# A comparison of super‐resolution microscopy techniques for imaging tightly packed microcolonies of an obligate intracellular bacterium

## TL;DR

This study compares various super-resolution microscopy techniques to better image tightly packed intracellular bacteria, finding that 3D-STED and 3D-SIM offer the best resolution for studying their structure.

## Contribution

The paper introduces a combined 3D-STED and deep learning-based analysis pipeline for resolving and characterizing intracellular bacterial morphology.

## Key findings

- 3D-SIM with short-wavelength dyes provides the best lateral resolution for imaging clustered bacteria.
- 3D-STED excels in axial resolution for resolving intracellular bacterial structures.
- Bacterial shape, but not size, varies depending on the mammalian cell line used for growth.

## Abstract

Conventional optical microscopy imaging of obligate intracellular bacteria is hampered by the small size of bacterial cells, tight clustering exhibited by some bacterial species and challenges relating to labelling such as background from host cells, a lack of validated reagents, and a lack of tools for genetic manipulation. In this study, we imaged intracellular bacteria from the species Orientia tsutsugamushi (Ot) using five different fluorescence microscopy techniques: standard confocal, Airyscan confocal, instant Structured Illumination Microscopy (iSIM), three‐dimensional Structured Illumination Microscopy (3D‐SIM) and Stimulated Emission Depletion Microscopy (STED). We compared the ability of each to resolve bacterial cells in intracellular clumps in the lateral (xy) axis, using full width half‐maximum (FWHM) measurements of a labelled outer membrane protein (ScaA) and the ability to detect small, outer membrane vesicles external to the cells. Comparing the techniques readily available to us (above), 3D‐SIM microscopy, in combination with the shortest‐wavelength dyes, was found overall to give the best lateral resolution. We next compared the ability of each technique to sufficiently resolve bacteria in the axial (z) direction and found 3D‐STED to be the most successful method for this. We then combined this 3D‐STED approach with a custom 3D cell segmentation and analysis pipeline using the open‐source, deep learning software, Cellpose to segment the cells and subsequently the commercial software Imaris to analyse their 3D shape and size. Using this combination, we demonstrated differences in bacterial shape, but not their size, when grown in different mammalian cell lines. Overall, we compare the advantages and disadvantages of different super‐resolution microscopy techniques for imaging this cytoplasmic obligate intracellular bacterium based on the specific research question being addressed.