Single-cell transcriptomics has proven to be an extremely powerful tool to investigate host-pathogen interactions heterogeneity. Most popular single-cell genomics techniques require the cells to be dissociated, leading to the loss of the spatial context, which could give more insight into the identity or function of involved cells. Yet, the emergence of spatial transcriptomics methods aims to preserve cell-to-cell interactions and keep the tissue intact. However, current commercially available methods (e.g., Visium) lack the ability to resolve infected cells in whole tissues and therefore are not amenable to detect rare events like in chronic infections where only a few infection foci are present in a whole organ.

Here, we aim to develop thick-section spatial transcriptomics amenable to capture host and pathogen interactions to reveal infection foci in 3D. We have implemented expansion microscopy and single-molecule FISH to identify rare events during infection.

We have achieved successful expansion of tissue sections from the lung, liver, and intestine, achieving a thickness of up to 300 µm. The expansion process is isotropic, with samples expanding by a factor of 3 in the x/y direction and 1.5 in the z direction. Notably, even after undergoing rigorous treatment, the samples retain their original structure post-embedding and expansion.

Leveraging existing single-cell data from mice, we have discerned marker genes associated with specific cell types. Subsequently, we have designed probes to precisely target these marker genes within our samples. Our efforts have yielded promising results, enabling us to identify distinct cell types such as alveolar macrophages and ciliated cells.

Looking ahead, our focus will shift towards extending this methodology to investigate infected tissues. Our initial experiments will concentrate on exploring the dynamics of Staphylococcus aureus infection in the lung.

The identification of single cells inside the tissue can be achieved by training neural networks like Cellpose or Stardist on organ-specific data.