The mRNA vaccine platform has offered the greatest potential in fighting the COVID-19 pandemic owing to rapid development, effectiveness, and scalability to meet the global demand. There are many other mRNA vaccines currently being developed against different emerging viral diseases. As with the current COVID-19 vaccines, these mRNA-based vaccine candidates are being developed for parenteral administration via injections. However, most of the emerging viruses colonize the mucosal surfaces prior to systemic infection making it very crucial to target mucosal immunity. Although parenterally administered vaccines would induce a robust systemic immunity, they often provoke a weak mucosal immunity which may not be effective in preventing mucosal infection. In contrast, mucosal administration potentially offers the dual benefit of inducing potent mucosal and systemic immunity which would be more effective in offering protection against mucosal viral infection. There are however many challenges posed by the mucosal environment which impede successful mucosal vaccination. The development of an effective delivery system remains a major challenge to the successful exploitation of mucosal mRNA vaccination. Nonetheless, a number of delivery vehicles have been experimentally harnessed with different degrees of success in the mucosal delivery of mRNA vaccines. In this review, we provide a comprehensive overview of mRNA vaccines and summarise their application in the fight against emerging viral diseases with particular emphasis on COVID-19 mRNA platforms. Furthermore, we discuss the prospects and challenges of mucosal administration of mRNA-based vaccines, and we explore the existing experimental studies on mucosal mRNA vaccine delivery.

In recent years, there has been ample evidence illustrating the effect of microbiota on gut immunity, homeostasis, and disease. Most of these studies have engaged more efforts in understanding the role of the bacteriome in gut mucosal immunity and disease. However, studies on the virome and its influence on gut mucosal immunity and pathology are still at infancy owing to limited metagenomic tools. Nonetheless, the existing studies on the virome have largely been focused on the bacteriophages as these represent the main component of the virome with little information on endogenous retroviruses (ERVs) and eukaryotic viruses. In this review, we describe the gut virome, and its role in gut mucosal response and disease progression. We also explore the crosstalk between the virome and other microorganisms in the gut mucosa and elaborate on how these interactions shape the gut mucosal immunity going from bacteriophages through ERVs to eukaryotic viruses. Finally, we elucidate the potential contribution of this crosstalk in the pathogenesis of inflammatory bowel diseases and colon cancer.

The oral route is effective and convenient for vaccine administration to stimulate a protective immune response. GALT plays a crucial role in mucosal immune responses, with Peyer’s patches (PPs) serving as the primary site of induction. A comprehensive understanding of the structures and functions of these structures is crucial for enhancing vaccination strategies and comprehending disease mechanisms; nonetheless, our current knowledge of these structures in dogs remains incomplete. We performed immunofluorescence and flow cytometry studies on canine PPs to identify cell populations and structures. We also performed single-cell RNA sequencing (scRNA-seq) to investigate the immune cell subpopulations present in PPs at steady state in dogs. We generated and validated an Ab specifically targeting canine M cells, which will be a valuable tool for elucidating Ag trafficking into the GALT of dogs. Our findings will pave the way for future studies of canine mucosal immune responses to oral vaccination and enteropathies. Moreover, they add to the growing body of knowledge in canine immunology, further expanding our understanding of the complex immune system of dogs.

The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell-cell interaction (PCI) network from ‘biological’ doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.

The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell–cell interaction (PCI) network from ‘biological’ doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.

Cells in tissues interact by direct physical contact or over short and long distances via secreted mediators. Cell-cell communication inference has now become routine in downstream scRNAseq analysis but this mostly fails to capture physical cell-cell interactions due to tissue dissociation. Multiplets (mostly doublets) in scRNAseq may represent undissociated physically attached cells that become sequenced together. Hence, identifying multiplets may serve as a good starting point to harness scRNAseq data for physical cell-cell interaction inference. In this study, we develop a computational method which utilizes univariate linear models (ULM) to identify multiplets in scRNAseq datasets, predict their cellular compositions, and infer physical cell-cell interaction networks. Indeed, our method showed good sensitivity (∼56%) with excellent precision (∼99%) in predicting FACS sorted doublet cell pairs with known constituents (ground truth), recording comparable or superior performance over two other existing methods. Also, applying it to scRNAseq data of partially dissociated tissues containing real multiplets unraveled physical networks which recapitulated the microanatomical structures of the tested tissues. This further underscores the accuracy in our predictions to capture biologically meaningful interactions. Finally, we tested our method on classical scRNAseq datasets and obtained biologically reasonable results. For example, when tested on classical cancer scRNAseq datasets, we recovered important interactions which followed biologically plausible cell interactions, validated by cell-cell colocalization in matched spatial transcriptomics datasets. This reassured the accuracy of our method in depicting physical interactions only between cells that were truly in close proximity in tissues.