Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as ‘spatial transcriptomics’. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.

Mucosal-associated Invariant T (MAIT) cells are recognized for their antibacterial functions. The protective capacity of MAIT cells has been demonstrated in murine models of local infection, including in the lungs. Here we show that during systemic infection of mice with Francisella tularensis live vaccine strain results in evident MAIT cell expansion in the liver, lungs, kidney and spleen and peripheral blood. The responding MAIT cells manifest a polarised Th1-like MAIT-1 phenotype, including transcription factor and cytokine profile, and confer a critical role in controlling bacterial load. Post resolution of the primary infection, the expanded MAIT cells form stable memory-like MAIT-1 cell populations, suggesting a basis for vaccination. Indeed, a systemic vaccination with synthetic antigen 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil in combination with CpG adjuvant similarly boosts MAIT cells, and results in enhanced protection against both systemic and local infections with different bacteria. Our study highlights the potential utility of targeting MAIT cells to combat a range of bacterial pathogens.

The dynamics of CD4 + T cell memory development remain to be examined at genome scale. In malaria-endemic regions, antimalarial chemoprevention protects long after its cessation and associates with effects on CD4 + T cells. We applied single-cell RNA sequencing and computational modelling to track memory development during Plasmodium infection and treatment. In the absence of central memory precursors, two trajectories developed as T helper 1 (T H 1) and follicular helper T (T FH) transcriptomes contracted and partially coalesced over three weeks. Progeny of single clones populated T H 1 and T FH trajectories, and fate-mapping suggested that there was minimal lineage plasticity. Relationships between T FH and central memory were revealed, with antimalarials modulating these responses and boosting T H 1 recall. Finally, single-cell epigenomics confirmed that heterogeneity among effectors was partially reset in memory. Thus, the effector-to-memory transition in CD4 + T cells is gradual during malaria and is modulated by antiparasitic drugs. Graphical user interfaces are presented for examining gene-expression dynamics and gene-gene correlations (http://haquelab.mdhs.unimelb.edu.au/cd4_memory/).