Integrative single cell and spatial transcriptomic analysis reveal reciprocal microglia-plasma cell crosstalk in the mouse brain during chronic Trypanosoma brucei infection
Posted on: 14 April 2022 , updated on: 17 April 2023
Preprint posted on 28 March 2022
Location matters! Navigating Trypanosoma brucei infection in the mouse brain
Selected by Jessica ChevallierCategories: bioinformatics, genomics, immunology
Background
Single-cell RNA-seq (scRNA-seq) allows researchers to sequence entire transcriptomes at the single-cell level. The method has proven useful in discovering rare cell types and tracking distinct cell lineages in development. Nevertheless, a major drawback of scRNA-seq is the use of isolated cells, which leads to a loss in spatial information. Recently, scRNA-seq methods have been used in conjunction with spatial transcriptomic methods to map spatial cell types within tissue sections at single-cell resolution.
In this study, Juan F. Quintana et al. integrated scRNA-seq and spatial transcriptomics to study the transcriptional responses of the mouse brain in response to chronic Trypanosoma brucei infection at 25-days post infection (dpi) and 45dpi. T. brucei is a protozoan parasite responsible for Human African trypanosomiasis (HAT), otherwise known as sleeping sickness. If left untreated, T. brucei will colonize the central nervous system (CNS) of the host resulting in neuroinflammation and subsequent death. Previous studies have identified an infiltration of adaptive immune cells upon infection, however, immune cell transcriptional responses to T. brucei infection remained largely unexplored.
Key findings
Exploitation of circumventricular organs (CVOs) during infection
Eight cell types were identified in the single-cell transcriptomic study across all biological replicates, including microglial, oligodendrocytes, B cells, astrocytes, T cells, endothelial cells, pericytes and ependymocytes. Cells within the microglia, B cell and T cell clusters were found to be differentially distributed in the disease versus wildtype state. The authors also found that the global expression level of inflammatory mediators, indicative of a post-infection response, was mainly observed in the microglia clusters at 25dpi and 45dpi.
Results obtained from the spatial transcriptomic study showed that the majority of T. brucei genes are highly expressed in specific locations of the mouse forebrain (e.g., thalamus, hippocampus, cerebral cortex, and CVOs). In fact, parasite location at 45dpi partially coincided with an increased expression in inflammatory mediators in spatial regions associated to CVOs. To further understand the importance of CVOs during infection, the authors performed a gene ontology and pathway analysis. The results demonstrated that parasites located at or near CVOs are metabolically active and expressed genes associated with parasite differentiation (i.e., slender and stumpy forms of the parasite).
Infection-associated microglia occupy spatial niches
The next step for the authors was to determine if there is a correlation between the microglia clusters, displaying an increased inflammatory response, and the distribution of parasites in the forebrain. Subclustering the microglia cluster and performing differential gene expression revealed six distinct subclusters. Three clusters, identified as Cx3cr1+ microglia 1 and 2, and Cd14+ monocytes by the authors, accounted for roughly 75% of the microglia detected in infected mice. The genes highly expressed in these three clusters were found to be spatially expressed around the hippocampus and CVOs at 25dpi and 45dpi compared to the uninfected controls. Taken together, the authors demonstrated that three microglia subclusters associated with infection are detected nears CVOs which coincides with the spatial distribution of T. brucei.
Immune cell heterogeneity during infection
Subclustering the previously identified T cell cluster resulted in the identification of three distinct T cell subclusters: Cd8+ 1 T cells, Cd8+ 2 T cells and Cd4+ T cells. Cluster 2 (Cd8+ 2 T cells) was only identified in infected samples which the authors suggest may indicate a disease-associated T cell subset in the brain. Interestingly, cells in cluster 3 resemble follicular-like regulatory Cd4+ T cells which had yet to be recorded in the brain during infections.
The authors also subclustered the previously identified B cell and found a plasma-cell specific cluster they called Cd138+ plasma cells. These cells were enriched during infection. In fact, the Cd138+ plasma cells were found to be localized around CVOs in uninfected mice, but their distribution spread to the lateral ventricle at 25dpi and to the cingulate cortex at 45 dpi suggesting an expansion of the original plasma cell population. Additional experiments demonstrated that Cx3cr1+ microglia are engaging in crosstalk with the Cd138+ plasma cells via the IL-10 receptor and the B cell activating factor (BAFF). The functional interaction between both cells promotes plasma cell survival during T. brucei infection.
Why this work is important
Cells do not act alone; they are directly influenced by their surrounding environment. scRNA-seq and spatial transcriptomics are complementary technologies that can be used to further understand how cells within a tissue interact at a spatial-temporal scale in health and disease. This preprint gives us valuable insight into how spatial cellular heterogeneity plays a key role in coordinating the colonization of the CNS by T. brucei, a parasite responsible for sleeping sickness. The work done by Juan F. Quintana and his team clearly demonstrate the need to generate a “cell atlas” cataloguing both cell type and location to elucidate their role in disease.
Questions for the authors
- You mentioned that your scRNA-seq analysis was focused on the hypothalamus, due to its role in controlling circadian behaviour. Did you check for a change in gene expression in circadian clock genes at 25dpi and 45dpi?
- Can your study be used to understand how T. brucei may manipulate the host defense mechanisms to ensure survival?
- What similarities and differences do you expect to arise if you repeat the experiments using B cell deficient mice?
doi: https://doi.org/10.1242/prelights.31795
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