- and allograft responses. Self grafts fuse completely in under three days, and the process appears to be controlled by relatively few genes. In contrast, nonself grafting results in a complete lack of fusion after three days, and appears to involve a broad downregulation of normal biological processes, rather than the mounting of an intense defensive response.

Origin and Evolution of the Sponge Aggregation Factor Gene Family.

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.

The origin of the ADAR gene family and animal RNA editing.

BACKGROUND: ADAR (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. RESULTS: We 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. CONCLUSIONS: The 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.