Tamim Abdelaal

Meningitis caused by Streptococcus suis serotype 2 (SS2) in humans and pigs is an acute nervous disorder associated with serious sequelae. Bacterial meningitis is tightly associated with immune cell responses and the local immune microenvironment. However, the dynamic changes of the immune system during the disease progression in the brain remains unclear. Here, single-cell mass cytometry analyses are used to comprehensively profile the composition and phenotypes of female mouse brain immune cells at different stages of SS2 meningitis. Ten major immune cell lineages are identified among which T cells and dendritic cells significantly increased during meningitis, with B cells increasing in the late stage. Specifically, SS2PD-L1 neutrophils with strong phagocytosis, bactericidal and apoptotic effects accumulate in the acute phase of SS2 infection. Microglia sequentially display the features of homeostasis, proliferation, and activation (enhanced MHCII and TLR2 signals and TNF-α secretion) during the process of meningitis. Both border-associated and monocyte-derived macrophages contribute to the process of SS2-induced meningitis, exhibiting upregulation of CD38 and MHCII. Interestingly, CD11cCD8T cells are the main contributor of IFN-γ and specifically appeared during SS2 infection. In addition, the appearance of other lymphocytes such as CCR6 B cells, CX3CR1 NK and MHCII ILC3 are related to the progression of meningitis. Moreover, correlation analysis between the composition of immune cell clusters and the SS2 infection process yield a dynamic immune landscape in which key immune clusters, including some previously unidentified, mark different stages of infection. Together, these data reveal the unique infection-stage immune microenvironment during the progression of meningitis caused by SS2 and provide resources for the analysis of immunological pathogenesis, potential diagnostic markers and therapeutic targets for bacterial meningitis.

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.

RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.

Chronic inflammation plays an important role in alveolar tissue damage in emphysema, but the underlying immune alterations and cellular interactions are incompletely understood. To explore disease-specific pulmonary immune cell alterations and cellular interactions in emphysema. We used single-cell mass cytometry (CyTOF) to compare the immune compartment in alveolar tissue from 15 patients with severe emphysema and 5 control subjects. Imaging mass cytometry (IMC) was applied to identify altered cell-cell interactions in alveolar tissue from patients with emphysema ( = 12) compared with control subjects ( = 8). We observed higher percentages of central memory CD4 T cells in combination with lower proportions of effector memory CD4 T cells in emphysema. In addition, proportions of cytotoxic central memory CD8 T cells and CD127CD27CD69 T cells were higher in emphysema, the latter potentially reflecting an influx of circulating lymphocytes into the lungs. Central memory CD8 T cells, isolated from alveolar tissue from patients with emphysema, exhibited an IFN-γ response upon anti-CD3 and anti-CD28 activation. Proportions of CD1c dendritic cells, expressing migratory and costimulatory markers, were higher in emphysema. Importantly, IMC enabled us to visualize increased spatial colocalization of CD1c dendritic cells and CD8 T cells in emphysema . Using CyTOF, we characterized the alterations of the immune cell signature in alveolar tissue from patients with chronic obstructive pulmonary disease stage III or IV emphysema versus control lung tissue. These data contribute to a better understanding of the pathogenesis of emphysema and highlight the feasibility of interrogating the immune cell signature using CyTOF and IMC in human lung tissue. Clinical trial registered with www.clinicaltrials.gov (NCT04918706).