SSNOMBACTER: A collection of scattering-type scanning near-field optical microscopy and atomic force microscopy images of bacterial cells

Abstract

Background In recent years, a variety of imaging techniques operating at nanoscale resolution have been reported. These techniques have the potential to enrich our understanding of bacterial species relevant to human health, such as antibiotic-resistant pathogens. However, owing to the novelty of these techniques, their use is still confined to addressing very particular applications, and their availability is limited owing to associated costs and required expertise. Among these, scattering-type scanning near field optical microscopy (s-SNOM) has been demonstrated as a powerful tool for exploring important optical properties at nanoscale resolution, depending only on the size of a sharp tip. Despite its huge potential to resolve aspects that cannot be tackled otherwise, the penetration of s-SNOM into the life sciences is still proceeding at a slow pace for the aforementioned reasons. Results In this work we introduce SSNOMBACTER, a set of s-SNOM images collected on 15 bacterial species. These come accompanied by registered Atomic Force Microscopy images, which are useful for placing nanoscale optical information in a relevant topographic context. Conclusions The proposed dataset aims to augment the popularity of s-SNOM and for accelerating its penetration in life sciences. Furthermore, we consider this dataset to be useful for the development and benchmarking of image analysis tools dedicated to s-SNOM imaging, which are scarce, despite the high need. In this latter context we discuss a series of image processing and analysis applications where SSNOMBACTER could be of help.

Infrared nanoscopy and tomography of intracellular structures

“…The potential spectroscopic information that can be gathered with this technique may be remarkably complex with a multitude of organic chemical groups contributing to the absorption in the IR region. While the focus of the presented work lies on the analysis of raw sSNOM phase and amplitude, additional information can be retrieved from the material's optical constants 15,50 . We may infer that applying machine learning to multichannel datasets of IR and topographical images will be crucial to extract more detailed information from cellular cross-sections 51 .…”

“…In addition to measurements on thin and well defined surfaces, sSNOM and nanoFTIR have been used for the imaging and spectroscopy of whole cells 14 . Recently, a comprehensive database with sSNOM and AFM images of various bacterial species showed the versatility and applicability of near-field imaging in life sciences 15 . The information gathered via traditional sSNOM and nanoFTIR is however limited to around 100 nm below the sample surface 16 and as recently shown, around 200 nm for materials with highly distinguishable contrast from the surrounding 17 .…”

Review: Advanced Atomic Force Microscopy Modes for Biomedical Research

“…As a new powerful technique with hype-pixel spectrum at each location, data-driven algorithms could be a powerful technique to uncover hidden information from a large amount of data. Researchers have started to create datasets to enable the training of algorithms such as the dataset for bacterial cells [ 100 ]. In addition to infrared s-SNOM, AFM-IR technique has also been used in biomedical applications.…”

Super-resolution mid-infrared spectro-microscopy of biological applications through tapping mode and peak force tapping mode atomic force microscope

“…It was found that gene deletion strains that lack the galactofuranose (Galf) exhibit less β-glucan but more αglucan on the ultrastructure of cell walls, which underscored the importance of Galf in maintaining the integrity and biofunction of fungi cell walls. For bacteria imaging, sample preparation protocols of general bacterial samples were reported, and a total of 15 bacterial species had been imaged by s-SNOM [84].…”