Laia Pasquina-Lemoche, University of Sheffield

Laia Pasquina-Lemoche, University of Sheffield

High resolution AFM and STORMForce to uncover cell division in Streptococcus pneumoniae

Streptococcus pneumoniae (S. pneumoniae) is the leading cause of death by pneumonia, with over 1 million cases annually. Increasing resistance to antimicrobials means a development of new solutions to combat this bacterium are becoming increasingly urgent but this first requires a robust understanding of key antimicrobial targets. One of those targets is the peptidoglycan (PG), the main component of the bacterial cell wall. Our lab focuses on deepening our understanding of PG from S. pneumoniae.  Stochastic Optical Reconstructive Microscopy (STORM) has been used in S. pneumoniae providing key understanding of cell division dynamics. This technique shows a significantly improved resolution beyond the diffraction limit of light. Trouve et al. (2021) have demonstrated the power of STORM to not only localise PG synthesis during the cell cycle providing temporal information and therefore classify stages of division during the cell cycle. However, despite the dynamics of this division process being well accepted in the field, the PG architecture of S. pneumoniae during that process is still unknown.  We used high resolution Atomic Force Microscopy (AFM) to interrogate other Gram-positive species in the past and we have shown that the cell wall has four significantly different architectures depending on the location of the PG in relation to cell division (Pasquina-Lemonche et al., 2020). Resulting in a highly porous hydrogel composed of fibres and pores arranged in specific patterns depending on the cell division cycle. However, S. pneumoniae is an ovococci-shaped bacteria that differs from other Gram-positives as septation and elongation occur simulatenously and concomitantly during the cell cycle. Due to the non-obvious spatial separation between these cell cycle stages, AFM alone cannot distinguish which part of the cell belongs to what age, making it almost impossible to build a cell wall structural model based on AFM alone.  Here we show that direct correlation between STORM and AFM (STORMForce) is capable of providing solutions to those limitations that make the two isolated techniques insufficient to answer this research question. The temporal information from STORM combined with the high-resolution from the AFM, allows us to hijack the system and observe how the PG architecture changes through the cell cycle with 1 nm resolution for this elusive pathogen