Plasmodium infection induces phenotypic, clonal, and spatial diversity among differentiating CD4+ T cells.

Naive CD4+ T cells must differentiate in order to orchestrate immunity to Plasmodium, yet understanding of their emerging phenotypes, clonality, spatial distributions, and cellular interactions remains incomplete. Here, we observe that splenic polyclonal CD4+ T cells differentiate toward T helper 1 (Th1) and T follicular helper (Tfh)-like states and exhibit rarer phenotypes not elicited among T cell receptor (TCR) transgenic counterparts. TCR clones present at higher frequencies exhibit Th1 skewing, suggesting that variation in major histocompatibility complex class II (MHC-II) interaction influences proliferation and Th1 differentiation. To characterize CD4+ T cell interactions, we map splenic microarchitecture, cellular locations, and molecular interactions using spatial transcriptomics at near single-cell resolution. Tfh-like cells co-locate with stromal cells in B cell follicles, while Th1 cells in red pulp co-locate with activated monocytes expressing multiple chemokines and MHC-II. Spatial mapping of individual transcriptomes suggests that proximity to chemokine-expressing monocytes correlates with stronger effector phenotypes in Th1 cells. Finally, CRISPR-Cas9 gene disruption reveals a role for CCR5 in promoting clonal expansion and Th1 differentiation. A database of cellular locations and interactions is presented: https://haquelab.mdhs.unimelb.edu.au/spatial_gui/.

CD4+ T cells display a spectrum of recall dynamics during re-infection with malaria parasites.

Children in malaria-endemic regions can experience repeated Plasmodium infections over short periods of time. Effects of re-infection on multiple co-existing CD4+ T cell subsets remain unresolved. Here, we examine antigen-experienced CD4+ T cells during re-infection in mice, using scRNA-seq/TCR-seq and spatial transcriptomics. TCR transgenic TEM cells initiate rapid Th1/Tr1 recall responses prior to proliferating, while GC Tfh counterparts are refractory, with TCM/Tfh-like cells exhibiting modest non-proliferative responses. Th1-recall is a partial facsimile of primary Th1-responses, with no upregulated effector-associated genes being unique to recall. Polyclonal, TCR-diverse, CD4+ T cells exhibit similar recall dynamics, with individual clones giving rise to multiple effectors including highly proliferative Th1/Tr1 cells, as well as GC Tfh and Tfh-like cells lacking proliferative capacity. Thus, we show substantial diversity in recall responses mounted by multiple co-existing CD4+ T cell subsets in the spleen, and present graphical user interfaces for studying gene expression dynamics and clonal relationships during re-infection.

An introduction to spatial transcriptomics for biomedical research.

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.